how to write analysis on an article

Organizing Your Social Sciences Research Assignments

• Annotated Bibliography
• Analyzing a Scholarly Journal Article
• Group Presentations
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Leading a Class Discussion
• Multiple Book Review Essay
• Reviewing Collected Works
• Writing a Case Analysis Paper
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Reflective Paper
• Writing a Research Proposal
• Generative AI and Writing
• Acknowledgments

Definition and Introduction

Journal article analysis assignments require you to summarize and critically assess the quality of an empirical research study published in a scholarly [a.k.a., academic, peer-reviewed] journal. The article may be assigned by the professor, chosen from course readings listed in the syllabus, or you must locate an article on your own, usually with the requirement that you search using a reputable library database, such as, JSTOR or ProQuest . The article chosen is expected to relate to the overall discipline of the course, specific course content, or key concepts discussed in class. In some cases, the purpose of the assignment is to analyze an article that is part of the literature review for a future research project.

Analysis of an article can be assigned to students individually or as part of a small group project. The final product is usually in the form of a short paper [typically 1- 6 double-spaced pages] that addresses key questions the professor uses to guide your analysis or that assesses specific parts of a scholarly research study [e.g., the research problem, methodology, discussion, conclusions or findings]. The analysis paper may be shared on a digital course management platform and/or presented to the class for the purpose of promoting a wider discussion about the topic of the study. Although assigned in any level of undergraduate and graduate coursework in the social and behavioral sciences, professors frequently include this assignment in upper division courses to help students learn how to effectively identify, read, and analyze empirical research within their major.

Franco, Josue. “Introducing the Analysis of Journal Articles.” Prepared for presentation at the American Political Science Association’s 2020 Teaching and Learning Conference, February 7-9, 2020, Albuquerque, New Mexico; Sego, Sandra A. and Anne E. Stuart. "Learning to Read Empirical Articles in General Psychology." Teaching of Psychology 43 (2016): 38-42; Kershaw, Trina C., Jordan P. Lippman, and Jennifer Fugate. "Practice Makes Proficient: Teaching Undergraduate Students to Understand Published Research." Instructional Science 46 (2018): 921-946; Woodward-Kron, Robyn. "Critical Analysis and the Journal Article Review Assignment." Prospect 18 (August 2003): 20-36; MacMillan, Margy and Allison MacKenzie. "Strategies for Integrating Information Literacy and Academic Literacy: Helping Undergraduate Students make the most of Scholarly Articles." Library Management 33 (2012): 525-535.

Benefits of Journal Article Analysis Assignments

Analyzing and synthesizing a scholarly journal article is intended to help students obtain the reading and critical thinking skills needed to develop and write their own research papers. This assignment also supports workplace skills where you could be asked to summarize a report or other type of document and report it, for example, during a staff meeting or for a presentation.

There are two broadly defined ways that analyzing a scholarly journal article supports student learning:

Improve Reading Skills

Conducting research requires an ability to review, evaluate, and synthesize prior research studies. Reading prior research requires an understanding of the academic writing style , the type of epistemological beliefs or practices underpinning the research design, and the specific vocabulary and technical terminology [i.e., jargon] used within a discipline. Reading scholarly articles is important because academic writing is unfamiliar to most students; they have had limited exposure to using peer-reviewed journal articles prior to entering college or students have yet to gain exposure to the specific academic writing style of their disciplinary major. Learning how to read scholarly articles also requires careful and deliberate concentration on how authors use specific language and phrasing to convey their research, the problem it addresses, its relationship to prior research, its significance, its limitations, and how authors connect methods of data gathering to the results so as to develop recommended solutions derived from the overall research process.

Improve Comprehension Skills

In addition to knowing how to read scholarly journals articles, students must learn how to effectively interpret what the scholar(s) are trying to convey. Academic writing can be dense, multi-layered, and non-linear in how information is presented. In addition, scholarly articles contain footnotes or endnotes, references to sources, multiple appendices, and, in some cases, non-textual elements [e.g., graphs, charts] that can break-up the reader’s experience with the narrative flow of the study. Analyzing articles helps students practice comprehending these elements of writing, critiquing the arguments being made, reflecting upon the significance of the research, and how it relates to building new knowledge and understanding or applying new approaches to practice. Comprehending scholarly writing also involves thinking critically about where you fit within the overall dialogue among scholars concerning the research problem, finding possible gaps in the research that require further analysis, or identifying where the author(s) has failed to examine fully any specific elements of the study.

In addition, journal article analysis assignments are used by professors to strengthen discipline-specific information literacy skills, either alone or in relation to other tasks, such as, giving a class presentation or participating in a group project. These benefits can include the ability to:

• Effectively paraphrase text, which leads to a more thorough understanding of the overall study;
• Identify and describe strengths and weaknesses of the study and their implications;
• Relate the article to other course readings and in relation to particular research concepts or ideas discussed during class;
• Think critically about the research and summarize complex ideas contained within;
• Plan, organize, and write an effective inquiry-based paper that investigates a research study, evaluates evidence, expounds on the author’s main ideas, and presents an argument concerning the significance and impact of the research in a clear and concise manner;
• Model the type of source summary and critique you should do for any college-level research paper; and,
• Increase interest and engagement with the research problem of the study as well as with the discipline.

Kershaw, Trina C., Jennifer Fugate, and Aminda J. O'Hare. "Teaching Undergraduates to Understand Published Research through Structured Practice in Identifying Key Research Concepts." Scholarship of Teaching and Learning in Psychology . Advance online publication, 2020; Franco, Josue. “Introducing the Analysis of Journal Articles.” Prepared for presentation at the American Political Science Association’s 2020 Teaching and Learning Conference, February 7-9, 2020, Albuquerque, New Mexico; Sego, Sandra A. and Anne E. Stuart. "Learning to Read Empirical Articles in General Psychology." Teaching of Psychology 43 (2016): 38-42; Woodward-Kron, Robyn. "Critical Analysis and the Journal Article Review Assignment." Prospect 18 (August 2003): 20-36; MacMillan, Margy and Allison MacKenzie. "Strategies for Integrating Information Literacy and Academic Literacy: Helping Undergraduate Students make the most of Scholarly Articles." Library Management 33 (2012): 525-535; Kershaw, Trina C., Jordan P. Lippman, and Jennifer Fugate. "Practice Makes Proficient: Teaching Undergraduate Students to Understand Published Research." Instructional Science 46 (2018): 921-946.

Structure and Organization

A journal article analysis paper should be written in paragraph format and include an instruction to the study, your analysis of the research, and a conclusion that provides an overall assessment of the author's work, along with an explanation of what you believe is the study's overall impact and significance. Unless the purpose of the assignment is to examine foundational studies published many years ago, you should select articles that have been published relatively recently [e.g., within the past few years].

Since the research has been completed, reference to the study in your paper should be written in the past tense, with your analysis stated in the present tense [e.g., “The author portrayed access to health care services in rural areas as primarily a problem of having reliable transportation. However, I believe the author is overgeneralizing this issue because...”].

Introduction Section

The first section of a journal analysis paper should describe the topic of the article and highlight the author’s main points. This includes describing the research problem and theoretical framework, the rationale for the research, the methods of data gathering and analysis, the key findings, and the author’s final conclusions and recommendations. The narrative should focus on the act of describing rather than analyzing. Think of the introduction as a more comprehensive and detailed descriptive abstract of the study.

Possible questions to help guide your writing of the introduction section may include:

• Who are the authors and what credentials do they hold that contributes to the validity of the study?
• What was the research problem being investigated?
• What type of research design was used to investigate the research problem?
• What theoretical idea(s) and/or research questions were used to address the problem?
• What was the source of the data or information used as evidence for analysis?
• What methods were applied to investigate this evidence?
• What were the author's overall conclusions and key findings?

Critical Analysis Section

The second section of a journal analysis paper should describe the strengths and weaknesses of the study and analyze its significance and impact. This section is where you shift the narrative from describing to analyzing. Think critically about the research in relation to other course readings, what has been discussed in class, or based on your own life experiences. If you are struggling to identify any weaknesses, explain why you believe this to be true. However, no study is perfect, regardless of how laudable its design may be. Given this, think about the repercussions of the choices made by the author(s) and how you might have conducted the study differently. Examples can include contemplating the choice of what sources were included or excluded in support of examining the research problem, the choice of the method used to analyze the data, or the choice to highlight specific recommended courses of action and/or implications for practice over others. Another strategy is to place yourself within the research study itself by thinking reflectively about what may be missing if you had been a participant in the study or if the recommended courses of action specifically targeted you or your community.

Possible questions to help guide your writing of the analysis section may include:

Introduction

• Did the author clearly state the problem being investigated?
• What was your reaction to and perspective on the research problem?
• Was the study’s objective clearly stated? Did the author clearly explain why the study was necessary?
• How well did the introduction frame the scope of the study?
• Did the introduction conclude with a clear purpose statement?

Literature Review

• Did the literature review lay a foundation for understanding the significance of the research problem?
• Did the literature review provide enough background information to understand the problem in relation to relevant contexts [e.g., historical, economic, social, cultural, etc.].
• Did literature review effectively place the study within the domain of prior research? Is anything missing?
• Was the literature review organized by conceptual categories or did the author simply list and describe sources?
• Did the author accurately explain how the data or information were collected?
• Was the data used sufficient in supporting the study of the research problem?
• Was there another methodological approach that could have been more illuminating?
• Give your overall evaluation of the methods used in this article. How much trust would you put in generating relevant findings?

Results and Discussion

• Were the results clearly presented?
• Did you feel that the results support the theoretical and interpretive claims of the author? Why?
• What did the author(s) do especially well in describing or analyzing their results?
• Was the author's evaluation of the findings clearly stated?
• How well did the discussion of the results relate to what is already known about the research problem?
• Was the discussion of the results free of repetition and redundancies?
• What interpretations did the authors make that you think are in incomplete, unwarranted, or overstated?
• Did the conclusion effectively capture the main points of study?
• Did the conclusion address the research questions posed? Do they seem reasonable?
• Were the author’s conclusions consistent with the evidence and arguments presented?
• Has the author explained how the research added new knowledge or understanding?

Overall Writing Style

• If the article included tables, figures, or other non-textual elements, did they contribute to understanding the study?
• Were ideas developed and related in a logical sequence?
• Were transitions between sections of the article smooth and easy to follow?

Overall Evaluation Section

The final section of a journal analysis paper should bring your thoughts together into a coherent assessment of the value of the research study . This section is where the narrative flow transitions from analyzing specific elements of the article to critically evaluating the overall study. Explain what you view as the significance of the research in relation to the overall course content and any relevant discussions that occurred during class. Think about how the article contributes to understanding the overall research problem, how it fits within existing literature on the topic, how it relates to the course, and what it means to you as a student researcher. In some cases, your professor will also ask you to describe your experiences writing the journal article analysis paper as part of a reflective learning exercise.

Possible questions to help guide your writing of the conclusion and evaluation section may include:

• Was the structure of the article clear and well organized?
• Was the topic of current or enduring interest to you?
• What were the main weaknesses of the article? [this does not refer to limitations stated by the author, but what you believe are potential flaws]
• Was any of the information in the article unclear or ambiguous?
• What did you learn from the research? If nothing stood out to you, explain why.
• Assess the originality of the research. Did you believe it contributed new understanding of the research problem?
• Were you persuaded by the author’s arguments?
• If the author made any final recommendations, will they be impactful if applied to practice?
• In what ways could future research build off of this study?
• What implications does the study have for daily life?
• Was the use of non-textual elements, footnotes or endnotes, and/or appendices helpful in understanding the research?
• What lingering questions do you have after analyzing the article?

NOTE: Avoid using quotes. One of the main purposes of writing an article analysis paper is to learn how to effectively paraphrase and use your own words to summarize a scholarly research study and to explain what the research means to you. Using and citing a direct quote from the article should only be done to help emphasize a key point or to underscore an important concept or idea.

Business: The Article Analysis . Fred Meijer Center for Writing, Grand Valley State University; Bachiochi, Peter et al. "Using Empirical Article Analysis to Assess Research Methods Courses." Teaching of Psychology 38 (2011): 5-9; Brosowsky, Nicholaus P. et al. “Teaching Undergraduate Students to Read Empirical Articles: An Evaluation and Revision of the QALMRI Method.” PsyArXi Preprints , 2020; Holster, Kristin. “Article Evaluation Assignment”. TRAILS: Teaching Resources and Innovations Library for Sociology . Washington DC: American Sociological Association, 2016; Kershaw, Trina C., Jennifer Fugate, and Aminda J. O'Hare. "Teaching Undergraduates to Understand Published Research through Structured Practice in Identifying Key Research Concepts." Scholarship of Teaching and Learning in Psychology . Advance online publication, 2020; Franco, Josue. “Introducing the Analysis of Journal Articles.” Prepared for presentation at the American Political Science Association’s 2020 Teaching and Learning Conference, February 7-9, 2020, Albuquerque, New Mexico; Reviewer's Guide . SAGE Reviewer Gateway, SAGE Journals; Sego, Sandra A. and Anne E. Stuart. "Learning to Read Empirical Articles in General Psychology." Teaching of Psychology 43 (2016): 38-42; Kershaw, Trina C., Jordan P. Lippman, and Jennifer Fugate. "Practice Makes Proficient: Teaching Undergraduate Students to Understand Published Research." Instructional Science 46 (2018): 921-946; Gyuris, Emma, and Laura Castell. "To Tell Them or Show Them? How to Improve Science Students’ Skills of Critical Reading." International Journal of Innovation in Science and Mathematics Education 21 (2013): 70-80; Woodward-Kron, Robyn. "Critical Analysis and the Journal Article Review Assignment." Prospect 18 (August 2003): 20-36; MacMillan, Margy and Allison MacKenzie. "Strategies for Integrating Information Literacy and Academic Literacy: Helping Undergraduate Students Make the Most of Scholarly Articles." Library Management 33 (2012): 525-535.

Writing Tip

Not All Scholarly Journal Articles Can Be Critically Analyzed

There are a variety of articles published in scholarly journals that do not fit within the guidelines of an article analysis assignment. This is because the work cannot be empirically examined or it does not generate new knowledge in a way which can be critically analyzed.

If you are required to locate a research study on your own, avoid selecting these types of journal articles:

• Theoretical essays which discuss concepts, assumptions, and propositions, but report no empirical research;
• Statistical or methodological papers that may analyze data, but the bulk of the work is devoted to refining a new measurement, statistical technique, or modeling procedure;
• Articles that review, analyze, critique, and synthesize prior research, but do not report any original research;
• Brief essays devoted to research methods and findings;
• Articles written by scholars in popular magazines or industry trade journals;
• Pre-print articles that have been posted online, but may undergo further editing and revision by the journal's editorial staff before final publication; and
• Academic commentary that discusses research trends or emerging concepts and ideas, but does not contain citations to sources.

Journal Analysis Assignment - Myers . Writing@CSU, Colorado State University; Franco, Josue. “Introducing the Analysis of Journal Articles.” Prepared for presentation at the American Political Science Association’s 2020 Teaching and Learning Conference, February 7-9, 2020, Albuquerque, New Mexico; Woodward-Kron, Robyn. "Critical Analysis and the Journal Article Review Assignment." Prospect 18 (August 2003): 20-36.

• << Previous: Annotated Bibliography
• Next: Giving an Oral Presentation >>
• Last Updated: Mar 6, 2024 1:00 PM
• URL: https://libguides.usc.edu/writingguide/assignments

How to Write an Article Analysis

What Are the Five Parts of an Argumentative Essay?

As you write an article analysis, focus on writing a summary of the main points followed by an analytical critique of the author’s purpose.

Knowing how to write an article analysis paper involves formatting, critical thinking of the literature, a purpose of the article and evaluation of the author’s point of view. In an article analysis critique, you integrate your perspective of the author about a specific topic into a mix of reasoning and arguments. So, you develop an argumentative approach to the point of view of the author. However, a careful distinction occurs between summary and analysis.

When presenting your findings of the article analysis, you might want to summarize the main points, which allows you to formulate a thesis statement. Then, inform the readers about the analytical aspects the author presents in his arguments. Most likely, developing ideas on how to write an article analysis entails a meticulous approach to the critical thinking of the author.

Writing Steps for an Article Analysis

As with any formal paper, you want to begin by quickly reading the article to get the main points. Once you generate a general idea of the point of view of the author, start analyzing the main ideas of each paragraph. An ideal way to take notes based on the reading is to jot them down in the margin of the article. If that's not possible, include notes on your computer or a separate piece of paper. Interact with the text you're reading.

Becoming an active reader helps you decide the relevant information the author intends to communicate. At this point, you might want to include a summary of the main ideas. After you finish writing down the main points, read them to yourself and decide on a concise thesis statement. To do so, begin with the author’s name followed by the title of the article. Next, complete the sentence with your analytical perspective.

Ideally, you want to use outlines, notes and concept mapping to draft your copy. As you progress through the body of the critical part of the paper, include relevant information such as literature references and the author’s purpose for the article. Formal documents, such as an article analysis, also use in-text citation and proofreading. Any academic paper includes a grammar, spelling and mechanics proofreading. Make sure you double-check your paper before submission.

When you write the summary of the article, focus on the purpose of the paper and develop ideas that inform the reader in an unbiased manner. One of the most crucial parts of an analysis essay is the citation of the author and the title of the article. First, introduce the author by first and last name followed by the title of the article. Add variety to your sentence structure by using different formats. For example, you can use “Title,” author’s name, then a brief explanation of the purpose of the piece. Also, many sentences might begin with the author, “Title,” then followed by a description of the main points. By implementing active, explicit verbs into your sentences, you'll show a clear understanding of the material.

Much like any formal paper, consider the most substantial points as your main ideas followed by evidence and facts from the author’s persuasive text. Remember to use transition words to guide your readers in the writing. Those transition phrases or words encourage readers to understand your perspective of the author’s purpose in the article. More importantly, as you write the body of the analysis essay, use the author’s name and article title at the beginning of a paragraph.

When you write your evidence-based arguments, keep the author’s last name throughout the paper. Besides writing your critique of the author’s purpose, remember the audience. The readers relate to your perspective based on what you write. So, use facts and evidence when making inferences about the author’s point of view.

Description of an Article Analysis Essay

When you analyze an article based on the argumentative evidence, generate ideas that support or not the author’s point of view. Although the author’s purpose to communicate the intentions of the article may be clear, you need to evaluate the reasons for writing the piece. Since the basis of your analysis consists of argumentative evidence, elaborate a concise and clear thesis. However, don't rely on the thesis to stay the same as you research the article.

At many times, you'll find that you'll change your argument when you see new facts. In this way, you might want to use text, reader, author, context and exigence approaches. You don't need elaborate ideas. Just use the author’s text so that the reader understands the point of views. However, evaluate the strong tone of the author and the validity of the claims in the article. So, use the context of the article.

Then, ask yourself if the author explains the purpose of his or her persuasive reasons. As you discern the facts and evidence of the article, analyze the point of views carefully. Look for assumptions without basis and biased ideas that aren't valid. An analysis example paragraph easily includes your perspective of the author’s purpose and whether you agree or not. Don't be surprised if your critique changes as you research other authors about the article.

Consequently, your response might end up agreeing, disagreeing or being somewhat in between despite your efforts of finding supporting evidence. Regardless of the consequences of your research of the literature and the perspective of the author’s point of view, maintain a definite purpose in writing. Don't fluctuate from agreement and disagreement. Focusing your analysis on presenting the points of view of the author so readers understand it and disseminating that critique is the basis of your paper.

When reading the text carefully, analyze the main points and explain the reasons of the author. Also, as you describe the document, offer evidence and facts to eliminate any biases. In an argumentative analysis, the focus of the writer can quickly shift. Avoid ineffective ways of approaching the author’s point of view that make the writing vague and lack supporting evidence. A clear way to stay away from biases is to use quotes from the author. However, using excessive amounts of quotes is counterproductive. Use author quotes sparingly.

As you develop your own ideas about the author’s viewpoints, use deductive reasoning to analyze the various aspects of the article. Often, you'll find the historical background influenced the author or persuaded the author to challenge the ideals of the time. Distinguishing between writing a summary and an analysis paper is crucial to your essay. You might find that using a review at the beginning of your article indicates a clear perspective to your analysis. Hence, a summary explains the main points of a paper in a concise manner.

You condense the original text, describe the main points, write your thesis and form no opinion about the article. On the other hand, an analysis is the breakdown of the author’s arguments that you use to derive the purpose of the author. When analyzing an article, you're dissecting the main points to draw conclusions about the persuasive ideas of the author. Furthermore, you offer argumentative evidence, strengths and weaknesses of the main points. More importantly, you don't give your opinion. Rather than providing comments on the author’s point of views, you compile evidence of how the author persuades readers to think about a particular topic and whether the author elaborates it adequately.

Examples of an Article Analysis

A summary and analysis essay example illustrates the arguments the author makes and how those claims are valid. For instance, a sample article analysis of “Sex, Lies, and Conversation; Why Is It So Hard for Men and Women to Talk to Each Other” by Deborah Tannen begins with a summary of the main critical points followed by an analytical perspective. One of the precise ways to summarize is to focus on the main ideas that Tannen uses to distinguish between men and women.

The writer of the summary also clearly states how one idea correlates to the other without presenting biases or opinions. Also, the writer doesn't take any sides on whether men or women are to blame for miscommunication. Instead, the summary points to the communication differences between men and women. In the analytical section of the sample, the writer immediately takes a transparent approach to the article and the author. The analysis shows apparent examples from the article with quotes and refers back to the article connecting miscommunication with misinterpretation. Finally, the writer poses various questions that Tannen didn't address, such as strategies for effective communication. However, the writer gives the reader the purpose of Tannen’s article and the reasons the author wrote it.

Another example of an article analysis is “The Year That Changed Everything” by Lance Morrow. The writer presents a concise summary of the elected government positions of Nixon, Kennedy and Johnson. Furthermore, the writer distinguishes between the three elected men's positions and discusses the similarities. The summary tends to lean toward a more powerful tone but effectively explains the author’s point of view for each one of these men. Then, the writer further describes the ideals of the period between morality and immoral values. The analytical aspect of the sample shows the reader the author’s powerful message.

The writer immediately lets the reader know about the persuasive nature of the article and the relevance of the time. For instance, the writer shows the reader in various parts of the article by suggesting examples in specific paragraph numbers. The writer also makes a powerful impact with the use of quotes embedded into the text. The writer uses transition words and active verbs, such as more examples, links, uncovering and secrets, and backs this claim up to describe Morrow’s purpose for the article. The analysis has the audience in mind.

The writer points out the specific details of the time era that only people of the time would relate. More importantly, the writer analyzes Morrow’s ideas as critical to formulating an opinion about Nixon, Kennedy and Johnson. However, the writer points out the assumptions Morrow makes between personal lifestyle and how it affects the political arena. Moreover, the writer suggests that Morrow’s claims aren't entirely valid just because the author mentions historical events. Unlike Tannen’s analytical example, the writer lets the readers know the misconnection between moral value and the lifestyle of many people at the time.

Both article analyses show a clear way to present different persuasive points of view. Unlike a summary, an analysis approach offers the reader an explicit representation of the author’s viewpoints without any opinions. The writers of each sample focus on providing evidence, facts and reasonable statements. Consequently, each example demonstrates the proper use of the critical analysis of the literature and evaluates the purpose of the author. Without seeking an agreement or not, the writer clearly distinguishes between a summary and an analysis of each article.

Related Articles

What Are Two Types of Research Papers?

How to Set Up a Rhetorical Analysis

How to Write a Thesis & Introduction for a Critical Reflection Essay

The Parts of an Argument

How to Write a Technical Essay

How to Write a Critical Summary of an Article

How to Analyze Expository Writing

How to Do a Summary for a Research Paper

• Owlcation: How to Write a Summary, Analysis, and Response Essay Paper with Examples
• Education: One How To: How to Write a Text Analysis Essay

Barbara earned a B. S. in Biochemistry and Chemistry from the Univ. of Houston and the Univ. of Central Florida, respectively. Besides working as a chemist for the pharmaceutical and water industry, she pursued her degree in secondary science teaching. Barbara now writes and researches educational content for blogs and higher-ed sites.

• PRO Courses Guides New Tech Help Pro Expert Videos About wikiHow Pro Upgrade Sign In
• EDIT Edit this Article
• EXPLORE Tech Help Pro About Us Random Article Quizzes Request a New Article Community Dashboard This Or That Game Popular Categories Arts and Entertainment Artwork Books Movies Computers and Electronics Computers Phone Skills Technology Hacks Health Men's Health Mental Health Women's Health Relationships Dating Love Relationship Issues Hobbies and Crafts Crafts Drawing Games Education & Communication Communication Skills Personal Development Studying Personal Care and Style Fashion Hair Care Personal Hygiene Youth Personal Care School Stuff Dating All Categories Arts and Entertainment Finance and Business Home and Garden Relationship Quizzes Cars & Other Vehicles Food and Entertaining Personal Care and Style Sports and Fitness Computers and Electronics Health Pets and Animals Travel Education & Communication Hobbies and Crafts Philosophy and Religion Work World Family Life Holidays and Traditions Relationships Youth
• Browse Articles
• Learn Something New
• Quizzes Hot
• This Or That Game New
• Train Your Brain
• Explore More
• Support wikiHow
• About wikiHow
• Log in / Sign up
• Finance and Business
• Business Skills
• Business Writing

How to Write an Analysis

Last Updated: April 3, 2024 Fact Checked

This article was co-authored by Christopher Taylor, PhD and by wikiHow staff writer, Megaera Lorenz, PhD . Christopher Taylor is an Adjunct Assistant Professor of English at Austin Community College in Texas. He received his PhD in English Literature and Medieval Studies from the University of Texas at Austin in 2014. There are 14 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 289,628 times.

An analysis is a piece of writing that looks at some aspect of a document in detail. To write a good analysis, you’ll need to ask yourself questions that focus on how and why the document works the way it does. You can start the process by gathering information about the subject of your analysis and defining the questions your analysis will answer. Once you’ve outlined your main arguments, look for specific evidence to support them. You can then work on putting your analysis together into a coherent piece of writing.

Gathering Information and Building Your Argument

• If your analysis is supposed to answer a specific question or focus on a particular aspect of the document you are analyzing.
• If there are any length or formatting requirements for the analysis.
• The citation style your instructor wants you to use.
• On what criteria your instructor will evaluate your analysis (e.g., organization, originality, good use of references and quotations, or correct spelling and grammar).

• The title of the document (if it has one).
• The name of the creator of the document. For example, depending on the type of document you’re working with, this could be the author, artist, director, performer, or photographer.
• The form and medium of the document (e.g., “Painting, oil on canvas”).
• When and where the document was created.
• The historical and cultural context of the work.

• Who you believe the intended audience is for the advertisement.
• What rhetorical choices the author made to persuade the audience of their main point.
• What product is being advertised.
• How the poster uses images to make the product look appealing.
• Whether there is any text in the poster, and, if so, how it works together with the images to reinforce the message of the ad.
• What the purpose of the ad is or what its main point is.

• For example, if you’re analyzing an advertisement poster, you might focus on the question: “How does this poster use colors to symbolize the problem that the product is intended to fix? Does it also use color to represent the beneficial results of using the product?”

• For example, you might write, “This poster uses the color red to symbolize the pain of a headache. The blue elements in the design represent the relief brought by the product.”
• You could develop the argument further by saying, “The colors used in the text reinforce the use of colors in the graphic elements of the poster, helping the viewer make a direct connection between the words and images.”

• For example, if you’re arguing that the advertisement poster uses red to represent pain, you might point out that the figure of the headache sufferer is red, while everyone around them is blue. Another piece of evidence might be the use of red lettering for the words “HEADACHE” and “PAIN” in the text of the poster.
• You could also draw on outside evidence to support your claims. For example, you might point out that in the country where the advertisement was produced, the color red is often symbolically associated with warnings or danger.

Tip: If you’re analyzing a text, make sure to properly cite any quotations that you use to support your arguments. Put any direct quotations in quotation marks (“”) and be sure to give location information, such as the page number where the quote appears. Additionally, follow the citation requirements for the style guide assigned by your instructor or one that's commonly used for the subject matter you're writing about.

Organizing and Drafting Your Analysis

• For example, “The poster ‘Say! What a relief,’ created in 1932 by designer Dorothy Plotzky, uses contrasting colors to symbolize the pain of a headache and the relief brought by Miss Burnham’s Pep-Em-Up Pills. The red elements denote pain, while blue ones indicate soothing relief.”

Tip: Your instructor might have specific directions about which information to include in your thesis statement (e.g., the title, author, and date of the document you are analyzing). If you’re not sure how to format your thesis statement or topic sentence, don’t hesitate to ask.

• a. Background
• ii. Analysis/Explanation
• iii. Example
• iv. Analysis/Explanation
• III. Conclusion

• For example, “In the late 1920s, Kansas City schoolteacher Ethel Burnham developed a patent headache medication that quickly achieved commercial success throughout the American Midwest. The popularity of the medicine was largely due to a series of simple but eye-catching advertising posters that were created over the next decade. The poster ‘Say! What a relief,’ created in 1932 by designer Dorothy Plotzky, uses contrasting colors to symbolize the pain of a headache and the relief brought by Miss Burnham’s Pep-Em-Up Pills.”

• Make sure to include clear transitions between each argument and each paragraph. Use transitional words and phrases, such as “Furthermore,” “Additionally,” “For example,” “Likewise,” or “In contrast . . .”
• The best way to organize your arguments will vary based on the individual topic and the specific points you are trying to make. For example, in your analysis of the poster, you might start with arguments about the red visual elements and then move on to a discussion about how the red text fits in.

• For example, you might end your essay with a few sentences about how other advertisements at the time might have been influenced by Dorothy Plotzky’s use of colors.

• For example, in your discussion of the advertisement, avoid stating that you think the art is “beautiful” or that the advertisement is “boring.” Instead, focus on what the poster was supposed to accomplish and how the designer attempted to achieve those goals.

Polishing Your Analysis

• For example, if your essay currently skips around between discussions of the red and blue elements of the poster, consider reorganizing it so that you discuss all the red elements first, then focus on the blue ones.

• For example, you might look for places where you could provide additional examples to support one of your major arguments.

• For example, if you included a paragraph about Dorothy Plotzky’s previous work as a children’s book illustrator, you may want to cut it if it doesn’t somehow relate to her use of color in advertising.
• Cutting material out of your analysis may be difficult, especially if you put a lot of thought into each sentence or found the additional material really interesting. Your analysis will be stronger if you keep it concise and to the point, however.

• You may find it helpful to have someone else go over your essay and look for any mistakes you might have missed.

Tip: When you’re reading silently, it’s easy to miss typos and other small errors because your brain corrects them automatically. Reading your work out loud can make problems easier to spot.

Sample Analysis Outline and Conclusion

Expert Q&A

You Might Also Like

• ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-do-i-make-sure-i-understand-an-assignment-.html
• ↑ https://www.bucks.edu/media/bcccmedialibrary/pdf/HOWTOWRITEALITERARYANALYSISESSAY_10.15.07_001.pdf
• ↑ https://owl.purdue.edu/owl/general_writing/visual_rhetoric/analyzing_visual_documents/elements_of_analysis.html
• ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-can-i-create-stronger-analysis-.html
• ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-do-i-decide-what-i-should-argue-.html
• ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-do-i-effectively-integrate-textual-evidence-.html
• ↑ https://writingcenter.uagc.edu/writing-a-thesis
• ↑ https://owl.purdue.edu/owl/general_writing/visual_rhetoric/analyzing_visual_documents/organizing_your_analysis.html
• ↑ https://lsa.umich.edu/sweetland/undergraduates/writing-guides/how-do-i-write-an-intro--conclusion----body-paragraph.html
• ↑ http://utminers.utep.edu/omwilliamson/engl0310/Textanalysis.htm
• ↑ https://owl.purdue.edu/owl/graduate_writing/graduate_writing_topics/graduate_writing_organization_structure_new.html
• ↑ https://owl.purdue.edu/owl/general_writing/mechanics/sentence_clarity.html
• ↑ https://writingcenter.unc.edu/tips-and-tools/conciseness-handout/
• ↑ https://writingcenter.unc.edu/tips-and-tools/editing-and-proofreading/

About This Article

If you need to write an analysis, first look closely at your assignment to make sure you understand the requirements. Then, gather background information about the document you’ll be analyzing and do a close read so that you’re thoroughly familiar with the subject matter. If it’s not already specified in your assignment, come up with one or more specific question’s you’d like your analysis to answer, then outline your main arguments. Finally, gather evidence and examples to support your arguments. Read on to learn how to organize, draft, and polish your analysis! Did this summary help you? Yes No

• Send fan mail to authors

Did this article help you?

Featured Articles

Trending Articles

Watch Articles

• Terms of Use
• Privacy Policy
• Do Not Sell or Share My Info
• Not Selling Info

Don’t miss out! Sign up for

wikiHow’s newsletter

How to Critically Analyse an Article

Critical analysis refers to the skill required to evaluate an author’s work. Students are frequently asked to critically analyse a particular journal. The analysis is designed to enhance the reader’s understanding of the thesis and content of the article, and crucially is subjective, because a piece of critical analysis writing is a way for the writer to express their opinions, analysis, and evaluation of the article in question. In essence, the article needs to be broken down into parts, each one analysed separately and then brought together as one piece of critical analysis of the whole.

Key point: you need to be aware that when you are analysing an article your goal is to ensure that your readers understand the main points of the paper with ease. This means demonstrating critical thinking skills, judgement, and evaluation to illustrate how you came to your conclusions and opinions on the work. This might sound simple, and it can be, if you follow our guide to critically analyse an article:

• Before you start your essay, you should read through the paper at least three times.
• The first time ensures you understand, the second allows you to examine the structure of the work and the third enables you to pick out the key points and focus of the thesis statement given by the author (if there is one of course!). During these reads and re-reads you can set down bullet points which will eventually frame your outline and draft for the final work.
• Look for the purpose of the article – is the writer trying to inform through facts and research, are they trying to persuade through logical argument, or are they simply trying to entertain and create an emotional response. Examine your own responses to the article and this will guide to the purpose.
• When you start writing your analysis, avoid phrases such as “I think/believe”, “In my opinion”. The analysis is of the paper, not your views and perspectives.
• Ensure you have clearly indicated the subject of the article so that is evident to the reader.
• Look for both strengths and weaknesses in the work – and always support your assertions with credible, viable sources that are clearly referenced at the end of your work.
• Be open-minded and objective, rely on facts and evidence as you pull your work together.

Structure for Critical Analysis of an Article

Remember, your essay should be in three mains sections: the introduction, the main body, and a conclusion.

Introduction

Your introduction should commence by indicating the title of the work being analysed, including author and date of publication. This should be followed by an indication of the main themes in the thesis statement. Once you have provided the information about the author’s paper, you should then develop your thesis statement which sets out what you intend to achieve or prove with your critical analysis of the article.

Key point: your introduction should be short, succinct and draw your readers in. Keep it simple and concise but interesting enough to encourage further reading.

Overview of the paper

This is an important section to include when writing a critical analysis of an article because it answers the four “w’s”, of what, why, who, when and also the how. This section should include a brief overview of the key ideas in the article, along with the structure, style and dominant point of view expressed. For example,

“The focus of this article is… based on work undertaken…  The main thrust of the thesis is that… which is the foundation for an argument which suggests. The conclusion from the authors is that…. However, it can be argued that…

Once you have given the overview and outline, you can then move onto the more detailed analysis.

For each point you make about the article, you should contain this in a separate paragraph. Introduce the point you wish to make, regarding what you see as a strength or weakness of the work, provide evidence for your perspective from reliable and credible sources, and indicate how the authors have achieved, or not their goal in relation to the points made. For each point, you should identify whether the paper is objective, informative, persuasive, and sufficiently unbiased. In addition, identify whether the target audience for the work has been correctly addressed, the survey instruments used are appropriate and the results are presented in a clear and concise way.

If the authors have used tables, figures or graphs do they back up the conclusions made? If not, why not? Again, back up your statements with reliable hard evidence and credible sources, fully referenced at the end of your work.

In the same way that an introduction opens up the analysis to readers, the conclusion should close it. Clearly, concisely and without the addition of any new information not included in the body paragraph.

Key points for a strong conclusion include restating your thesis statement, paraphrased, with a summary of the evidence for the accuracy of your views, combined with identification of how the article could have been improved – in other words, asking the reader to take action.

Key phrases for Critical Analysis of an article

• This article has value because it…
• There is a clear bias within this article based on the focus on…
• It appears that the assumptions made do not correlate with the information presented…
• Aspects of the work suggest that…
• The proposal is therefore that…
• The evidence presented supports the view that…
• The evidence presented however overlooks…
• Whilst the author’s view is generally accurate, it can also be indicated that…
• Closer examination suggests there is an omission in relation to

You may also like

Information Literacy Resource Bank

Reading and critically analysing a journal article.

• Explain the broad categorisations of journal articles
• Explain/outline the peer review process whereby an article is submitted for scholarly evaluation by experts
• Suggest questions you need to ask when reading and critically analysing a journal article
• Provide practical tips to facilitate the effective reading and critical analysis of an article

No comments.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

We use cookies to give you the best experience possible. By continuing we’ll assume you’re on board with our cookie policy

• A Research Guide
• Writing Guide
• Article Writing

How to Analyze an Article

• What is an article analysis
• Outline and structure
• Step-by-step writing guide
• Article analysis format
• Analysis examples
• Article analysis template

What Is an Article Analysis?

• Summarize the main points in the piece – when you get to do an article analysis, you have to analyze the main points so that the reader can understand what the article is all about in general. The summary will be an overview of the story outline, but it is not the main analysis. It just acts to guide the reader to understand what the article is all about in brief.
• Proceed to the main argument and analyze the evidence offered by the writer in the article – this is where analysis begins because you must critique the article by analyzing the evidence given by the piece’s author. You should also point out the flaws in the work and support where it needs to be; it should not necessarily be a positive critique. You are free to pinpoint even the negative part of the story. In other words, you should not rely on one side but be truthful about what you are addressing to the satisfaction of anyone who would read your essay.
• Analyze the piece’s significance – most readers would want to see why you need to make article analysis. It is your role as a writer to emphasize the importance of the article so that the reader can be content with your writing. When your audience gets interested in your work, you will have achieved your aim because the main aim of writing is to convince the reader. The more persuasive you are, the more your article stands out. Focus on motivating your audience, and you will have scored.

Outline and Structure of an Article Analysis

What do you need to write an article analysis, how to write an analysis of an article, step 1: analyze your audience, step 2: read the article.

• The evidence : identify the evidence the writer used in the article to support their claim. While looking into the evidence, you should gauge whether the writer brings out factual evidence or it is personal judgments.
• The argument’s validity: a writer might use many pieces of evidence to support their claims, but you need to identify the sources they use and determine whether they are credible. Credible sources are like scholarly articles and books, and some are not worth relying on for research.
• How convictive are the arguments? You should be able to judge the writer’s persuasion of the audience. An article is usually informative and therefore has to be persuasive to the readers to be considered worthy. If it does not achieve this, you should be able to critique that and illustrate the same.

Step 3: Make the plan

Step 4: write a critical analysis of an article, step 5: edit your essay, article analysis format, article analysis example, what didn’t you know about the article analysis template.

• Read through the piece quickly to get an overview.
• Look for confronting words in the article and note them down.
• Read the piece for the second time while summarizing major points in the literature piece.
• Reflect on the paper’s thesis to affirm and adhere to it in your writing.
• Note the arguments and the evidence used.
• Evaluate the article and focus on your audience.
• Give your opinion and support it to the satisfaction of your audience.

Receive paper in 3 Hours!

• Choose the number of pages.
• Select your deadline.
• Complete your order.

Number of Pages

550 words (double spaced)

Deadline: 10 days left

By clicking "Log In", you agree to our terms of service and privacy policy . We'll occasionally send you account related and promo emails.

Sign Up for your FREE account

Forget about ChatGPT and get quality content right away.

• Link to facebook
• Link to linkedin
• Link to twitter
• Link to youtube
• Writing Tips

 How to Write a Perfect Analytical Paragraph

• 8-minute read
• 30th January 2023

If you are looking up how to write an analytical paragraph, you are most likely writing an argumentative or analytical essay. Analytical essays are similar to other essays, such as descriptive essays, in that you have a central idea, organize supporting ideas into body paragraphs, and make conclusions.

However, analytical essays differ from other essays because the writer must go further. They require the writer to interpret and analyze a given text or information using evidence to support their central idea or thesis statement. This analysis takes place in analytical paragraphs, or body paragraphs, if you are writing an analytical essay .

In this article, you’ll learn the components of a perfect analytical paragraph: the topic sentence, evidence, analysis, and conclusion. Keep reading to learn more.

What Is an Analytical Paragraph?

An analytical paragraph is a paragraph that breaks down a piece of literature, an idea, or a concept into smaller parts and analyzes each part to understand the whole. Being able to write an effective and successful analytical paragraph reflects a writer’s critical thinking and organizational writing skills. All in all, like any other type of writing, writing an analytical paragraph requires skill and practice.

Write the Topic Sentence

A topic sentence is usually the first, or sometimes second, sentence at the beginning of anybody paragraph. Your topic sentence should contain one main idea related to the thesis statement . If it is not related to your thesis statement, then you are likely off topic.

Pro Tip: If your topic sentence is the second sentence of your paragraph, then your first sentence should be a transitional sentence .

Let’s look at a thesis statement and some topic sentences to get a better idea.

Topic: Examine and analyze the marriages in George Eliot’s Middlemarch .

Thesis Statement: Eliot uses three different marriages to give depth to everyday people and show the reader the struggles of marriage within the nineteenth century’s societal standards of submissive roles, class range, and financial status.

Topic Sentence 1: Lydgate and Rosamond had a terrible marriage in Middlemarch , like all other marriages during this time.

This topic sentence is not effective because it is not specific enough and does not directly relate to the thesis statement. It does not mention how their “terrible” marriage is related to submissive roles, class range, or financial status. Additionally, the overly generalized language of “all” marriages being terrible marriages during this time is a weak argument.

Topic Sentence 2: Financial matters play a huge role in the Lydgate and Rosamond marriage, as Lydgate has no money and Rosamond is a big spender.

This topic sentence is effective because it directly supports the thesis statement. It is focused on the financial status of this marriage.

Provide Evidence

The type of evidence you use to support your topic sentence will largely depend on the topic of your analytical essay. For example, if you are writing an essay related to a work of literature, you will need to provide direct quotes, paraphrasing, specific details, or a summary from the work to support your main idea. If your topic is related to analyzing data, then you may use figures, statistics, or charts and graph evidence to support your topic sentence.

Regardless of what type of evidence you provide, it must be appropriate and directly relate to and support your topic sentence.

For example, if we take the above thesis and topic sentence, we might select direct quotes, paraphrases, or summaries from the novel Middlemarch that depict the marriage’s financial stress.

Pro Tip: When using direct quotes, make sure you always provide an in-text citation and use correct punctuation to ensure your essay is neat and clean.

Find this useful?

Subscribe to our newsletter and get writing tips from our editors straight to your inbox.

Once you have provided evidence, you should analyze it to illustrate its significance and how it relates to the topic sentence. In your analysis, you can discuss how an author uses certain literary devices to emphasize character traits, themes, patterns, and connections in a literary work.

Be sure that your analysis always connects to the topic sentence/main idea of the paragraph. Avoid introducing new ideas in this section. Save those for later paragraphs or consider creating a new one to explore and analyze the new point.

Conclude Your Paragraph

When closing an analytical paragraph, you can consider doing two things:

●  Briefly emphasize the main point your reader should take away after having read the paragraph.

●  Begin a transition if the analysis continues into the next paragraph. (This strategy may be more suitable for longer, more in-depth analytical essays).

Using the above example topic sentence, we might conclude the paragraph as follows:

Notice how this concluding statement not only emphasizes the main points from the paragraph but also ties back into the thesis statement.

Writing Tips For Analytical Paragraphs

Leave out first person language.

Avoid using language such as “in my opinion,” “from my perspective,” or “I think.” While the analysis is your interpretation of a text or information, you should rely on and focus on using evidence to support your ideas. Overall, you should aim to maintain an objective tone .

Instead of saying “I think Rosamond is manipulative,” you should use evidence from the text to show that she was manipulative. For example, “Rosamond shows a pattern of manipulation throughout Middlemarch , specifically toward her husband. For instance, she says, ‘…’”

Do Writing Exercises

When writing, especially in the early drafts of an essay, it is typical to find the main idea of a paragraph at the end. This is a natural course for our thinking process. However, the main idea should be presented as your topic sentence at the beginning of this paragraph. Additionally, most students leave this main idea at the end because they do not identify it as the main idea.

To overcome this dilemma, try a looping prewriting exercise . In this exercise, you write continuously for a designated time (maybe 10 minutes, your choice). At the end of that time, read over what you’ve written and circle the main idea of the text (this is usually at the end). In the next cycle, you start with this main idea at the beginning and further examine and analyze it.

This is a wonderful exercise to help you pick out main ideas and delve deeper into your analysis.

Get Feedback

If you are a student, there are several options to get feedback for free. Ask a friend to read your essay. Go to your writing center to get feedback and help with your writing. Go to your professor’s office hours with your writing or questions to get detailed advice. More often than not, they are happy to see you take advantage of their expertise.

As a working professional, writer, or author, you can look to fellow authors or bookish friends to read your work. You can find free beta readers online from sites such as Goodreads to get feedback from your target audience. You can also find writing groups on social media platforms.

Proofread Your Work

It can be easy to finish writing an essay and think “Finally, I’m done!” Unfortunately, that is only half the process. Be sure to always read and reread your writing before hitting submit. Check for stray commas, spelling errors, or awkward sentences to make your main ideas and hard work shine. Learn about 6 Quick and Easy Tips for Proofreading you can do at home.

Writing an analytical paragraph doesn’t have to be stressful. Be sure to include a topic sentence at the beginning of your paragraph that connects to the thesis statement. Provide a variety of evidence to support your main idea, analyze the text by highlighting literary devices used, themes, and patterns, and end with a brief concluding statement.

If you need more help with writing analysis, descriptive essays, or any other type of essay, then Proofed is here to help. Try our free trial today!

What Is a Topic Sentence?

A topic sentence goes at the beginning of a body paragraph and clearly states the main idea of the paragraph.

How Do I Organize an Analytical Paragraph?

An analytical paragraph has four components: topic sentence, evidence, analysis, and conclusion. The topic sentence is the most important part of any body paragraph because it establishes the main idea of the paragraph and relates to the thesis statement.

What Makes a Good Analytical Paragraph?

A good analytical paragraph has a clear topic sentence, strong evidence, and a thorough analysis that reflects the writer’s critical thinking and writing skills. It should conclude by emphasizing the main idea of the paragraph and how it supports the essay overall.

Share this article:

Post A New Comment

Got content that needs a quick turnaround? Let us polish your work. Explore our editorial business services.

3-minute read

What Is a Content Editor?

Are you interested in learning more about the role of a content editor and the...

4-minute read

The Benefits of Using an Online Proofreading Service

Proofreading is important to ensure your writing is clear and concise for your readers. Whether...

2-minute read

6 Online AI Presentation Maker Tools

Creating presentations can be time-consuming and frustrating. Trying to construct a visually appealing and informative...

What Is Market Research?

No matter your industry, conducting market research helps you keep up to date with shifting...

8 Press Release Distribution Services for Your Business

In a world where you need to stand out, press releases are key to being...

How to Get a Patent

In the United States, the US Patent and Trademarks Office issues patents. In the United...

Make sure your writing is the best it can be with our expert English proofreading and editing.

Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

Welcome to the Purdue OWL

This page is brought to you by the OWL at Purdue University. When printing this page, you must include the entire legal notice.

Copyright ©1995-2018 by The Writing Lab & The OWL at Purdue and Purdue University. All rights reserved. This material may not be published, reproduced, broadcast, rewritten, or redistributed without permission. Use of this site constitutes acceptance of our terms and conditions of fair use.

Primary research involves collecting data about a given subject directly from the real world. This section includes information on what primary research is, how to get started, ethics involved with primary research and different types of research you can do. It includes details about interviews, surveys, observations, and analysis.

Analysis is a type of primary research that involves finding and interpreting patterns in data, classifying those patterns, and generalizing the results. It is useful when looking at actions, events, or occurrences in different texts, media, or publications. Analysis can usually be done without considering most of the ethical issues discussed in the overview, as you are not working with people but rather publicly accessible documents. Analysis can be done on new documents or performed on raw data that you yourself have collected.

Here are several examples of analysis:

• Recording commercials on three major television networks and analyzing race and gender within the commercials to discover some conclusion.
• Analyzing the historical trends in public laws by looking at the records at a local courthouse.
• Analyzing topics of discussion in chat rooms for patterns based on gender and age.

Analysis research involves several steps:

• Finding and collecting documents.
• Specifying criteria or patterns that you are looking for.
• Analyzing documents for patterns, noting number of occurrences or other factors.

8.5 Writing Process: Creating an Analytical Report

Learning outcomes.

By the end of this section, you will be able to:

• Identify the elements of the rhetorical situation for your report.
• Find and focus a topic to write about.
• Gather and analyze information from appropriate sources.
• Distinguish among different kinds of evidence.
• Draft a thesis and create an organizational plan.
• Compose a report that develops ideas and integrates evidence from sources.
• Give and act on productive feedback to works in progress.

You might think that writing comes easily to experienced writers—that they draft stories and college papers all at once, sitting down at the computer and having sentences flow from their fingers like water from a faucet. In reality, most writers engage in a recursive process, pushing forward, stepping back, and repeating steps multiple times as their ideas develop and change. In broad strokes, the steps most writers go through are these:

• Planning and Organization . You will have an easier time drafting if you devote time at the beginning to consider the rhetorical situation for your report, understand your assignment, gather ideas and information, draft a thesis statement, and create an organizational plan.
• Drafting . When you have an idea of what you want to say and the order in which you want to say it, you’re ready to draft. As much as possible, keep going until you have a complete first draft of your report, resisting the urge to go back and rewrite. Save that for after you have completed a first draft.
• Review . Now is the time to get feedback from others, whether from your instructor, your classmates, a tutor in the writing center, your roommate, someone in your family, or someone else you trust to read your writing critically and give you honest feedback.
• Revising . With feedback on your draft, you are ready to revise. You may need to return to an earlier step and make large-scale revisions that involve planning, organizing, and rewriting, or you may need to work mostly on ensuring that your sentences are clear and correct.

Considering the Rhetorical Situation

Like other kinds of writing projects, a report starts with assessing the rhetorical situation —the circumstance in which a writer communicates with an audience of readers about a subject. As the writer of a report, you make choices based on the purpose of your writing, the audience who will read it, the genre of the report, and the expectations of the community and culture in which you are working. A graphic organizer like Table 8.1 can help you begin.

Summary of Assignment

Write an analytical report on a topic that interests you and that you want to know more about. The topic can be contemporary or historical, but it must be one that you can analyze and support with evidence from sources.

The following questions can help you think about a topic suitable for analysis:

• Why or how did ________ happen?
• What are the results or effects of ________?
• Is ________ a problem? If so, why?
• What are examples of ________ or reasons for ________?
• How does ________ compare to or contrast with other issues, concerns, or things?

Consult and cite three to five reliable sources. The sources do not have to be scholarly for this assignment, but they must be credible, trustworthy, and unbiased. Possible sources include academic journals, newspapers, magazines, reputable websites, government publications or agency websites, and visual sources such as TED Talks. You may also use the results of an experiment or survey, and you may want to conduct interviews.

Consider whether visuals and media will enhance your report. Can you present data you collect visually? Would a map, photograph, chart, or other graphic provide interesting and relevant support? Would video or audio allow you to present evidence that you would otherwise need to describe in words?

Another Lens. To gain another analytic view on the topic of your report, consider different people affected by it. Say, for example, that you have decided to report on recent high school graduates and the effect of the COVID-19 pandemic on the final months of their senior year. If you are a recent high school graduate, you might naturally gravitate toward writing about yourself and your peers. But you might also consider the adults in the lives of recent high school graduates—for example, teachers, parents, or grandparents—and how they view the same period. Or you might consider the same topic from the perspective of a college admissions department looking at their incoming freshman class.

Quick Launch: Finding and Focusing a Topic

Coming up with a topic for a report can be daunting because you can report on nearly anything. The topic can easily get too broad, trapping you in the realm of generalizations. The trick is to find a topic that interests you and focus on an angle you can analyze in order to say something significant about it. You can use a graphic organizer to generate ideas, or you can use a concept map similar to the one featured in Writing Process: Thinking Critically About a “Text.”

Asking the Journalist’s Questions

One way to generate ideas about a topic is to ask the five W (and one H) questions, also called the journalist’s questions : Who? What? When? Where? Why? How? Try answering the following questions to explore a topic:

Who was or is involved in ________?

What happened/is happening with ________? What were/are the results of ________?

When did ________ happen? Is ________ happening now?

Where did ________ happen, or where is ________ happening?

Why did ________ happen, or why is ________ happening now?

How did ________ happen?

For example, imagine that you have decided to write your analytical report on the effect of the COVID-19 shutdown on high-school students by interviewing students on your college campus. Your questions and answers might look something like those in Table 8.2 :

Asking Focused Questions

Another way to find a topic is to ask focused questions about it. For example, you might ask the following questions about the effect of the 2020 pandemic shutdown on recent high school graduates:

• How did the shutdown change students’ feelings about their senior year?
• How did the shutdown affect their decisions about post-graduation plans, such as work or going to college?
• How did the shutdown affect their academic performance in high school or in college?
• How did/do they feel about continuing their education?
• How did the shutdown affect their social relationships?

Any of these questions might be developed into a thesis for an analytical report. Table 8.3 shows more examples of broad topics and focusing questions.

Gathering Information

Because they are based on information and evidence, most analytical reports require you to do at least some research. Depending on your assignment, you may be able to find reliable information online, or you may need to do primary research by conducting an experiment, a survey, or interviews. For example, if you live among students in their late teens and early twenties, consider what they can tell you about their lives that you might be able to analyze. Returning to or graduating from high school, starting college, or returning to college in the midst of a global pandemic has provided them, for better or worse, with educational and social experiences that are shared widely by people their age and very different from the experiences older adults had at the same age.

Some report assignments will require you to do formal research, an activity that involves finding sources and evaluating them for reliability, reading them carefully, taking notes, and citing all words you quote and ideas you borrow. See Research Process: Accessing and Recording Information and Annotated Bibliography: Gathering, Evaluating, and Documenting Sources for detailed instruction on conducting research.

Whether you conduct in-depth research or not, keep track of the ideas that come to you and the information you learn. You can write or dictate notes using an app on your phone or computer, or you can jot notes in a journal if you prefer pen and paper. Then, when you are ready to begin organizing your report, you will have a record of your thoughts and information. Always track the sources of information you gather, whether from printed or digital material or from a person you interviewed, so that you can return to the sources if you need more information. And always credit the sources in your report.

Kinds of Evidence

Depending on your assignment and the topic of your report, certain kinds of evidence may be more effective than others. Other kinds of evidence may even be required. As a general rule, choose evidence that is rooted in verifiable facts and experience. In addition, select the evidence that best supports the topic and your approach to the topic, be sure the evidence meets your instructor’s requirements, and cite any evidence you use that comes from a source. The following list contains different kinds of frequently used evidence and an example of each.

Definition : An explanation of a key word, idea, or concept.

The U.S. Census Bureau refers to a “young adult” as a person between 18 and 34 years old.

Example : An illustration of an idea or concept.

The college experience in the fall of 2020 was starkly different from that of previous years. Students who lived in residence halls were assigned to small pods. On-campus dining services were limited. Classes were small and physically distanced or conducted online. Parties were banned.

Expert opinion : A statement by a professional in the field whose opinion is respected.

According to Louise Aronson, MD, geriatrician and author of Elderhood , people over the age of 65 are the happiest of any age group, reporting “less stress, depression, worry, and anger, and more enjoyment, happiness, and satisfaction” (255).

Fact : Information that can be proven correct or accurate.

According to data collected by the NCAA, the academic success of Division I college athletes between 2015 and 2019 was consistently high (Hosick).

Interview : An in-person, phone, or remote conversation that involves an interviewer posing questions to another person or people.

During our interview, I asked Betty about living without a cell phone during the pandemic. She said that before the pandemic, she hadn’t needed a cell phone in her daily activities, but she soon realized that she, and people like her, were increasingly at a disadvantage.

Quotation : The exact words of an author or a speaker.

In response to whether she thought she needed a cell phone, Betty said, “I got along just fine without a cell phone when I could go everywhere in person. The shift to needing a phone came suddenly, and I don’t have extra money in my budget to get one.”

Statistics : A numerical fact or item of data.

The Pew Research Center reported that approximately 25 percent of Hispanic Americans and 17 percent of Black Americans relied on smartphones for online access, compared with 12 percent of White people.

Survey : A structured interview in which respondents (the people who answer the survey questions) are all asked the same questions, either in person or through print or electronic means, and their answers tabulated and interpreted. Surveys discover attitudes, beliefs, or habits of the general public or segments of the population.

A survey of 3,000 mobile phone users in October 2020 showed that 54 percent of respondents used their phones for messaging, while 40 percent used their phones for calls (Steele).

• Visuals : Graphs, figures, tables, photographs and other images, diagrams, charts, maps, videos, and audio recordings, among others.

Thesis and Organization

Drafting a thesis.

When you have a grasp of your topic, move on to the next phase: drafting a thesis. The thesis is the central idea that you will explore and support in your report; all paragraphs in your report should relate to it. In an essay-style analytical report, you will likely express this main idea in a thesis statement of one or two sentences toward the end of the introduction.

For example, if you found that the academic performance of student athletes was higher than that of non-athletes, you might write the following thesis statement:

student sample text Although a common stereotype is that college athletes barely pass their classes, an analysis of athletes’ academic performance indicates that athletes drop fewer classes, earn higher grades, and are more likely to be on track to graduate in four years when compared with their non-athlete peers. end student sample text

The thesis statement often previews the organization of your writing. For example, in his report on the U.S. response to the COVID-19 pandemic in 2020, Trevor Garcia wrote the following thesis statement, which detailed the central idea of his report:

student sample text An examination of the U.S. response shows that a reduction of experts in key positions and programs, inaction that led to equipment shortages, and inconsistent policies were three major causes of the spread of the virus and the resulting deaths. end student sample text

After you draft a thesis statement, ask these questions, and examine your thesis as you answer them. Revise your draft as needed.

• Is it interesting? A thesis for a report should answer a question that is worth asking and piques curiosity.
• Is it precise and specific? If you are interested in reducing pollution in a nearby lake, explain how to stop the zebra mussel infestation or reduce the frequent algae blooms.
• Is it manageable? Try to split the difference between having too much information and not having enough.

Organizing Your Ideas

As a next step, organize the points you want to make in your report and the evidence to support them. Use an outline, a diagram, or another organizational tool, such as Table 8.4 .

Drafting an Analytical Report

With a tentative thesis, an organization plan, and evidence, you are ready to begin drafting. For this assignment, you will report information, analyze it, and draw conclusions about the cause of something, the effect of something, or the similarities and differences between two different things.

Introduction

Some students write the introduction first; others save it for last. Whenever you choose to write the introduction, use it to draw readers into your report. Make the topic of your report clear, and be concise and sincere. End the introduction with your thesis statement. Depending on your topic and the type of report, you can write an effective introduction in several ways. Opening a report with an overview is a tried-and-true strategy, as shown in the following example on the U.S. response to COVID-19 by Trevor Garcia. Notice how he opens the introduction with statistics and a comparison and follows it with a question that leads to the thesis statement (underlined).

student sample text With more than 83 million cases and 1.8 million deaths at the end of 2020, COVID-19 has turned the world upside down. By the end of 2020, the United States led the world in the number of cases, at more than 20 million infections and nearly 350,000 deaths. In comparison, the second-highest number of cases was in India, which at the end of 2020 had less than half the number of COVID-19 cases despite having a population four times greater than the U.S. (“COVID-19 Coronavirus Pandemic,” 2021). How did the United States come to have the world’s worst record in this pandemic? underline An examination of the U.S. response shows that a reduction of experts in key positions and programs, inaction that led to equipment shortages, and inconsistent policies were three major causes of the spread of the virus and the resulting deaths end underline . end student sample text

For a less formal report, you might want to open with a question, quotation, or brief story. The following example opens with an anecdote that leads to the thesis statement (underlined).

student sample text Betty stood outside the salon, wondering how to get in. It was June of 2020, and the door was locked. A sign posted on the door provided a phone number for her to call to be let in, but at 81, Betty had lived her life without a cell phone. Betty’s day-to-day life had been hard during the pandemic, but she had planned for this haircut and was looking forward to it; she had a mask on and hand sanitizer in her car. Now she couldn’t get in the door, and she was discouraged. In that moment, Betty realized how much Americans’ dependence on cell phones had grown in the months since the pandemic began. underline Betty and thousands of other senior citizens who could not afford cell phones or did not have the technological skills and support they needed were being left behind in a society that was increasingly reliant on technology end underline . end student sample text

Body Paragraphs: Point, Evidence, Analysis

Use the body paragraphs of your report to present evidence that supports your thesis. A reliable pattern to keep in mind for developing the body paragraphs of a report is point , evidence , and analysis :

• The point is the central idea of the paragraph, usually given in a topic sentence stated in your own words at or toward the beginning of the paragraph. Each topic sentence should relate to the thesis.
• The evidence you provide develops the paragraph and supports the point made in the topic sentence. Include details, examples, quotations, paraphrases, and summaries from sources if you conducted formal research. Synthesize the evidence you include by showing in your sentences the connections between sources.
• The analysis comes at the end of the paragraph. In your own words, draw a conclusion about the evidence you have provided and how it relates to the topic sentence.

The paragraph below illustrates the point, evidence, and analysis pattern. Drawn from a report about concussions among football players, the paragraph opens with a topic sentence about the NCAA and NFL and their responses to studies about concussions. The paragraph is developed with evidence from three sources. It concludes with a statement about helmets and players’ safety.

student sample text The NCAA and NFL have taken steps forward and backward to respond to studies about the danger of concussions among players. Responding to the deaths of athletes, documented brain damage, lawsuits, and public outcry (Buckley et al., 2017), the NCAA instituted protocols to reduce potentially dangerous hits during football games and to diagnose traumatic head injuries more quickly and effectively. Still, it has allowed players to wear more than one style of helmet during a season, raising the risk of injury because of imperfect fit. At the professional level, the NFL developed a helmet-rating system in 2011 in an effort to reduce concussions, but it continued to allow players to wear helmets with a wide range of safety ratings. The NFL’s decision created an opportunity for researchers to look at the relationship between helmet safety ratings and concussions. Cocello et al. (2016) reported that players who wore helmets with a lower safety rating had more concussions than players who wore helmets with a higher safety rating, and they concluded that safer helmets are a key factor in reducing concussions. end student sample text

Developing Paragraph Content

In the body paragraphs of your report, you will likely use examples, draw comparisons, show contrasts, or analyze causes and effects to develop your topic.

Paragraphs developed with Example are common in reports. The paragraph below, adapted from a report by student John Zwick on the mental health of soldiers deployed during wartime, draws examples from three sources.

student sample text Throughout the Vietnam War, military leaders claimed that the mental health of soldiers was stable and that men who suffered from combat fatigue, now known as PTSD, were getting the help they needed. For example, the New York Times (1966) quoted military leaders who claimed that mental fatigue among enlisted men had “virtually ceased to be a problem,” occurring at a rate far below that of World War II. Ayres (1969) reported that Brigadier General Spurgeon Neel, chief American medical officer in Vietnam, explained that soldiers experiencing combat fatigue were admitted to the psychiatric ward, sedated for up to 36 hours, and given a counseling session with a doctor who reassured them that the rest was well deserved and that they were ready to return to their units. Although experts outside the military saw profound damage to soldiers’ psyches when they returned home (Halloran, 1970), the military stayed the course, treating acute cases expediently and showing little concern for the cumulative effect of combat stress on individual soldiers. end student sample text

When you analyze causes and effects , you explain the reasons that certain things happened and/or their results. The report by Trevor Garcia on the U.S. response to the COVID-19 pandemic in 2020 is an example: his report examines the reasons the United States failed to control the coronavirus. The paragraph below, adapted from another student’s report written for an environmental policy course, explains the effect of white settlers’ views of forest management on New England.

student sample text The early colonists’ European ideas about forest management dramatically changed the New England landscape. White settlers saw the New World as virgin, unused land, even though indigenous people had been drawing on its resources for generations by using fire subtly to improve hunting, employing construction techniques that left ancient trees intact, and farming small, efficient fields that left the surrounding landscape largely unaltered. White settlers’ desire to develop wood-built and wood-burning homesteads surrounded by large farm fields led to forestry practices and techniques that resulted in the removal of old-growth trees. These practices defined the way the forests look today. end student sample text

Compare and contrast paragraphs are useful when you wish to examine similarities and differences. You can use both comparison and contrast in a single paragraph, or you can use one or the other. The paragraph below, adapted from a student report on the rise of populist politicians, compares the rhetorical styles of populist politicians Huey Long and Donald Trump.

student sample text A key similarity among populist politicians is their rejection of carefully crafted sound bites and erudite vocabulary typically associated with candidates for high office. Huey Long and Donald Trump are two examples. When he ran for president, Long captured attention through his wild gesticulations on almost every word, dramatically varying volume, and heavily accented, folksy expressions, such as “The only way to be able to feed the balance of the people is to make that man come back and bring back some of that grub that he ain’t got no business with!” In addition, Long’s down-home persona made him a credible voice to represent the common people against the country’s rich, and his buffoonish style allowed him to express his radical ideas without sounding anti-communist alarm bells. Similarly, Donald Trump chose to speak informally in his campaign appearances, but the persona he projected was that of a fast-talking, domineering salesman. His frequent use of personal anecdotes, rhetorical questions, brief asides, jokes, personal attacks, and false claims made his speeches disjointed, but they gave the feeling of a running conversation between him and his audience. For example, in a 2015 speech, Trump said, “They just built a hotel in Syria. Can you believe this? They built a hotel. When I have to build a hotel, I pay interest. They don’t have to pay interest, because they took the oil that, when we left Iraq, I said we should’ve taken” (“Our Country Needs” 2020). While very different in substance, Long and Trump adopted similar styles that positioned them as the antithesis of typical politicians and their worldviews. end student sample text

The conclusion should draw the threads of your report together and make its significance clear to readers. You may wish to review the introduction, restate the thesis, recommend a course of action, point to the future, or use some combination of these. Whichever way you approach it, the conclusion should not head in a new direction. The following example is the conclusion from a student’s report on the effect of a book about environmental movements in the United States.

student sample text Since its publication in 1949, environmental activists of various movements have found wisdom and inspiration in Aldo Leopold’s A Sand County Almanac . These audiences included Leopold’s conservationist contemporaries, environmentalists of the 1960s and 1970s, and the environmental justice activists who rose in the 1980s and continue to make their voices heard today. These audiences have read the work differently: conservationists looked to the author as a leader, environmentalists applied his wisdom to their movement, and environmental justice advocates have pointed out the flaws in Leopold’s thinking. Even so, like those before them, environmental justice activists recognize the book’s value as a testament to taking the long view and eliminating biases that may cloud an objective assessment of humanity’s interdependent relationship with the environment. end student sample text

Citing Sources

You must cite the sources of information and data included in your report. Citations must appear in both the text and a bibliography at the end of the report.

The sample paragraphs in the previous section include examples of in-text citation using APA documentation style. Trevor Garcia’s report on the U.S. response to COVID-19 in 2020 also uses APA documentation style for citations in the text of the report and the list of references at the end. Your instructor may require another documentation style, such as MLA or Chicago.

Peer Review: Getting Feedback from Readers

You will likely engage in peer review with other students in your class by sharing drafts and providing feedback to help spot strengths and weaknesses in your reports. For peer review within a class, your instructor may provide assignment-specific questions or a form for you to complete as you work together.

If you have a writing center on your campus, it is well worth your time to make an online or in-person appointment with a tutor. You’ll receive valuable feedback and improve your ability to review not only your report but your overall writing.

Another way to receive feedback on your report is to ask a friend or family member to read your draft. Provide a list of questions or a form such as the one in Table 8.5 for them to complete as they read.

Revising: Using Reviewers’ Responses to Revise your Work

When you receive comments from readers, including your instructor, read each comment carefully to understand what is being asked. Try not to get defensive, even though this response is completely natural. Remember that readers are like coaches who want you to succeed. They are looking at your writing from outside your own head, and they can identify strengths and weaknesses that you may not have noticed. Keep track of the strengths and weaknesses your readers point out. Pay special attention to those that more than one reader identifies, and use this information to improve your report and later assignments.

As you analyze each response, be open to suggestions for improvement, and be willing to make significant revisions to improve your writing. Perhaps you need to revise your thesis statement to better reflect the content of your draft. Maybe you need to return to your sources to better understand a point you’re trying to make in order to develop a paragraph more fully. Perhaps you need to rethink the organization, move paragraphs around, and add transition sentences.

Below is an early draft of part of Trevor Garcia’s report with comments from a peer reviewer:

student sample text To truly understand what happened, it’s important first to look back to the years leading up to the pandemic. Epidemiologists and public health officials had long known that a global pandemic was possible. In 2016, the U.S. National Security Council (NSC) published a 69-page document with the intimidating title Playbook for Early Response to High-Consequence Emerging Infectious Disease Threats and Biological Incidents . The document’s two sections address responses to “emerging disease threats that start or are circulating in another country but not yet confirmed within U.S. territorial borders” and to “emerging disease threats within our nation’s borders.” On 13 January 2017, the joint Obama-Trump transition teams performed a pandemic preparedness exercise; however, the playbook was never adopted by the incoming administration. end student sample text

annotated text Peer Review Comment: Do the words in quotation marks need to be a direct quotation? It seems like a paraphrase would work here. end annotated text

annotated text Peer Review Comment: I’m getting lost in the details about the playbook. What’s the Obama-Trump transition team? end annotated text

student sample text In February 2018, the administration began to cut funding for the Prevention and Public Health Fund at the Centers for Disease Control and Prevention; cuts to other health agencies continued throughout 2018, with funds diverted to unrelated projects such as housing for detained immigrant children. end student sample text

annotated text Peer Review Comment: This paragraph has only one sentence, and it’s more like an example. It needs a topic sentence and more development. end annotated text

student sample text Three months later, Luciana Borio, director of medical and biodefense preparedness at the NSC, spoke at a symposium marking the centennial of the 1918 influenza pandemic. “The threat of pandemic flu is the number one health security concern,” she said. “Are we ready to respond? I fear the answer is no.” end student sample text

annotated text Peer Review Comment: This paragraph is very short and a lot like the previous paragraph in that it’s a single example. It needs a topic sentence. Maybe you can combine them? end annotated text

annotated text Peer Review Comment: Be sure to cite the quotation. end annotated text

Reading these comments and those of others, Trevor decided to combine the three short paragraphs into one paragraph focusing on the fact that the United States knew a pandemic was possible but was unprepared for it. He developed the paragraph, using the short paragraphs as evidence and connecting the sentences and evidence with transitional words and phrases. Finally, he added in-text citations in APA documentation style to credit his sources. The revised paragraph is below:

student sample text Epidemiologists and public health officials in the United States had long known that a global pandemic was possible. In 2016, the National Security Council (NSC) published Playbook for Early Response to High-Consequence Emerging Infectious Disease Threats and Biological Incidents , a 69-page document on responding to diseases spreading within and outside of the United States. On January 13, 2017, the joint transition teams of outgoing president Barack Obama and then president-elect Donald Trump performed a pandemic preparedness exercise based on the playbook; however, it was never adopted by the incoming administration (Goodman & Schulkin, 2020). A year later, in February 2018, the Trump administration began to cut funding for the Prevention and Public Health Fund at the Centers for Disease Control and Prevention, leaving key positions unfilled. Other individuals who were fired or resigned in 2018 were the homeland security adviser, whose portfolio included global pandemics; the director for medical and biodefense preparedness; and the top official in charge of a pandemic response. None of them were replaced, leaving the White House with no senior person who had experience in public health (Goodman & Schulkin, 2020). Experts voiced concerns, among them Luciana Borio, director of medical and biodefense preparedness at the NSC, who spoke at a symposium marking the centennial of the 1918 influenza pandemic in May 2018: “The threat of pandemic flu is the number one health security concern,” she said. “Are we ready to respond? I fear the answer is no” (Sun, 2018, final para.). end student sample text

A final word on working with reviewers’ comments: as you consider your readers’ suggestions, remember, too, that you remain the author. You are free to disregard suggestions that you think will not improve your writing. If you choose to disregard comments from your instructor, consider submitting a note explaining your reasons with the final draft of your report.

As an Amazon Associate we earn from qualifying purchases.

This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

Access for free at https://openstax.org/books/writing-guide/pages/1-unit-introduction

• Authors: Michelle Bachelor Robinson, Maria Jerskey, featuring Toby Fulwiler
• Publisher/website: OpenStax
• Book title: Writing Guide with Handbook
• Publication date: Dec 21, 2021
• Location: Houston, Texas
• Book URL: https://openstax.org/books/writing-guide/pages/1-unit-introduction
• Section URL: https://openstax.org/books/writing-guide/pages/8-5-writing-process-creating-an-analytical-report

© Dec 19, 2023 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.

• Privacy Policy

Buy Me a Coffee

Home » Critical Analysis – Types, Examples and Writing Guide

Critical Analysis – Types, Examples and Writing Guide

Table of Contents

Critical Analysis

Definition:

Critical analysis is a process of examining a piece of work or an idea in a systematic, objective, and analytical way. It involves breaking down complex ideas, concepts, or arguments into smaller, more manageable parts to understand them better.

Types of Critical Analysis

Types of Critical Analysis are as follows:

Literary Analysis

This type of analysis focuses on analyzing and interpreting works of literature , such as novels, poetry, plays, etc. The analysis involves examining the literary devices used in the work, such as symbolism, imagery, and metaphor, and how they contribute to the overall meaning of the work.

Film Analysis

This type of analysis involves examining and interpreting films, including their themes, cinematography, editing, and sound. Film analysis can also include evaluating the director’s style and how it contributes to the overall message of the film.

Art Analysis

This type of analysis involves examining and interpreting works of art , such as paintings, sculptures, and installations. The analysis involves examining the elements of the artwork, such as color, composition, and technique, and how they contribute to the overall meaning of the work.

Cultural Analysis

This type of analysis involves examining and interpreting cultural artifacts , such as advertisements, popular music, and social media posts. The analysis involves examining the cultural context of the artifact and how it reflects and shapes cultural values, beliefs, and norms.

Historical Analysis

This type of analysis involves examining and interpreting historical documents , such as diaries, letters, and government records. The analysis involves examining the historical context of the document and how it reflects the social, political, and cultural attitudes of the time.

Philosophical Analysis

This type of analysis involves examining and interpreting philosophical texts and ideas, such as the works of philosophers and their arguments. The analysis involves evaluating the logical consistency of the arguments and assessing the validity and soundness of the conclusions.

Scientific Analysis

This type of analysis involves examining and interpreting scientific research studies and their findings. The analysis involves evaluating the methods used in the study, the data collected, and the conclusions drawn, and assessing their reliability and validity.

Critical Discourse Analysis

This type of analysis involves examining and interpreting language use in social and political contexts. The analysis involves evaluating the power dynamics and social relationships conveyed through language use and how they shape discourse and social reality.

Comparative Analysis

This type of analysis involves examining and interpreting multiple texts or works of art and comparing them to each other. The analysis involves evaluating the similarities and differences between the texts and how they contribute to understanding the themes and meanings conveyed.

Critical Analysis Format

Critical Analysis Format is as follows:

I. Introduction

• Provide a brief overview of the text, object, or event being analyzed
• Explain the purpose of the analysis and its significance
• Provide background information on the context and relevant historical or cultural factors

II. Description

• Provide a detailed description of the text, object, or event being analyzed
• Identify key themes, ideas, and arguments presented
• Describe the author or creator’s style, tone, and use of language or visual elements

III. Analysis

• Analyze the text, object, or event using critical thinking skills
• Identify the main strengths and weaknesses of the argument or presentation
• Evaluate the reliability and validity of the evidence presented
• Assess any assumptions or biases that may be present in the text, object, or event
• Consider the implications of the argument or presentation for different audiences and contexts

IV. Evaluation

• Provide an overall evaluation of the text, object, or event based on the analysis
• Assess the effectiveness of the argument or presentation in achieving its intended purpose
• Identify any limitations or gaps in the argument or presentation
• Consider any alternative viewpoints or interpretations that could be presented
• Summarize the main points of the analysis and evaluation
• Reiterate the significance of the text, object, or event and its relevance to broader issues or debates
• Provide any recommendations for further research or future developments in the field.

VI. Example

• Provide an example or two to support your analysis and evaluation
• Use quotes or specific details from the text, object, or event to support your claims
• Analyze the example(s) using critical thinking skills and explain how they relate to your overall argument

VII. Conclusion

• Reiterate your thesis statement and summarize your main points
• Provide a final evaluation of the text, object, or event based on your analysis
• Offer recommendations for future research or further developments in the field
• End with a thought-provoking statement or question that encourages the reader to think more deeply about the topic

How to Write Critical Analysis

Writing a critical analysis involves evaluating and interpreting a text, such as a book, article, or film, and expressing your opinion about its quality and significance. Here are some steps you can follow to write a critical analysis:

• Read and re-read the text: Before you begin writing, make sure you have a good understanding of the text. Read it several times and take notes on the key points, themes, and arguments.
• Identify the author’s purpose and audience: Consider why the author wrote the text and who the intended audience is. This can help you evaluate whether the author achieved their goals and whether the text is effective in reaching its audience.
• Analyze the structure and style: Look at the organization of the text and the author’s writing style. Consider how these elements contribute to the overall meaning of the text.
• Evaluate the content : Analyze the author’s arguments, evidence, and conclusions. Consider whether they are logical, convincing, and supported by the evidence presented in the text.
• Consider the context: Think about the historical, cultural, and social context in which the text was written. This can help you understand the author’s perspective and the significance of the text.
• Develop your thesis statement : Based on your analysis, develop a clear and concise thesis statement that summarizes your overall evaluation of the text.
• Support your thesis: Use evidence from the text to support your thesis statement. This can include direct quotes, paraphrases, and examples from the text.
• Write the introduction, body, and conclusion : Organize your analysis into an introduction that provides context and presents your thesis, a body that presents your evidence and analysis, and a conclusion that summarizes your main points and restates your thesis.
• Revise and edit: After you have written your analysis, revise and edit it to ensure that your writing is clear, concise, and well-organized. Check for spelling and grammar errors, and make sure that your analysis is logically sound and supported by evidence.

When to Write Critical Analysis

You may want to write a critical analysis in the following situations:

• Academic Assignments: If you are a student, you may be assigned to write a critical analysis as a part of your coursework. This could include analyzing a piece of literature, a historical event, or a scientific paper.
• Journalism and Media: As a journalist or media person, you may need to write a critical analysis of current events, political speeches, or media coverage.
• Personal Interest: If you are interested in a particular topic, you may want to write a critical analysis to gain a deeper understanding of it. For example, you may want to analyze the themes and motifs in a novel or film that you enjoyed.
• Professional Development : Professionals such as writers, scholars, and researchers often write critical analyses to gain insights into their field of study or work.

Critical Analysis Example

An Example of Critical Analysis Could be as follow:

Research Topic:

The Impact of Online Learning on Student Performance

Introduction:

The introduction of the research topic is clear and provides an overview of the issue. However, it could benefit from providing more background information on the prevalence of online learning and its potential impact on student performance.

Literature Review:

The literature review is comprehensive and well-structured. It covers a broad range of studies that have examined the relationship between online learning and student performance. However, it could benefit from including more recent studies and providing a more critical analysis of the existing literature.

Research Methods:

The research methods are clearly described and appropriate for the research question. The study uses a quasi-experimental design to compare the performance of students who took an online course with those who took the same course in a traditional classroom setting. However, the study may benefit from using a randomized controlled trial design to reduce potential confounding factors.

The results are presented in a clear and concise manner. The study finds that students who took the online course performed similarly to those who took the traditional course. However, the study only measures performance on one course and may not be generalizable to other courses or contexts.

Discussion :

The discussion section provides a thorough analysis of the study’s findings. The authors acknowledge the limitations of the study and provide suggestions for future research. However, they could benefit from discussing potential mechanisms underlying the relationship between online learning and student performance.

Conclusion :

The conclusion summarizes the main findings of the study and provides some implications for future research and practice. However, it could benefit from providing more specific recommendations for implementing online learning programs in educational settings.

Purpose of Critical Analysis

There are several purposes of critical analysis, including:

• To identify and evaluate arguments : Critical analysis helps to identify the main arguments in a piece of writing or speech and evaluate their strengths and weaknesses. This enables the reader to form their own opinion and make informed decisions.
• To assess evidence : Critical analysis involves examining the evidence presented in a text or speech and evaluating its quality and relevance to the argument. This helps to determine the credibility of the claims being made.
• To recognize biases and assumptions : Critical analysis helps to identify any biases or assumptions that may be present in the argument, and evaluate how these affect the credibility of the argument.
• To develop critical thinking skills: Critical analysis helps to develop the ability to think critically, evaluate information objectively, and make reasoned judgments based on evidence.
• To improve communication skills: Critical analysis involves carefully reading and listening to information, evaluating it, and expressing one’s own opinion in a clear and concise manner. This helps to improve communication skills and the ability to express ideas effectively.

Importance of Critical Analysis

Here are some specific reasons why critical analysis is important:

• Helps to identify biases: Critical analysis helps individuals to recognize their own biases and assumptions, as well as the biases of others. By being aware of biases, individuals can better evaluate the credibility and reliability of information.
• Enhances problem-solving skills : Critical analysis encourages individuals to question assumptions and consider multiple perspectives, which can lead to creative problem-solving and innovation.
• Promotes better decision-making: By carefully evaluating evidence and arguments, critical analysis can help individuals make more informed and effective decisions.
• Facilitates understanding: Critical analysis helps individuals to understand complex issues and ideas by breaking them down into smaller parts and evaluating them separately.
• Fosters intellectual growth : Engaging in critical analysis challenges individuals to think deeply and critically, which can lead to intellectual growth and development.

Advantages of Critical Analysis

Some advantages of critical analysis include:

• Improved decision-making: Critical analysis helps individuals make informed decisions by evaluating all available information and considering various perspectives.
• Enhanced problem-solving skills : Critical analysis requires individuals to identify and analyze the root cause of a problem, which can help develop effective solutions.
• Increased creativity : Critical analysis encourages individuals to think outside the box and consider alternative solutions to problems, which can lead to more creative and innovative ideas.
• Improved communication : Critical analysis helps individuals communicate their ideas and opinions more effectively by providing logical and coherent arguments.
• Reduced bias: Critical analysis requires individuals to evaluate information objectively, which can help reduce personal biases and subjective opinions.
• Better understanding of complex issues : Critical analysis helps individuals to understand complex issues by breaking them down into smaller parts, examining each part and understanding how they fit together.
• Greater self-awareness: Critical analysis helps individuals to recognize their own biases, assumptions, and limitations, which can lead to personal growth and development.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Cluster Analysis – Types, Methods and Examples

Data Collection – Methods Types and Examples

Delimitations in Research – Types, Examples and...

Discriminant Analysis – Methods, Types and...

Research Process – Steps, Examples and Tips

Research Design – Types, Methods and Examples

• Entertainment

How To Write An Analytical Essay A Full Guide

Crafting an impeccable analytical essay is an art form that demands precision, insight, and a structured approach. Whether you’re delving into literature, dissecting historical events, or unraveling scientific theories, the ability to present a compelling analysis is pivotal. Here’s a comprehensive guide to navigate the intricate path of writing a flawless analytical essay.

What Is An Analytical Essay

An analytical essay is a type of academic writing that delves deeply into a topic, idea, or piece of literature. Unlike descriptive or narrative essays, which focus on providing a vivid description or telling a story, an analytical essay aims to examine and dissect its subject matter.

The primary objective of an analytical essay is to present a thorough analysis or interpretation of the subject, often breaking it down into its constituent parts and scrutinizing how they contribute to the whole.

Why Analytical Essay Is Important

Analytical essays play a pivotal role in developing critical thinking skills and fostering a deeper understanding of complex subjects. Through the meticulous examination and interpretation of information, these essays teach individuals how to dissect arguments, evaluate evidence, and form well-supported conclusions. They serve as a platform for honing analytical prowess, enabling individuals to engage with diverse perspectives, challenge assumptions, and articulate their insights effectively. Moreover, mastering the art of analytical essays equips individuals with invaluable skills applicable across various disciplines, fostering a capacity for logical reasoning, problem-solving, and persuasive communication—a skill set indispensable in academia, professional endeavors, and everyday life.

Tips For Writing A Good Analytical Essay

Understanding the essence.

To excel in analytical writing, one must comprehend the essence of analysis itself. It’s not merely about summarizing or narrating; it’s about deconstructing the core components, scrutinizing their significance, and synthesizing perspectives to derive insightful conclusions.

Devising a Strategic Blueprint

Begin with a comprehensive understanding of your subject matter. Formulate a thesis statement —a succinct encapsulation of your perspective—which serves as the guiding beacon throughout your essay. Craft an outline delineating key sections and their respective arguments, ensuring a logical flow that seamlessly connects each point.

The Pinnacle of Research

A sturdy analytical essay is built upon a foundation of rigorous research. Delve into reputable sources, be it scholarly articles, books, or credible online repositories. Gather diverse perspectives and data to fortify your arguments, but always uphold the standards of credibility and relevance.

Structure: The Backbone of Brilliance

A well-structured essay is akin to an architectural marvel. The introduction should entice readers with a gripping hook, provide context, and introduce the thesis statement. The body paragraphs, each beginning with a topic sentence, should expound on individual arguments supported by evidence and analysis. Finally, the conclusion should reaffirm the thesis while offering a nuanced synthesis of the essay’s core ideas.

The Art of Analysis

Here’s where the magic unfolds. Analyze, dissect, and interpret the data and evidence gathered. Scrutinize underlying themes, dissect intricate details, and juxtapose contrasting viewpoints. Employ analytical tools pertinent to your subject, such as literary devices for literature analyses, statistical methods for scientific inquiries, or historical frameworks for historical essays.

Precision in Language and Style

The language employed in an analytical essay should be precise, articulate, and tailored to convey complex ideas clearly. Utilize a formal tone, vary sentence structures, and employ transitions to ensure a seamless progression of ideas. Embrace clarity and coherence as your allies in elucidating intricate analyses.

Revisiting and Refining

Revision is the crucible wherein a good essay transforms into a great one. Review your work meticulously—check for coherence, refine arguments, ensure logical transitions, and verify the alignment of evidence with your thesis. Seek feedback from peers or mentors to gain diverse perspectives and refine your essay further.

Conclusion: A Culmination of Mastery

In conclusion, a perfect analytical essay isn’t merely a collection of facts and opinions; it’s an orchestrated symphony of critical thinking, analysis, and eloquent expression. Embrace the journey of discovery, relish the complexities, and let your essay resonate as a testament to your mastery of analytical prowess.

Best Place To Avail Analytical Essay Service

At Allessaywriter.com, excellence meets expertise in crafting exceptional analytical essay services . Our platform is your gateway to top-tier service, offering a seamless experience to elevate your academic journey. With a team of seasoned writers dedicated to precision and depth in analysis, we ensure tailored essays that reflect critical thinking and comprehensive understanding. Trust us for meticulous research, compelling arguments, and impeccable structure, all aimed at delivering the finest analytical essays that exceed expectations.

An analytical essay service encapsulates the culmination of rigorous analysis, insightful interpretation, and concise articulation. It serves as the pinnacle of intellectual prowess, combining critical thinking with eloquent expression to offer a profound understanding of complex subjects. So if you are still wondering about analytical essay writing then ask our writers and get our do my essay help services.

Meet Kathy, the mindful mind behind the words at minimalistfocus.com. With an innate ability to distill the essence of life down to its purest form, Kathy's writing resonates with those seeking clarity in a cluttered world.

Related Post

Finding your dream vacation rental: the ultimate guide for unforgettable getaways, why you should make videos with your podcasts, cybersecurity for seo professionals: safeguarding your digital assets, leave a reply cancel reply.

Save my name, email, and website in this browser for the next time I comment.

Type above and press Enter to search. Press Esc to cancel.

Baltimore bridge collapse: a bridge engineer explains what happened, and what needs to change

Associate Professor, Civil Engineering, Monash University

Disclosure statement

Colin Caprani receives funding from the Department of Transport (Victoria) and the Level Crossing Removal Project. He is also Chair of the Confidential Reporting Scheme for Safer Structures - Australasia, Chair of the Australian Regional Group of the Institution of Structural Engineers, and Australian National Delegate for the International Association for Bridge and Structural Engineering.

Monash University provides funding as a founding partner of The Conversation AU.

View all partners

When the container ship MV Dali, 300 metres long and massing around 100,000 tonnes, lost power and slammed into one of the support piers of the Francis Scott Key Bridge in Baltimore, the bridge collapsed in moments . Six people are presumed dead, several others injured, and the city and region are expecting a months-long logistical nightmare in the absence of a crucial transport link.

It was a shocking event, not only for the public but for bridge engineers like me. We work very hard to ensure bridges are safe, and overall the probability of being injured or worse in a bridge collapse remains even lower than the chance of being struck by lightning.

However, the images from Baltimore are a reminder that safety can’t be taken for granted. We need to remain vigilant.

So why did this bridge collapse? And, just as importantly, how might we make other bridges more safe against such collapse?

A 20th century bridge meets a 21st century ship

The Francis Scott Key Bridge was built through the mid 1970s and opened in 1977. The main structure over the navigation channel is a “continuous truss bridge” in three sections or spans.

The bridge rests on four supports, two of which sit each side of the navigable waterway. It is these two piers that are critical to protect against ship impacts.

And indeed, there were two layers of protection: a so-called “dolphin” structure made from concrete, and a fender. The dolphins are in the water about 100 metres upstream and downstream of the piers. They are intended to be sacrificed in the event of a wayward ship, absorbing its energy and being deformed in the process but keeping the ship from hitting the bridge itself.

The fender is the last layer of protection. It is a structure made of timber and reinforced concrete placed around the main piers. Again, it is intended to absorb the energy of any impact.

Fenders are not intended to absorb impacts from very large vessels . And so when the MV Dali, weighing more than 100,000 tonnes, made it past the protective dolphins, it was simply far too massive for the fender to withstand.

Read more: I've captained ships into tight ports like Baltimore, and this is how captains like me work with harbor pilots to avoid deadly collisions

Video recordings show a cloud of dust appearing just before the bridge collapsed, which may well have been the fender disintegrating as it was crushed by the ship.

Once the massive ship had made it past both the dolphin and the fender, the pier – one of the bridge’s four main supports – was simply incapable of resisting the impact. Given the size of the vessel and its likely speed of around 8 knots (15 kilometres per hour), the impact force would have been around 20,000 tonnes .

Bridges are getting safer

This was not the first time a ship hit the Francis Scott Bridge. There was another collision in 1980 , damaging a fender badly enough that it had to be replaced.

Around the world, 35 major bridge collapses resulting in fatalities were caused by collisions between 1960 and 2015, according to a 2018 report from the World Association for Waterborne Transport Infrastructure. Collisions between ships and bridges in the 1970s and early 1980s led to a significant improvement in the design rules for protecting bridges from impact.

Further impacts in the 1970s and early 1980s instigated significant improvements in the design rules for impact.

The International Association for Bridge and Structural Engineering’s Ship Collision with Bridges guide, published in 1993, and the American Association of State Highway and Transporation Officials’ Guide Specification and Commentary for Vessel Collision Design of Highway Bridges (1991) changed how bridges were designed.

In Australia, the Australian Standard for Bridge Design (published in 2017) requires designers to think about the biggest vessel likely to come along in the next 100 years, and what would happen if it were heading for any bridge pier at full speed. Designers need to consider the result of both head-on collisions and side-on, glancing blows. As a result, many newer bridges protect their piers with entire human-made islands.

Of course, these improvements came too late to influence the design of the Francis Scott Key Bridge itself.

Lessons from disaster

So what are the lessons apparent at this early stage?

First, it’s clear the protection measures in place for this bridge were not enough to handle this ship impact. Today’s cargo ships are much bigger than those of the 1970s, and it seems likely the Francis Scott Key Bridge was not designed with a collision like this in mind.

So one lesson is that we need to consider how the vessels near our bridges are changing. This means we cannot just accept the structure as it was built, but ensure the protection measures around our bridges are evolving alongside the ships around them.

Second, and more generally, we must remain vigilant in managing our bridges. I’ve written previously about the current level of safety of Australian bridges, but also about how we can do better.

This tragic event only emphasises the need to spend more on maintaining our ageing infrastructure. This is the only way to ensure it remains safe and functional for the demands we put on it today.

• Engineering
• Infrastructure
• Urban infrastructure
• container ships
• Baltimore bridge collapse

School of Social Sciences – Public Policy and International Relations opportunities

School of Social Sciences – Human Geography opportunities

School of Social Sciences – Criminology opportunities

School of Social Sciences – Academic appointment opportunities

Audience Development Coordinator (fixed-term maternity cover)

• Share full article

Advertisement

Supported by

G.O.P. Congressman’s Wild Claim: F.B.I. Entrapped Jan. 6 Rioters

More than three years after the attack on Congress, a Republican subcommittee chairman offered a series of baseless and disproved claims about it, reflecting an effort on the right to falsify what occurred.

By Luke Broadwater and Alan Feuer

Luke Broadwater reported from Washington, and Alan Feuer from New York.

Even by a conspiracy theorist’s standards, the wild claims made by Representative Clay Higgins, Republican of Louisiana, stand out.

The hard-right congressman, now in his fourth term in the House, has said that “ghost buses” took agents provocateurs to the Capitol on Jan. 6, 2021, to instigate the riot. He has claimed that the federal government is waging a “civil war” against Texas . And he has called the criminal charges against former President Donald J. Trump for mishandling classified documents a “ perimeter probe from the oppressors .”

But far from relegating Mr. Higgins to the fringe of their increasingly fractious conference, House Republicans have elevated him. They made him the chairman of the subcommittee overseeing border enforcement, and Speaker Mike Johnson named him one of 11 impeachment managers tasked with trying to remove the homeland security secretary from office in a Senate trial set to take place next week.

None of it has dampened Mr. Higgins’s penchant for spreading unsupported theories, many of which portray law enforcement and the government in an evil, conspiratorial light.

This week, in a lengthy podcast interview, he expounded at length on his belief — based, he said, on his own extensive investigation and evidence that only he has been able to see — that federal law enforcement officers entrapped Mr. Trump’s supporters into violently attacking the Capitol on Jan. 6. He was repeating a conspiracy theory that has been debunked repeatedly.

Over the course of a two-hour interview on the “Implicit Bias” podcast, Mr. Higgins, wearing a shirt emblazoned with the logo of the Three Percenters, a right-wing antigovernment militia, repeated the lie that the 2020 election was fraudulent. He laid out an outlandish story that tied the rise of the coronavirus pandemic to what he said was a plot by the government to infiltrate pro-Trump online forums and urge members to engage in “riotous” behavior, as he put it.

Finally, he said, also groundlessly, that federal agents posing as Trump supporters traveled to Washington on Jan. 6 and tricked Mr. Trump’s backers into carrying out mob violence.

It was the latest reminder that, more than three years after the attack, right-wing Republicans at every level continue to spread falsehoods about what happened on Jan. 6 and are now seeking to use those lies as a rallying cry to denounce the government, promote Mr. Trump’s candidacy and rile up his supporters.

Mr. Higgins claimed that he had conducted his own investigation into what happened that day. He predicted that once House Republicans finish posting security footage of the attack online, charges against Jan. 6 defendants would crumble.

“The whole thing,” Mr. Higgins said, “was a nefarious agenda to entrap MAGA Americans.”

Mr. Higgins, who before coming to Congress was accused of using unnecessary force when he was a police officer , later gained fame through a series of popular videos that earned him the nickname “Cajun John Wayne.”

Now he is the chairman of the House Homeland Security subcommittee on border enforcement. Neither Mr. Higgins nor Mr. Johnson responded to requests for comment about his podcast appearance.

The outspoken Louisianian has long trafficked in conspiracy theories, but the podcast interview that aired this week, which he promoted on social media, covered an unusually wide range of them.

For instance, Mr. Higgins said it was “very suspicious” that while votes in the presidential election were being counted, Joseph R. Biden Jr. overtook Mr. Trump in certain key states.

Describing the frustration of Trump supporters ahead of Jan. 6, he said, “We were witnessing the death of our republic.”

There is no evidence of widespread voter fraud in the 2020 election. That was a lie Mr. Trump told after he lost.

Mr. Higgins then laid out a long and convoluted theory that federal law enforcement embedded itself in various groups of Trump supporters around the country after the onset of the coronavirus pandemic — whose origins he said were suspicious on their own — riled them up and then encouraged them to go to the Capitol on Jan. 6.

“The original seeds of riotous or illegal or occupation behavior amongst these groups were planted by the F.B.I.-embedded agents in those groups,” Mr. Higgins claimed.

In fact, it was Mr. Trump who called for his supporters to amass in Washington on Jan. 6, telling them, “Be there, will be wild!”

No Jan. 6 defendant has successfully argued entrapment as a defense in court. Even a lawyer for the Proud Boys extremist group, whose members were charged with seditious conspiracy in connection with the attack, stood up in court last year and called that theory “slander.”

The fact that some F.B.I. informants were in the crowd of tens of thousands has long been known , but it does not suggest the federal government was behind the attack. If anything, it points to the opposite: that the F.B.I. failed at using the assets it had in extremist organizations to learn in advance that an attack might be coming.

Steven M. D’Antuono, the former leader of the F.B.I.’s Washington field office, testified before the House Judiciary Committee that he believed there may have been a “handful” of people who had previously served as informants for field offices who were in the crowd that day. But, he said, they had not been asked by the bureau to attend.

Most of the known informants in far-right groups like the Proud Boys were not, by their own accounts , recruited by the F.B.I. to reveal secrets about their own organizations. Instead, they have said they were approached by the bureau to share what they knew about leftist movements like antifa.

One of the F.B.I. informants in the crowd on Jan. 6 was James Ehren Knowles, a member of the Proud Boys Kansas City chapter. Right-wing politicians and pundits have sought to spin Mr. Knowles’s presence at the Capitol into a narrative suggesting that the bureau used covert operatives to instigate the riot, but he told a very different story under oath during the Proud Boys’ seditious conspiracy trial.

Mr. Knowles testified that he was not acting “at the direction of the F.B.I.” that day, but had joined the crowd as a member of the far-right group — or what a prosecutor described as “an independent human” making his own decisions.

Mr. Higgins said his claims of “ghost buses” came from a whistle-blower who said he saw two white tour buses at Union Station early in the morning on Jan. 6, which later disappeared. Tour buses carrying visitors to Washington are a nearly omnipresent sight in the area, especially on days when large events — such as Mr. Trump’s rally on the Ellipse on Jan. 6 — are planned.

“We don’t know what happened to them,” Mr. Higgins said ominously during the podcast. “I don’t run the F.B.I., man.”

Christopher A. Wray, the F.B.I. director, has said unequivocally that his agency had no role in causing the riot at the Capitol. “If you are asking whether the violence at the Capitol on Jan. 6 was part of some operation orchestrated by F.B.I. sources and/or agents,” Mr. Wray said, “the answer is emphatically no.”

As his podcast hosts sampled expensive whiskey, Mr. Higgins claimed that the unreleased Capitol surveillance footage would clear the Jan. 6 rioters, and he said he had access to inside information from their trials.

“Once this is all out, then these J6 prosecutions are going to start falling apart,” Mr. Higgins said. “And you’re going to see reversals of prosecution.”

But defendants and their lawyers would have access to the same information, including the Capitol security footage in the possession of House Republicans.

The Justice Department has charged more than 1,350 people in connection with the attack on the Capitol. The charges show a range of culpability. Some, including the leader of the Oath Keepers militia , have been convicted of seditious conspiracy and sentenced to lengthy prison terms. Others have been charged with merely trespassing and received no jail sentence.

The videos also show a range of approaches by police officers in different situations. Some fought a bloody battle to keep rioters from breaching the building; some tried to use persuasion to get people to leave the halls of Congress. Badly outnumbered, others are shown merely monitoring the crowd.

Six Capitol Police officers, out of a force of 2,000, were disciplined for their actions during the riot, including for unbecoming conduct and failure to comply with directives. But many more fought strenuously to keep the rioters out. About 150 police officers were injured during the assault.

Luke Broadwater covers Congress with a focus on congressional investigations. More about Luke Broadwater

Alan Feuer covers extremism and political violence for The Times, focusing on the criminal cases involving the Jan. 6 attack on the Capitol and against former President Donald J. Trump.  More about Alan Feuer

A Divided Congress: Latest News and Analysis

G.O.P. Congressman’s Wild Claim: More than three years after the attack on Congress, a Republican subcommittee chairman offered a series of baseless and disproved claims  about it, reflecting an effort on the right to falsify what occurred.

Plan for Ukraine Aid: Speaker Mike Johnson has begun laying out potential conditions for extending a fresh round of military assistance  to Ukraine, the strongest indication yet that he plans to push  through a package that many Republicans have tried to block.

Replacing Mitch McConnell: The intensifying battle for a new Senate Republican leader recalls an earlier era , when such races in Congress were crowded and sometimes messy affairs.

Spending Bill: A  bipartisan spending package  approved by Congress ended the prospect of a government shutdown. But the legislation also represented a major defeat for ultraconservatives in the House, who immediately turned on Johnson .

A Dwindling Majority: Representative Mike Gallagher, Republican of Wisconsin, announced that he would resign from Congress months earlier than expected on April 19, bringing the already minuscule G.O.P. majority down to a lonely one vote .

An Invite for Netanyahu: Johnson said that he planned to invite Prime Minister Benjamin Netanyahu of Israel to address Congress, moving to welcome a leader who has become a flashpoint for partisan disagreement  over the war in Gaza.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base
• How to write a literary analysis essay | A step-by-step guide

How to Write a Literary Analysis Essay | A Step-by-Step Guide

Published on January 30, 2020 by Jack Caulfield . Revised on August 14, 2023.

Literary analysis means closely studying a text, interpreting its meanings, and exploring why the author made certain choices. It can be applied to novels, short stories, plays, poems, or any other form of literary writing.

A literary analysis essay is not a rhetorical analysis , nor is it just a summary of the plot or a book review. Instead, it is a type of argumentative essay where you need to analyze elements such as the language, perspective, and structure of the text, and explain how the author uses literary devices to create effects and convey ideas.

Before beginning a literary analysis essay, it’s essential to carefully read the text and c ome up with a thesis statement to keep your essay focused. As you write, follow the standard structure of an academic essay :

• An introduction that tells the reader what your essay will focus on.
• A main body, divided into paragraphs , that builds an argument using evidence from the text.
• A conclusion that clearly states the main point that you have shown with your analysis.

Instantly correct all language mistakes in your text

Upload your document to correct all your mistakes in minutes

Table of contents

Step 1: reading the text and identifying literary devices, step 2: coming up with a thesis, step 3: writing a title and introduction, step 4: writing the body of the essay, step 5: writing a conclusion, other interesting articles.

The first step is to carefully read the text(s) and take initial notes. As you read, pay attention to the things that are most intriguing, surprising, or even confusing in the writing—these are things you can dig into in your analysis.

Your goal in literary analysis is not simply to explain the events described in the text, but to analyze the writing itself and discuss how the text works on a deeper level. Primarily, you’re looking out for literary devices —textual elements that writers use to convey meaning and create effects. If you’re comparing and contrasting multiple texts, you can also look for connections between different texts.

To get started with your analysis, there are several key areas that you can focus on. As you analyze each aspect of the text, try to think about how they all relate to each other. You can use highlights or notes to keep track of important passages and quotes.

Language choices

Consider what style of language the author uses. Are the sentences short and simple or more complex and poetic?

What word choices stand out as interesting or unusual? Are words used figuratively to mean something other than their literal definition? Figurative language includes things like metaphor (e.g. “her eyes were oceans”) and simile (e.g. “her eyes were like oceans”).

Also keep an eye out for imagery in the text—recurring images that create a certain atmosphere or symbolize something important. Remember that language is used in literary texts to say more than it means on the surface.

Narrative voice

Ask yourself:

• Who is telling the story?
• How are they telling it?

Is it a first-person narrator (“I”) who is personally involved in the story, or a third-person narrator who tells us about the characters from a distance?

Consider the narrator’s perspective . Is the narrator omniscient (where they know everything about all the characters and events), or do they only have partial knowledge? Are they an unreliable narrator who we are not supposed to take at face value? Authors often hint that their narrator might be giving us a distorted or dishonest version of events.

The tone of the text is also worth considering. Is the story intended to be comic, tragic, or something else? Are usually serious topics treated as funny, or vice versa ? Is the story realistic or fantastical (or somewhere in between)?

Consider how the text is structured, and how the structure relates to the story being told.

• Novels are often divided into chapters and parts.
• Poems are divided into lines, stanzas, and sometime cantos.
• Plays are divided into scenes and acts.

Think about why the author chose to divide the different parts of the text in the way they did.

There are also less formal structural elements to take into account. Does the story unfold in chronological order, or does it jump back and forth in time? Does it begin in medias res —in the middle of the action? Does the plot advance towards a clearly defined climax?

With poetry, consider how the rhyme and meter shape your understanding of the text and your impression of the tone. Try reading the poem aloud to get a sense of this.

In a play, you might consider how relationships between characters are built up through different scenes, and how the setting relates to the action. Watch out for  dramatic irony , where the audience knows some detail that the characters don’t, creating a double meaning in their words, thoughts, or actions.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Your thesis in a literary analysis essay is the point you want to make about the text. It’s the core argument that gives your essay direction and prevents it from just being a collection of random observations about a text.

If you’re given a prompt for your essay, your thesis must answer or relate to the prompt. For example:

Essay question example

Is Franz Kafka’s “Before the Law” a religious parable?

Your thesis statement should be an answer to this question—not a simple yes or no, but a statement of why this is or isn’t the case:

Thesis statement example

Franz Kafka’s “Before the Law” is not a religious parable, but a story about bureaucratic alienation.

Sometimes you’ll be given freedom to choose your own topic; in this case, you’ll have to come up with an original thesis. Consider what stood out to you in the text; ask yourself questions about the elements that interested you, and consider how you might answer them.

Your thesis should be something arguable—that is, something that you think is true about the text, but which is not a simple matter of fact. It must be complex enough to develop through evidence and arguments across the course of your essay.

Say you’re analyzing the novel Frankenstein . You could start by asking yourself:

Your initial answer might be a surface-level description:

The character Frankenstein is portrayed negatively in Mary Shelley’s Frankenstein .

However, this statement is too simple to be an interesting thesis. After reading the text and analyzing its narrative voice and structure, you can develop the answer into a more nuanced and arguable thesis statement:

Mary Shelley uses shifting narrative perspectives to portray Frankenstein in an increasingly negative light as the novel goes on. While he initially appears to be a naive but sympathetic idealist, after the creature’s narrative Frankenstein begins to resemble—even in his own telling—the thoughtlessly cruel figure the creature represents him as.

Remember that you can revise your thesis statement throughout the writing process , so it doesn’t need to be perfectly formulated at this stage. The aim is to keep you focused as you analyze the text.

Finding textual evidence

To support your thesis statement, your essay will build an argument using textual evidence —specific parts of the text that demonstrate your point. This evidence is quoted and analyzed throughout your essay to explain your argument to the reader.

It can be useful to comb through the text in search of relevant quotations before you start writing. You might not end up using everything you find, and you may have to return to the text for more evidence as you write, but collecting textual evidence from the beginning will help you to structure your arguments and assess whether they’re convincing.

To start your literary analysis paper, you’ll need two things: a good title, and an introduction.

Your title should clearly indicate what your analysis will focus on. It usually contains the name of the author and text(s) you’re analyzing. Keep it as concise and engaging as possible.

A common approach to the title is to use a relevant quote from the text, followed by a colon and then the rest of your title.

If you struggle to come up with a good title at first, don’t worry—this will be easier once you’ve begun writing the essay and have a better sense of your arguments.

“Fearful symmetry” : The violence of creation in William Blake’s “The Tyger”

The introduction

The essay introduction provides a quick overview of where your argument is going. It should include your thesis statement and a summary of the essay’s structure.

A typical structure for an introduction is to begin with a general statement about the text and author, using this to lead into your thesis statement. You might refer to a commonly held idea about the text and show how your thesis will contradict it, or zoom in on a particular device you intend to focus on.

Then you can end with a brief indication of what’s coming up in the main body of the essay. This is called signposting. It will be more elaborate in longer essays, but in a short five-paragraph essay structure, it shouldn’t be more than one sentence.

Mary Shelley’s Frankenstein is often read as a crude cautionary tale about the dangers of scientific advancement unrestrained by ethical considerations. In this reading, protagonist Victor Frankenstein is a stable representation of the callous ambition of modern science throughout the novel. This essay, however, argues that far from providing a stable image of the character, Shelley uses shifting narrative perspectives to portray Frankenstein in an increasingly negative light as the novel goes on. While he initially appears to be a naive but sympathetic idealist, after the creature’s narrative Frankenstein begins to resemble—even in his own telling—the thoughtlessly cruel figure the creature represents him as. This essay begins by exploring the positive portrayal of Frankenstein in the first volume, then moves on to the creature’s perception of him, and finally discusses the third volume’s narrative shift toward viewing Frankenstein as the creature views him.

Some students prefer to write the introduction later in the process, and it’s not a bad idea. After all, you’ll have a clearer idea of the overall shape of your arguments once you’ve begun writing them!

If you do write the introduction first, you should still return to it later to make sure it lines up with what you ended up writing, and edit as necessary.

The body of your essay is everything between the introduction and conclusion. It contains your arguments and the textual evidence that supports them.

Paragraph structure

A typical structure for a high school literary analysis essay consists of five paragraphs : the three paragraphs of the body, plus the introduction and conclusion.

Each paragraph in the main body should focus on one topic. In the five-paragraph model, try to divide your argument into three main areas of analysis, all linked to your thesis. Don’t try to include everything you can think of to say about the text—only analysis that drives your argument.

In longer essays, the same principle applies on a broader scale. For example, you might have two or three sections in your main body, each with multiple paragraphs. Within these sections, you still want to begin new paragraphs at logical moments—a turn in the argument or the introduction of a new idea.

Robert’s first encounter with Gil-Martin suggests something of his sinister power. Robert feels “a sort of invisible power that drew me towards him.” He identifies the moment of their meeting as “the beginning of a series of adventures which has puzzled myself, and will puzzle the world when I am no more in it” (p. 89). Gil-Martin’s “invisible power” seems to be at work even at this distance from the moment described; before continuing the story, Robert feels compelled to anticipate at length what readers will make of his narrative after his approaching death. With this interjection, Hogg emphasizes the fatal influence Gil-Martin exercises from his first appearance.

Topic sentences

To keep your points focused, it’s important to use a topic sentence at the beginning of each paragraph.

A good topic sentence allows a reader to see at a glance what the paragraph is about. It can introduce a new line of argument and connect or contrast it with the previous paragraph. Transition words like “however” or “moreover” are useful for creating smooth transitions:

… The story’s focus, therefore, is not upon the divine revelation that may be waiting beyond the door, but upon the mundane process of aging undergone by the man as he waits.

Nevertheless, the “radiance” that appears to stream from the door is typically treated as religious symbolism.

This topic sentence signals that the paragraph will address the question of religious symbolism, while the linking word “nevertheless” points out a contrast with the previous paragraph’s conclusion.

Using textual evidence

A key part of literary analysis is backing up your arguments with relevant evidence from the text. This involves introducing quotes from the text and explaining their significance to your point.

It’s important to contextualize quotes and explain why you’re using them; they should be properly introduced and analyzed, not treated as self-explanatory:

It isn’t always necessary to use a quote. Quoting is useful when you’re discussing the author’s language, but sometimes you’ll have to refer to plot points or structural elements that can’t be captured in a short quote.

In these cases, it’s more appropriate to paraphrase or summarize parts of the text—that is, to describe the relevant part in your own words:

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

The conclusion of your analysis shouldn’t introduce any new quotations or arguments. Instead, it’s about wrapping up the essay. Here, you summarize your key points and try to emphasize their significance to the reader.

A good way to approach this is to briefly summarize your key arguments, and then stress the conclusion they’ve led you to, highlighting the new perspective your thesis provides on the text as a whole:

If you want to know more about AI tools , college essays , or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

• Ad hominem fallacy
• Post hoc fallacy
• Appeal to authority fallacy
• False cause fallacy
• Sunk cost fallacy

College essays

• Choosing Essay Topic
• Write a College Essay
• Write a Diversity Essay
• College Essay Format & Structure
• Comparing and Contrasting in an Essay

 (AI) Tools

• Grammar Checker
• Paraphrasing Tool
• Text Summarizer
• AI Detector
• Plagiarism Checker
• Citation Generator

By tracing the depiction of Frankenstein through the novel’s three volumes, I have demonstrated how the narrative structure shifts our perception of the character. While the Frankenstein of the first volume is depicted as having innocent intentions, the second and third volumes—first in the creature’s accusatory voice, and then in his own voice—increasingly undermine him, causing him to appear alternately ridiculous and vindictive. Far from the one-dimensional villain he is often taken to be, the character of Frankenstein is compelling because of the dynamic narrative frame in which he is placed. In this frame, Frankenstein’s narrative self-presentation responds to the images of him we see from others’ perspectives. This conclusion sheds new light on the novel, foregrounding Shelley’s unique layering of narrative perspectives and its importance for the depiction of character.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Caulfield, J. (2023, August 14). How to Write a Literary Analysis Essay | A Step-by-Step Guide. Scribbr. Retrieved April 5, 2024, from https://www.scribbr.com/academic-essay/literary-analysis/

Is this article helpful?

Jack Caulfield

Other students also liked, how to write a thesis statement | 4 steps & examples, academic paragraph structure | step-by-step guide & examples, how to write a narrative essay | example & tips, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Pigments, dyes and inks: their analysis on manuscripts, scrolls and papyri

• Original Paper
• Published: 13 October 2021
• Volume 13 , article number  194 , ( 2021 )

Cite this article

• Lucia Burgio   ORCID: orcid.org/0000-0001-5436-1520 1 , 2  

1411 Accesses

25 Citations

Explore all metrics

This chapter provides an overview of some of the materials used to write and decorate different types of manuscripts, i.e. inks, pigments and dyes, and of the different scientific methods and techniques available for their analysis and characterisation. The target audience is not only made of scientists but also curators, conservators, students and practitioners who want to know more about the technical examination of manuscripts and wish to appreciate the context of the scientific analysis of this type of objects. The possible types of approach to the scientific investigation of manuscripts are discussed, and the most frequently used analytical techniques are grouped according to their level of invasiveness and destructiveness. The chapter also contains a quick guide to some of the main questions that may arise about manuscripts, and explains how scientists can help to address them. Finally, a general protocol for the scientific analysis of manuscripts is illustrated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

How to Check for Plagiarism?

Digitization, Digitalization, and Digital Transformation

Patchwriting, Plagiarism, Pedagogy: Definitions and Implications

https://www.smithsonianmag.com/history/ancient-egypt-shipping-mining-farming-economy-pyramids-180956619/ . Accessed 7 December 2020.

Please note that the earliest example of a papyrus roll is said to be the blank roll from the tomb of Hemaka at Saqqara, dating from the beginning of the third millennium BC, as reported on page 227 in Leach and Tait ( 2000 ).

https://colour-index.com/definitions-of-a-dye-and-a-pigment . Accessed 7 December 2020.

https://www.winsornewton.com/row/articles/colours/spotlight-on-colourants-dyes-pigments/ . Accessed 7 December 2020.

http://cameo.mfa.org/wiki/Carbon_black . Accessed 7 December 2020.

http://cameo.mfa.org/wiki/Bone_black . Accessed 7 December 2020.

http://cameo.mfa.org/wiki/Ivory_black . Accessed 7 December 2020.

http://cameo.mfa.org/wiki/Iron_gall_ink . Accessed 7 December 2020.

http://cameo.mfa.org/wiki/Berberis_dye . Accessed 7 December 2020.

Hark RR, personal communication, 2020.

http://licensedigitome.weebly.com/ . Accessed 7 December 2020.

Abel A (2012) The history of dyes and pigments: from natural dyes to high performance pigments. In:&nbsp;Colour Design. Woodhead Publishing, pp 557–587. https://doi.org/10.1016/B978-0-08-101270-3.00024-2

Aceto M (2021) The palette of organic colourants in wall paintings. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01392-3

Article   Google Scholar  

Aceto M, Calà E (2017) Analytical evidences of the use of iron-gall ink as a pigment on miniature paintings. Spectrochim Acta Part A Mol Biomol Spectrosc 187:1–8. https://doi.org/10.1016/j.saa.2017.06.017

Aceto M, Agostino A, Boccaleri E, Garlanda AC (2008) The Vercelli Gospels laid open: an investigation into the inks used to write the oldest Gospels in Latin. X-Ray Spectrometry Int J 37(4):286–292. https://doi.org/10.1002/xrs.1047

Aceto M, Agostino A, Fenoglio G, Gulmini M, Bianco V, Pellizzi E (2012) Non invasive analysis of miniature paintings: proposal for an analytical protocol. Spectrochim Acta Part A Mol Biomol Spectrosc 91:352–359. https://doi.org/10.1016/j.saa.2012.02.021

Aceto M, Agostino A, Fenoglio G, Idone A, Gulmini M et al (2014) Characterisation of colourants on illuminated manuscripts by portable fibre optic UV-visible-NIR reflectance spectrophotometry. Anal Methods 6(5):1488–1500. https://doi.org/10.1039/C3AY41904E

Aceto M, Agostino A, Fenoglio G, Idone A, Crivello F et al (2017) Analytical investigations on the Coronation Gospels manuscript. Spectrochim Acta Part A Mol Biomol Spectrosc 171:213–221. https://doi.org/10.1016/j.saa.2016.07.050

Analytical Methods Committee AMCTB No. 67 (2015) Raman spectroscopy in cultural heritage: Background paper. Anal Methods7: 4844–4847. https://doi.org/10.1039/C5AY90036K . Open access online: https://pubs.rsc.org/az/content/articlepdf/2015/ay/c5ay90036k

Analytical Methods Committee, AMCTB No. 101 (2021) Analysis of historical dyes on heritage objects. Anal Methods. https://doi.org/10.1039/D0AY90167A . Open access online https://pubs.rsc.org/az/content/articlepdf/2021/ay/d0ay90167a

Analytical Methods Committee AMCTB No. 75 (2016) UV-visible-NIR reflectance spectrophotometry in cultural heritage: Background paper. Anal Methods 8: 5894–5896. https://doi.org/10.1039/c6ay90112c . Open access online: https://pubs.rsc.org/en/content/articlepdf/2016/ay/c6ay90112c

Analytical Methods Committee AMCTB No. 80 (2017) Surface-enhanced Raman spectroscopy (SERS) in cultural heritage. Anal Methods 9(30): 4338–4340. https://doi.org/10.1039/C7AY90097J . Open access online: https://www.rsc.org/images/TB_80_tcm18-249824.pdf

Analytical Methods Committee AMCTB No. 91 (2019) Laser-induced breakdown spectroscopy (LIBS) in cultural heritage. Anal Methods 11: 5833–5836. https://doi.org/10.1039/C9AY90147G . Open access online: https://pubs.rsc.org/en/content/articlepdf/2019/ay/c9ay90147g

Analytical Methods Committee, AMCTB No. 98 (2020) X-ray micro computed tomography in cultural heritage. Anal Methods 12(36): 4496–4500. https://doi.org/10.1039/D0AY90112A . Open access online https://pubs.rsc.org/az/content/articlepdf/2020/ay/d0ay90112a

Arizzi A, Cultrone G (2021) Mortars and plasters – How to characterise hydraulic mortars. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01404-2

Aru M, Burgio L, Rumsey MS (2014) Mineral impurities in azurite pigments: artistic or natural selection? J Raman Spectrosc 45(11–12):1013–1018. https://doi.org/10.1002/jrs.4469

Bailey K (2012) A note on Prussian blue in nineteenth-century Canton. Stud Conserv 57(2):116–121. https://doi.org/10.1179/2047058412Y.0000000002

Becker H (2021) Pigment nomenclature in the ancient Near East, Greece, and Rome. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01394-1

Berrie BH (1997) Prussian Blue. In:&nbsp;West FitzHugh E (ed) Artists’ Pigments. A Handbook of their History and Characteristics volume 3. National Gallery of Art, Washington. ISBN&nbsp;0894682563. Open access online: https://www.nga.gov/content/dam/ngaweb/research/publications/pdfs/artists-pigments-vol3.pdf

Berrie BH, editor (2007) Artists' Pigments: Volume 4: A Handbook of their History and Characteristics. Archetype Publications, London. ISBN: 9781904982234

Berrie BH, Matthew LC (2005) Material innovation and artistic invention: new materials and new colors in Renaissance Venetian paintings. In:&nbsp;Scientific Examination of Art: Modern Techniques in Conservation and Analysis. Washington: National Academy Press. Open access online: https://www.nap.edu/read/11413/chapter/3

Bersani D, Conti C, Matousek P, Pozzi F, Vandenabeele P (2016) Methodological evolutions of Raman spectroscopy in art and archaeology. Anal Methods 8(48):8395–8409. https://doi.org/10.1039/C6AY02327D

Brunetti B, Miliani C, Sgamellotti A (2016) MOLAB growth, aims and results. Tech Sci Service De L’histoire De L’art Et De La Préservation Des Biens Culturels 43:32–40. https://doi.org/10.4000/techne.592

Budnar M, Uršič M, Simčič J, Pelicon P, Kolar J, Šelih VS, Strlič M (2006) Analysis of iron gall inks by PIXE. Nucl Instrum Methods Phys Res Sect B 243(2):407–416. https://doi.org/10.1016/j.nimb.2005.10.013

Burgio L (2000) Analysis of pigments on art objects by Raman microscopy and other techniques. Doctoral dissertation, University College London

Burgio L (2010) Victoria and Albert Museum - internal scientific analysis report 10–160-LB

Burgio L (2011) Analysis of Pigments on Manuscripts by Raman Spectroscopy: Advantages and Limitations. In: Neate S, Howell D, Ovenden R, Pollard AM (eds) The technological study of books and manuscripts as artefacts: research questions and analytical solutions. Archeopress, pp 67–76. ISBN 978 1 4073 0767 1

Burgio L (2012) Study of Manuscripts by Vibrational Spectroscopy. In: Edwards HGM, Chalmers JM (eds) Infrared and Raman Spectroscopy in Forensic Science. Wiley, pp 409–417

Burgio L (2017a) James Leman’s glorious palette – part 3, https://www.vam.ac.uk/blog/caring-for-our-collections/james-lemans-glorious-palette-part-3 . Accessed 7 Dec 2020

Burgio L (2017b) A prayer book fit for a king. https://www.vam.ac.uk/blog/caring-for-our-collections/a-prayer-book-fit-for-a-king . Accessed 7 Dec 2020

Burgio L (2018) Dye analysis of the psychedelic Leman silk. https://www.vam.ac.uk/blog/caring-for-our-collections/dye-analysis-of-the-psychedelic-leman-silk . Accessed 7 Dec 2020

Burgio L (2019) Nonsuch Palace: a Tudor Grand Design? https://www.vam.ac.uk/blog/museum-life/nonsuch-palace-a-tudor-grand-design . Accessed 7 Dec 2020

Burgio L, Clark RJH (2000) Comparative pigment analysis of six modern Egyptian papyri and an authentic one of the 13th century BC by Raman microscopy and other techniques. J Raman Spectrosc 31(5):395–401. https://doi.org/10.1002/1097-4555(200005)31:5%3c395::AID-JRS527%3e3.0.CO;2-E

Burgio L, Clark RJH (2001) Library of FT-Raman spectra of pigments, minerals, pigment media and varnishes, and supplement to existing library of Raman spectra of pigments with visible excitation. Spectrochim Acta Part A Mol Biomol Spectrosc 57(7):1491–1521. https://doi.org/10.1016/S1386-1425(00)00495-9

Burgio L,&nbsp;Clark RJH,&nbsp;Hark RR (2010) Raman microscopy and x-ray fluorescence analysis of pigments on medieval and Renaissance Italian manuscript cuttings, PNAS 107&nbsp;(13):, 5726–5731. https://doi.org/10.1073/pnas.0914797107 . Open access online: https://www.pnas.org/content/107/13/5726

Burgio L, Bioletti S, Mehan B (2013) Non-destructive, in situ analysis of three early medieval manuscripts from Trinity College Library Dublin (Codex Usserianus Primus, Book of Durrow, Book of Armagh). In: Making Histories: Proceedings of the Sixth International Conference on Insular Art, York 2011

Burgio L, Clark RJH, Hark RR, Rumsey MS, Zannini C (2009a) Spectroscopic investigations of Bourdichon miniatures: masterpieces of light and color. Appl Spectrosc 63(6):611–620. https://doi.org/10.1366/000370209788559593

Burgio L, Clark RJH, Hark RR (2009b) Spectroscopic investigation of modern pigments on purportedly medieval miniatures by the ‘Spanish Forger.’ J Raman Spectrosc 40(12):2031–2036. https://doi.org/10.1002/jrs.2364

Calà E, Agostino A, Fenoglio G, Capra V, Porticelli F, Manzari F et al (2019) The Messale Rosselli: Scientific investigation on an outstanding 14th century illuminated manuscript from Avignon. J Archaeol Sci Rep 23:721–730. https://doi.org/10.1016/j.jasrep.2018.12.001

Cameo materials database: http://cameo.mfa.org/wiki/Category:Materials_database . Accessed 7 Dec 2020

Cañamares MV, Reagan DA, Lombardi JR, Leona M (2014) TLC-SERS of mauve, the first synthetic dye. J Raman Spectrosc 45(11–12):1147–1152. https://doi.org/10.1002/jrs.4508

Cardon D (2007) Natural dyes. Archetype Publications, London

Google Scholar  

Caroselli M, Ruffolo SA, Piqué F (2021) Mortars and plasters – How to manage mortars and plasters conservation. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01409-x

Castro R, Pozzi F, Leona M, Melo MJ (2014) Combining SERS and microspectrofluorimetry with historically accurate reconstructions for the characterization of lac dye paints in medieval manuscript illuminations. J Raman Spectrosc 45(11–12):1172–1179. https://doi.org/10.1002/jrs.4608

Cavallo G, Riccardi MP (2021) Glass-based pigments in painting. Archaeol Anthropol Sci (forthcoming)

Cesaratto A, Centeno SA, Lombardi JR, Shibayama N, Leona M (2017) A complete Raman study of common acid red dyes: application to the identification of artistic materials in polychrome prints. J Raman Spectrosc 48(4):601–609. https://doi.org/10.1002/jrs.5082

Chaplin TD, Clark RJH, Jacobs D, Jensen K, Smith GD (2005) The Gutenberg Bibles: analysis of the illuminations and inks using Raman spectroscopy. Anal Chem 77(11):3611–3622. https://doi.org/10.1021/ac050346y

Christiansen T, Buti D, Dalby KN, Lindelof PE, Ryholt K, Vila A (2017) Chemical characterization of black and red inks inscribed on ancient Egyptian papyri: the Tebtunis temple library. J Archaeol Sci Rep 14:208–219. https://doi.org/10.1016/j.jasrep.2017.05.042

Clark RJH (2005) Raman microscopy in the identification of pigments on manuscripts and other artwork. The Scientific Examination of Art. National Academies of Science, Washington, pp 162–185

Clarke M (2001) The analysis of medieval European manuscripts. Stud Conserv 46(sup1):3–17. https://doi.org/10.1179/sic.2001.46.Supplement-1.3

Corregidor V, Viegas R, Ferreira LM, Alves LC (2019) Study of Iron Gall Inks, Ingredients and Paper Composition Using Non-Destructive Techniques. Heritage 2(4):2691–2703. https://doi.org/10.3390/heritage2040166

Cotte M, Genty-Vincent A, Janssens K, Susini J (2018) Applications of synchrotron X-ray nano-probes in the field of cultural heritage. C R Phys 19(7):575–588. https://doi.org/10.1016/j.crhy.2018.07.002

Coupry C, Sagon G, Gorguet-Ballesteros P (1997) Raman spectroscopic investigation of blue contemporary textiles. J Raman Spectrosc 28(2–3):85–89. https://doi.org/10.1002/(SICI)1097-4555(199702)28:2/3%3c85::AID-JRS64%3e3.0.CO;2-4

Craddock P (2009) Scientific investigation of copies, fakes and forgeries. Elsevier

Book   Google Scholar  

Crippa M, Legnaioli S, Kimbriel C, Ricciardi P (2020) New evidence for the intentional use of calomel as a white pigment. J Raman Spectrosc. https://doi.org/10.1002/jrs.5876

Cucci C, Delaney JK, Picollo M (2016) Reflectance hyperspectral imaging for investigation of works of art: old master paintings and illuminated manuscripts. Acc Chem Res 49(10):2070–2079. https://doi.org/10.1021/acs.accsounts.6b00048

Del Carmine P, Giuntini L, Hooper W, Lucarelli F, Mandò PA (1996) Further results from PIXE analysis of inks in Galileo’s notes on motion. Nucl Inst Methods Phys Res B 113:354–358

DeLaine J (2021) Production, transport and on-site organisation of Roman mortars and plasters. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01401-5

Delaney JK, Ricciardi P, Glinsman LD, Facini M, Thoury M, Palmer M, de la Rie ER (2014) Use of imaging spectroscopy, fiber optic reflectance spectroscopy, and X-ray fluorescence to map and identify pigments in illuminated manuscripts. Stud Conserv 59(2):91–101. https://doi.org/10.1179/2047058412Y.0000000078

Delbey T, Holck JP, Jørgensen B, Alvis A, Smith VH, Kavich GM et al (2019) Poisonous books: analyses of four sixteenth and seventeenth century book bindings covered with arsenic rich green paint. Heritage Science 7(1):91. https://doi.org/10.1186/s40494-019-0334-2

Di Stefano LM, Fuchs R (2011) Characterisation of the pigments in a Ptolemaic Egyptian Book of the Dead papyrus. Archaeol Anthropol Sci 3(3):229. https://doi.org/10.1007/s12520-011-0054-3

Doherty B, Brunetti BG, Sgamellotti A, Miliani C (2011) A detachable SERS active cellulose film: a minimally invasive approach to the study of painting lakes. J Raman Spectrosc 42(11):1932–1938. https://doi.org/10.1002/jrs.2942

Doherty B, Daveri A, Clementi C, Romani A, Bioletti S, Brunetti B et al (2013) The Book of Kells: a non-invasive MOLAB investigation by complementary spectroscopic techniques. Spectrochim Acta Part A Mol Biomol Spectrosc 115:330–336. https://doi.org/10.1016/j.saa.2013.06.020

Doherty B, Presciutti F, Sgamellotti A, Brunetti BG, Miliani C (2014) Monitoring of optimized SERS active gel substrates for painting and paper substrates by unilateral NMR profilometry. J Raman Spectrosc 45(11–12):1153–1159. https://doi.org/10.1002/jrs.4542

Domingo Sanz I, Chieli A (2021) Characterising the pigments and paints of prehistoric artists. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01397-y

Dooley KA, Lomax S, Zeibel JG, Miliani C, Ricciardi P, Hoenigswald A et al (2013) Mapping of egg yolk and animal skin glue paint binders in Early Renaissance paintings using near infrared reflectance imaging spectroscopy. Analyst 138(17):4838–4848. https://doi.org/10.1039/C3AN00926B

Douma M (2008) Pigments through the Ages.&nbsp; Institute for Dynamic Educational Development (IDEA). Open access online: www.webexhibits.org/pigments . Accessed 7 Dec 2020

Duran A, Perez-Rodriguez JL, Espejo T, Franquelo ML, Castaing J, Walter P (2009) Characterization of illuminated manuscripts by laboratory-made portable XRD and micro-XRD systems. Anal Bioanal Chem 395(7):1997–2004. https://doi.org/10.1007/s00216-009-2992-5

Durán A, Franquelo ML, Centeno MA, Espejo T, Perez-Rodriguez JL (2011) Forgery detection on an Arabic illuminated manuscript by micro-Raman and X-ray fluorescence spectroscopy. J Raman Spectrosc 42(1):48–55. https://doi.org/10.1002/jrs.2644

Duran A, López-Montes A, Castaing J, Espejo T (2014) Analysis of a royal 15th century illuminated parchment using a portable XRF–XRD system and micro-invasive techniques. J Archaeol Sci 45:52–58. https://doi.org/10.1016/j.jas.2014.02.011

Eastaugh N, Walsh V, Chaplin T, Siddall R (2007)&nbsp; Pigment compendium: a dictionary of historical pigments . Routledge. Free pdf available on https://www.researchgate.net/publication/273692562_Pigment_Compendium_A_Dictionary_and_Optical_Microscopy_of_Historic_Pigments

Edwards HGM (2018) Analytical Raman Spectroscopy of Inks. In&nbsp;Raman Spectroscopy in Archaeology and Art History: Volume 2, 1–15. https://doi.org/10.1039/9781788013475-00001

Ergenç D, Fort R, Varas−Muriel MJ, Alvarez de Buergo M, (2021) Mortars and plasters – How to characterise aerial mortars and plasters. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01398-x

Feller R, editor (1986) Artists' Pigments: Volume 1: A Handbook of their History and Characteristics. National Gallery of Art, Washington. Open access online: https://www.nga.gov/content/dam/ngaweb/research/publications/pdfs/artists-pigments-vol1.pdf

Fiddyment S, Holsinger B, Ruzzier C, Devine A, Binois A, Albarella U et al (2015) Animal origin of 13th-century uterine vellum revealed using noninvasive peptide fingerprinting. Proc Natl Acad Sci 112(49):15066–15071. https://doi.org/10.1073/pnas.1512264112

Fischer C, Kakoulli I (2006) Multispectral and hyperspectral imaging technologies in conservation: current research and potential applications. Stud Conserv 51(sup1):3–16. https://doi.org/10.1179/sic.2006.51.Supplement-1.3

Flieder F (1968) Mise au point des techniques d’identification des pigments et des liants inclus dans la couche picturale des enluminures de manuscrits. Stud Conserv 13(2):49–86. https://doi.org/10.1179/sic.1968.005

Ford B (2018) A microfading survey of the lightfastness of blue, black and red ballpoint pen inks in ambient and modified environments. AICCM Bull 39(1):26–34. https://doi.org/10.1080/10344233.2018.1515809

Gambardella AA, Schmidt Patterson CM, Webb SM, Walton MS (2016) Sulfur K-edge XANES of lazurite: Toward determining the provenance of lapis lazuli. Microchem J 125:299–307. https://doi.org/10.1016/j.microc.2015.11.030

Gettens RJ, Kuhn H, Chase WT (1993a) Lead White. In Roy A (ed)&nbsp;Artists’ Pigments: A Handbook of their History and Characteristics. Volume 2. National Gallery of Art, Washington. Open access online: https://www.nga.gov/content/dam/ngaweb/research/publications/pdfs/artists-pigments-vol1.pdf

Gettens RJ, Feller R, Chase W (1993b) Vermilion and Cinnabar. In Roy A (ed)&nbsp;Artists’ Pigments: A Handbook of their History and Characteristics. Volume 2. National Gallery of Art, Washington. Open access online: https://www.nga.gov/content/dam/ngaweb/research/publications/pdfs/artists-pigments-vol1.pdf

Gliozzo E (2021) Pigments – Mercury-based red (cinnabar-vermilion) and white (calomel) and their degradation products. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01402-4

Gliozzo E, Burgio L (2021) Pigments – Arsenic-based yellows and reds. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01431-z

Gliozzo E, Ionescu C (2021) Pigments – Lead-based whites, reds, yellows and oranges and their alteration phases. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01407-z

Gliozzo E, Pizzo A, La Russa MF (2021) Mortars, plasters and pigments - Research questions and sampling criteria. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01393-2

Goler S, Hagadorn A, Ratzan DM, Bagnall R, Cacciola A, McInerney J, Yardley JT (2019) Using Raman spectroscopy to estimate the dates of carbon-based inks from Ancient Egypt. J Cult Herit 38:106–117. https://doi.org/10.1016/j.culher.2018.12.003

Grazia C, Buti D, Cartechini L, Rosi F, Gabrieli F, Lladó-Buisán VM et al (2018) Exploring the materiality of Mesoamerican manuscripts by non-invasive spectroscopic methods: Codex Laud, Bodley, Selden, Mendoza and Selden Roll at the Bodleian Library. In: Mesoamerican Manuscripts. Brill, pp 134–159. https://doi.org/10.1163/9789004388116_007

Hahn O (2010) Analyses of iron gall and carbon inks by means of X-ray fluorescence analysis: a non-destructive approach in the field of archaeometry and conservation science. Restaurator Int J Preserv Library Arch Mater 31(1):41–64. https://doi.org/10.1515/rest.2010.003

Harley RD (1982) Artists' pigments, c. 1600–1835; a study in English documentary sources. Butterworth Scientific, London

Hidalgo RJD, Córdoba R, Nabais P, Silva V, Melo MJ, Pina F et al (2018) New insights into iron-gall inks through the use of historically accurate reconstructions. Heritage Sci 6(1):63. https://doi.org/10.1186/s40494-018-0228-8

Houston K (2011) Spectroscopic Analysis of Pigments on Portrait Miniatures by William Wood (1769-1810). Undergraduate dissertation, Juniata College, Pennsylvania

Kimbriel C,&nbsp;Ricciardi P&nbsp;(2020)&nbsp;Secrets of a Silent Miniaturist: Findings from a Technical Study of Miniatures Attributed to Isaac Oliver.&nbsp;British Art Studies,&nbsp;(17). https://doi.org/10.17658/issn.2058-5462/issue-17/kimbrielricciardi . Open access online https://doi.org/10.17658/issn.2058-5462/issue-17/kimbrielricciardi

Knapp CW, Christidis GE, Venieri D, Gounaki I, Gibney-Vamvakari J, Stillings M, Photos-Jones E (2021) The ecology and bioactivity of some Greco-Roman medicinal minerals: the case of Melos earth pigments. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01396-z

Kogou S, Lucian A, Bellesia S, Burgio L, Bailey K, Brooks C, Liang H (2015) A holistic multimodal approach to the non-invasive analysis of watercolour paintings. Appl Phys A 121(3):999–1014. https://doi.org/10.1007/s00339-015-9425-4

Kühn H (1986) Zinc white. In: Roy A (ed) Artists’ Pigments: A Handbook of their History and Characteristics. Volume 2. National Gallery of Art, Washington. Open access online: https://www.nga.gov/content/dam/ngaweb/research/publications/pdfs/artists-pigments-vol1.pdf

Kühn H (1993) Verdigris and Copper Resinate.&nbsp;In: Roy A (ed) Artists’ Pigments: A Handbook of their History and Characteristics. Volume 2. National Gallery of Art, Washington. Open access online: https://www.nga.gov/research/publications/pdf-library/artists-pigments-vol2.html

La Russa MF, Ruffolo SA (2021) Mortars and plasters - How to characterise mortars and plasters degradation. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01405-1

Lancaster LC (2021) Mortars and plasters – How mortars were made. The Literary Sources. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01395-0

Leach B, Tait WJ (2000) Papyrus. In: Nicholson PT, Shaw I (eds) Ancient Egyptian Materials and Technology. University Press, Cambridge, pp 227–253

Lee AS, Mahon PJ, Creagh DC (2006) Raman analysis of iron gall inks on parchment. Vib Spectrosc 41(2):170–175. https://doi.org/10.1016/j.vibspec.2005.11.006

Lee AS, Otieno-Alego V, Creagh DC (2008) Identification of iron-gall inks with near-infrared Raman microspectroscopy. J Raman Spectrosc 39(8):1079–1084. https://doi.org/10.1002/jrs.1989

Leona M, Winter (2001) Fiber optics reflectance spectroscopy: a unique tool for the investigation of Japanese paintings. Stud Conserv 46(3):153–162. https://doi.org/10.1179/sic.2001.46.3.153

Liang H (2011) Advanced optical imaging methods for investigating manuscripts. In: Neate S, Howell D, Ovenden R, Pollard AM (eds) The technological study of books and manuscripts as artefacts: research questions and analytical solutions. Archaeopress, Oxford, pp 55–66 ( ISBN 9781407307671 )

Liang H, Burgio L, Bailey K, Lucian A, Dilley C, Bellesia S, Cheung C, Brooks C (2014) Culture and trade through the prism of technical art history: A study of Chinese export paintings. Stud Conserv 59(sup1):S96–S99. https://doi.org/10.1179/204705814X13975704318272

Lucas A (1962) Ancient Egyptian Materials and Industries, 4th ed. Revised by J.R. Harris. London: Edward Arnold

Manca R, Burgio L, Button V et al (2019) Scientific analysis underpinning the multidisciplinary project “The Leman Album: an Enhanced Facsimile.” Eur Phys J plus 134:267. https://doi.org/10.1140/epjp/i2019-12668-0

Mastrotheodoros GP, Beltsios KG, Bassiakos Y (2021) Pigments – Iron-based red, yellow and brown ochres. Archaeol Anthropol Sci (forthcoming)

McNeil RJ (1984) Scanning Auger microscopy for dating of manuscript inks. In: Lambert JB (ed) Archaeological Chemistry III. American Chemical Society, Washington, pp 255–269

Chapter   Google Scholar  

Melessanaki K, Papadakis V, Balas C, Anglos D (2001) Laser induced breakdown spectroscopy and hyper-spectral imaging analysis of pigments on an illuminated manuscript. Spectrochim Acta Part B 56(12):2337–2346. https://doi.org/10.1016/S0584-8547(01)00302-0

Melo MJ (2009) History of natural dyes in the ancient mediterranean world. In:&nbsp;Bechtold T, Mussak R (eds) Handbook of natural colorants. Wiley, pp 3–20

Melo MJ, Nabais P, Guimarães M, Araújo R, Castro R, Oliveira MC, Whitworth I (2016) Organic dyes in illuminated manuscripts: a unique cultural and historic record. Philos Trans R Soc A Math Phys Eng Sci 374(2082): 20160050. https://doi.org/10.1098/rsta.2016.0050 . Available on line: https://royalsocietypublishing.org/doi/pdf/ https://doi.org/10.1098/rsta.2016.0050

Melo MJ, Nabais P, Araújo R, Vitorino T (2019) The conservation of medieval manuscript illuminations: A chemical perspective.&nbsp; Phys Sci Rev ,&nbsp; 4 (8). https://doi.org/10.1515/psr-2018-0017

Miliani C, Rosi F, Brunetti BG, Sgamellotti A (2010) In situ noninvasive study of artworks: the MOLAB multitechnique approach. Acc Chem Res 43(6):728–738. https://doi.org/10.1021/ar100010t

Miliani C, Rosi F, Daveri A, Brunetti BG (2012) Reflection infrared spectroscopy for the non-invasive in situ study of artists’ pigments. Appl Phys A 106(2):295–307. https://doi.org/10.1007/s00339-011-6708-2

Mosca S, Alberti R, Frizzi T, Nevin A, Valentini G, Comelli D (2016) A whole spectroscopic mapping approach for studying the spatial distribution of pigments in paintings. Appl Phys A 122(9):815. https://doi.org/10.1007/s00339-016-0345-8

Murat Z (2021) Wall paintings through the ages. The medieval period (Italy, 12th-15th century). Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01410-4

Mühlethaler B, Thyssen J (1993) Smalt. In: Roy A (ed) Artists’ Pigments: A Handbook of their History and Characteristics. Volume 2. National Gallery of Art, Washington, pp 113–130. Open access online: https://www.nga.gov/content/dam/ngaweb/research/publications/pdfs/artists-pigments-vol1.pdf

Nabais P, Melo Maria J, Lopes JA, Vitorino T, Neves A, Castro R (2018) Microspectrofluorimetry and chemometrics for the identification of medieval lake pigments. Heritage Science 6(1). https://doi.org/10.1186/s40494-018-0178-1

Nesměrák K, Němcová I (2012) Dating of historical manuscripts using spectrometric methods: a mini-review. Anal Lett 45(4):330–344. https://doi.org/10.1080/00032719.2011.644741

Nicholson CE, Beeby A, Gameson R (2016)&nbsp;Shedding light on medieval manuscripts. Spectrosc Europe 28(4): 6–8, open access online https://www.spectroscopyeurope.com/system/files/pdf/SE%2028-4-Raman.pdf

Nodari L, Ricciardi P (2019) Non-invasive identification of paint binders in illuminated manuscripts by ER-FTIR spectroscopy: a systematic study of the influence of different pigments on the binders’ characteristic spectral features. Heritage Sci 7(1): 7. https://doi.org/10.1186/s40494-019-0249-y . Open access online: https://heritagesciencejournal.springeropen.com/track/pdf/ https://doi.org/10.1186/s40494-019-0249-y.pdf

Olsson AM, Calligaro T, Colinart S, Dran JC, Lövestam NEG, Moignard B, Salomon J (2001) Micro-PIXE analysis of an ancient Egyptian papyrus: Identification of pigments used for the “Book of the Dead.” Nucl Instrum Methods Phys Res Sect B 181(1–4):707–714. https://doi.org/10.1016/S0168-583X(01)00545-6

Pérez-Arantegui J (2021) Not only wall paintings – Pigments for cosmetics. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01399-w

Pessanha S, Manso M, Carvalho ML (2012) Application of spectroscopic techniques to the study of illuminated manuscripts: a survey. Spectrochim Acta Part B 71:54–61. https://doi.org/10.1016/j.sab.2012.05.014

Pozzi F, Lombardi JR, Leona M (2013) Winsor & Newton original handbooks: a surface-enhanced Raman scattering (SERS) and Raman spectral database of dyes from modern watercolor pigments.&nbsp;Heritage Sci&nbsp;1(1): 23. https://doi.org/10.1186/2050-7445-1-23 . Open access online: https://link.springer.com/article/ https://doi.org/10.1186/2050-7445-1-23#Abs1

Pouyet E, Devine S, Grafakos T, Kieckhefer R, Salvant J, Smieska L et al (2017) Revealing the biography of a hidden medieval manuscript using synchrotron and conventional imaging techniques. Anal Chim Acta 982:20–30. https://doi.org/10.1016/j.aca.2017.06.016

Pretzel P (2021) Surface alteration of Japanese lacquers on exposure to (UV-free) light. In: Bridgland J (ed) Transcending Boundaries: Integrated Approaches to Conservation. ICOM-CC 19th Triennial Conference Preprints, Beijing, 17-21 May 2021. International Council of Museums, Paris

Ricciardi P, Delaney JK, Facini M, Zeibel JG, Picollo M, Lomax S, Loew M (2012) Near infrared reflectance imaging spectroscopy to map paint binders in situ on illuminated manuscripts. Angew Chem Int Ed 51(23):5607–5610. https://doi.org/10.1002/anie.201200840

Ricciardi P, Schmidt Patterson C (2020) Science of the book: analytical methods for the study of illuminated manuscripts. In: Panayotova S (ed) A handbook of the art and science of illuminated manuscripts. Harvey Miller, London

Ricciardi P, Delaney JK, Facini M, Glinsman L (2013) Use of imaging spectroscopy and in situ analytical methods for the characterization of the materials and techniques of 15th century illuminated manuscripts. J Am Inst Conserv 52(1):13–29. https://doi.org/10.1179/0197136012Z.0000000004

Ricciardi P, Mazzinghi A, Legnaioli S, Ruberto C, Castelli L (2019) The choir books of San Giorgio Maggiore in Venice: results of in depth non-invasive analyses.&nbsp;Heritage 2(2): 1684–1701. https://doi.org/10.3390/heritage2020103 . Open access online: https://www.mdpi.com/2571-9408/2/2/103

Roberts CH, Skeat TC (1983) The birth of the codex. The British Academy, London, pp 15–22

Romero-Pastor J, Navas N, Kuckova S, Rodríguez-Navarro A, Cardell C (2012) Collagen-based proteinaceous binder-pigment interaction study under UV ageing conditions by MALDI-TOF-MS and principal component analysis. Journal of Mass Spectrometry 47(3):322–330. https://doi.org/10.1002/jms.2966

Ropret P, Miliani C, Centeno SA, Tavzes Č, Rosi F (2010) Advances in Raman mapping of works of art. J Raman Spectrosc 41(11):1462–1467. https://doi.org/10.1002/jrs.273

Rosin PL, Lai YK, Liu C, Davis GR, Mills D, Tuson G, Russell Y (2018) Virtual recovery of content from X-ray micro-tomography scans of damaged historic scrolls.&nbsp;Sci Rep&nbsp;8(1): 1–10. https://doi.org/10.1038/s41598-018-29037-x . Open access online: https://www.nature.com/articles/s41598-018-29037-x

Roy A, editor (1993) Artists' Pigments: Volume 2: A Handbook of their History and Characteristics. National Gallery of Art, Washington. Open access online: https://www.nga.gov/content/dam/ngaweb/research/publications/pdfs/artists-pigments-vol2.pdf

Salvadori M, Sbrolli C (2021) Wall paintings through the ages. The Roman period: Republic and early Empire. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01411-3

Schmidt CM, Walton MS, Trentelman K (2009) Characterization of lapis lazuli pigments using a multitechnique analytical approach: implications for identification and geological provenancing. Anal Chem 81(20):8513–8518. https://doi.org/10.1021/ac901436g

Schweppe H, Winter J (1997) Madder and Alizarin. In: West FitzHugh E (ed) Artists' Pigments. A Handbook of their History and Characteristics’ volume 3. National Gallery of Art, Washington. ISBN 0894682563. Open access online: https://www.nga.gov/content/dam/ngaweb/research/publications/pdfs/artists-pigments-vol3.pdf

Scott DA (2016) A review of ancient Egyptian pigments and cosmetics. Stud Conserv 61(4):185–202. https://doi.org/10.1179/2047058414Y.0000000162

Seales WB, Griffioen J, Baumann R, Field M (2011) Analysis of Herculaneum papyri with x-ray computed tomography. In&nbsp; International Conference on nondestructive investigations and microanalysis for the diagnostics and conservation of cultural and environmental heritage . Open access online: https://www.ndt.net/article/art2011/papers/FIELD%20-%20M%2014.pdf

Senesi GS, Harmon RS, Hark RR (2020) Field-portable and handheld LIBS: Historical review, current status and future prospects.Spectrochim Acta Part B Atom Spectrosc: 106013. https://doi.org/10.1016/j.sab.2020.106013

Shahid M, Wertz J, Degano I, Aceto M, Khan MI, Quye A (2019) Analytical methods for determination of anthraquinone dyes in historical textiles: A review. Anal Chim Acta 1083:58–87. https://doi.org/10.1016/j.aca.2019.07.009

Siddall R (2018) Mineral pigments in archaeology: their analysis and the range of available materials. Minerals&nbsp;8(5): 201. https://doi.org/10.3390/min8050201 . Open access online: https://www.mdpi.com/2075-163X/8/5/201

Smieska LM, Mullett R, Ferri L, Woll AR (2017) Trace elements in natural azurite pigments found in illuminated manuscript leaves investigated by synchrotron x-ray fluorescence and diffraction mapping. Appl Phys A 123(7):484. https://doi.org/10.1007/s00339-017-1093-0

Smith GD, Klinshaw RJ II (2009) The presence of trapped carbon dioxide in lapis lazuli and its potential use in geo-sourcing natural ultramarine pigment. J Cult Herit 10(3):415–421. https://doi.org/10.1016/j.culher.2008.12.001

Striova J, Pampaloni E, Barucci M, Fontana R, Bracci S, Riminesi C, Ragionieri P, Marongiu M (2014) Michelangelo’s drawings seen under visible, infrared and X rays. ART’14 11th International Conference, June 11–13 2014, Museo Arqueòlogico Nacional, Madrid

Stromer D, Christlein V, Martindale C et al (2018) (2018) Browsing through sealed historical manuscripts by using 3-D computed tomography with low-brilliance X-ray sources. Sci Rep 8:15335. https://doi.org/10.1038/s41598-018-33685-4.Openaccessonline:doi:10.1038/s41598-018-33685-4

Švarcová S, Hradil D, Hradilová J, Čermáková Z (2021) Pigments – Copper-based greens and blues. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01406-0

Szirmai JA (1999) The Archaeology of medieval bookbinding. Ashgate, p.3. ISBN 0 85967 9047

Tamburini D (2019) Investigating Asian colourants in Chinese textiles from Dunhuang (7th-10th century AD) by high performance liquid chromatography tandem mass spectrometry–Towards the creation of a mass spectra database. Dyes Pigm 163:454–474. https://doi.org/10.1016/j.dyepig.2018.12.025

Thompson DV (1956)&nbsp;The materials and techniques of medieval painting. Courier Corporation

Tonazzini A, Salerno E, Abdel-Salam ZA, Harith MA, Marras L, Botto A et al (2019) Analytical and mathematical methods for revealing hidden details in ancient manuscripts and paintings: A review. J Adv Res 17:31–42. https://doi.org/10.1016/j.jare.2019.01.003

Tournié A, Fleischer K, Bukreeva I, Palermo F, Perino M, Cedola A et al (2019) Ancient Greek text concealed on the back of unrolled papyrus revealed through shortwave-infrared hyperspectral imaging. Sci Adv 5(10). https://doi.org/10.1126/sciadv.aav8936

Trentelman K, Turner N (2009) Investigation of the painting materials and techniques of the late-15th century manuscript illuminator Jean Bourdichon. J Raman Spectrosc 40(5):577–584. https://doi.org/10.1002/jrs.2186

Turner NK, Patterson CS, MacLennan DK, Trentelman K (2019) Visualizing underdrawings in medieval manuscript illuminations with macro-X-ray fluorescence scanning. X-Ray Spectrom 48(4):251–261. https://doi.org/10.1002/xrs.2956

Vermeulen M, Burgio L, Vandeperre N, Driscoll E, Viljoen M, Woo J et al (2020) Beyond the connoisseurship approach: creating a chronology in Hokusai prints using non-invasive techniques and multivariate data analysis. Heritage Sci 8(1):1–13. https://doi.org/10.1186/s40494-020-00406-y

Vitti P (2021) Mortars and masonry - Structural lime and gypsum mortars in Antiquity and Middle Ages. Archaeol Anthropol Sci. https://doi.org/10.1007/s12520-021-01408-y

Wainwright INM, Taylor JM, Harley RD (1986) Lead Antimonate yellow. In: Feller RL (ed) Artists’ Pigments. A Handbook of Their History and Characteristics, Volume 1. National Gallery of Art, Washington, pp&nbsp;219 – 254. Open access online: https://www.nga.gov/content/dam/ngaweb/research/publications/pdfs/artists-pigments-vol3.pdf

West FitzHugh E, editor (1997) Artists' Pigments: Volume 3: A Handbook of their History and Characteristics. National Gallery of Art, Washington. Open access online: https://www.nga.gov/content/dam/ngaweb/research/publications/pdfs/artists-pigments-vol3.pdf

Whitney AV, Van Duyne RP, Casadio F (2006) An innovative surface-enhanced Raman spectroscopy (SERS) method for the identification of six historical red lakes and dyestuffs. J Raman Spectrosc 37(10):993–1002. https://doi.org/10.1002/jrs.1576

Winter J, West FitzHugh E (2007) Pigments based on Carbon. In: Berrie BH (ed) Artists’ Pigments, A Handbook of Their History and Characteristics, vol 4. Archetype, London, pp 1–37 ( ISBN 9781904982234 )

Download references

Acknowledgements

My thanks go to Richard Hark for his advice and comments on the draft of this chapter and to Jane Rutherston, Head of Book Conservation at the Victoria and Albert Museum, for the gift of her time and for our discussions about manuscripts and codices. Some of the examples and images used here were part of the research undertaken within the V&A Research Institute (VARI) Leman Album Project, one of a 5-year programme of projects and partnerships generously supported by the Andrew W. Mellon Foundation. Past financial support by the Access to Research Infrastructures activity in the H2020 Programme of the EU (IPERION-CH grant agreement n. 654028) is also gratefully acknowledged.

Author information

Authors and affiliations.

Science Section, Conservation Department, Victoria and Albert Museum, South Kensington, London, SW7 2RL, UK

Lucia Burgio

Department of Chemistry, University College London, 20 Gordon Street, London, WC1H 0AJ, UK

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Lucia Burgio .

Ethics declarations

Conflict of interest.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Mortars, plasters and pigments: Research questions and answers

Rights and permissions

Reprints and permissions

About this article

Burgio, L. Pigments, dyes and inks: their analysis on manuscripts, scrolls and papyri. Archaeol Anthropol Sci 13 , 194 (2021). https://doi.org/10.1007/s12520-021-01403-3

Download citation

Received : 22 January 2021

Accepted : 30 June 2021

Published : 13 October 2021

DOI : https://doi.org/10.1007/s12520-021-01403-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Manuscripts
• Scientific analysis

Advertisement

• Find a journal
• Publish with us
• Track your research

We are now featured on Product Hunt!       SEO Content Assistant →

SEO Content Editor 4.8

Start Writing content →

The free AI powered writing Assistant

TIP : Now also available as API Discover more →

The SEO Content Editor is a writing assistant designed to help you optimize content for the web. You can just focus on writing GREAT content while the tool will calculate your SEO score and provides you with valuable feedback on how to improve your content Keyword focus, Page Titles, Meta Descriptions and a lot more… Use the editor to add text, images and YouTube video to write appealing content and when you’re done simply copy / past the result to your CMS of choice.

How to select the right focus keyword(s)?

When it comes to writing search engine optimized content, it all starts with the selecting the right focus keyword(s). This is why the free online content editor comes with a built-in keyword suggestion tool. Add the main subject / focus keyword for your article and use the “get suggestions” button to generate an overview of related to keywords so you can compose a keyword theme for your blog post, service or product page. The tool enables you to select a primary focus keyword and secondary keywords from multiple sources:

• Web keyword search suggestions
• Commercial / Shopping keyword suggestions
• YouTube keyword suggestions
• News keyword suggestions

And to give you an indication of the search popularity (measured over time) the tool will render a graph for the selected keyword.

What others say..

I have been working with the SEO Content Editor of SEO Review Tools for a couple of months now, and I couldn’t be happier that I found it. Jasja chose a very efficient approach when he implemented his tools: Keep it simple! You have a great overview of your content, it is very intuitive, and it reminds you that you need to save your files before closing the tab, which saved my life a couple of times.

Andreas Inama — Founder ai.linguistic solutions

Discover “Trending Topics”

Are you’re looking for writing inspiration? Click the ” tools icon” on the bottom left part of the page and click “trending topics” to get an overview of the what is happing today. The trending topics tool collects it’s data from Google’s most popular search queries and shows you:

• Daily search trends
• Related search queries
• Daily search estimates
• And additional research tools to dig deeper into the subject

The trending topics tool is designed to help you discover emerging search trends so you can be the first one to cover a subject.

Discover Trending Topics →

Content Completion

The “Complete content” function is available when you click on your right mouse button. It allows you to generate suggestions or completions for the current heading or paragraph / text section that you’re writing. This helps you to write faster utilizing the power of generative AI, which performs best when asked to create short form content. The content completion function is an invaluable tool as it not only generates content based on your writing but also replicates your unique writing style and tone of voice.

The benifits

• Content is created based on your unique writing style and brand voice.
• Easy accessible, it just takes a single click to put AI to work.
• Have your personal writing assistant that learns from your input and previous writing.

Write faster →

Meta Description generator → new

Create 15 Meta description suggestions to choose from and to make your search snippet stand out a on the search engine results page (SERP). Write effective meta descriptions →

• That are descriptive and highly relevant for the specific page
• Contain your focus keyword
• Stand out on the SERP
• Contain ~ 50 and 160 characters (including spaces and punctuation).

Start generating Meta Descriptions →

AI writing assistant

Use the AI writer integration as your personal writing assistant to become more creative and effective. The AI content writer tool includes a list of example commands to automatically generate headlines, blog title suggestions, keyword research, content creation, content completion, content rephrasing and more..

Here are some SEO Prompt examples that you can use directly from the interface to connect with AI.

• Suggest 5 blog post title ideas for: [keyword]
• Create a blog outline for: [subject]
• Generate a list of semantic keywords for: [subject]
• Complete: [sentence]
• Rewrite: [content]
• And much more …

While using the benefits of automated content creation you should also be aware of its limitations including the fact that information can be outdated, factually incorrect and the generated content is not always unique.

Utilize the power of AI →

Content Rewriter

The integrated content rewriting tool is designed to quickly rewrite, translate or improve a content section. Navigate to the advanced settings tab to gain even more control over the output by specifying the target audience, language, writing style, and other parameters.

When to use the Content rewriting tool? The content rewriting tool can assist you in various situations to optimize your workflow, here are some examples.

• Rewrite product descriptions to make them unique and to prevent duplicate content
• Create multiple unique versions of a press release customized for your specific target audience.
• Make your content more attractive by changing the writing style
• Detect and correct grammar and spelling errors.
• Effortlessly translate content by selecting the desired output language from a range of 27 supported languages.

Rewrite your content →

AI Headline Generator

How to use the headline generator?

• Enter / describe your topic.
• Hit the “Get headline suggestions” button
• And select the headline that work for your article and audience.

Use AI to generate 25 different headline suggestions for your article with just a single click. Spark your creativity and write headlines that both capture your reader’s attention and are optimize for search.

Generate 25 Headline suggestions →

Featured snippet generator

Optimize your content for Featured snippets using the question generator. Enter your keyword and the tool will automatically generate up to 50 questions directly related to your subject.

Google displays featured snippets to directly answer a question in search. Featured snippets most commonly contain a small paragraph of text or a list of items that Google extracts from a webpage. Because featured snippets appear above the “regular” search results they can potentially generate more awareness for your brand and earn you more traffic.

Start Writing Content →

Content Brief Generator

Use the integrated Content Brief Generator to create a comprehensive content briefing that will guide your content creation process and ensure that you meet your goals and objectives.

How to use the Content Brief generator?

• Enter your topic.
• Select the writing style
• Enter related keywords to use
• Describe your target audience
• Add additioanl commands / instructions
• Use the creativity slider to give the AI some freedom
• Hit the “Create content brief” and let AI perform it’s magic.

Content Brief example The content briefing includes:

• Clear formulated content goal.
• 5 SEO optimized Title suggestions
• A list of directly related keywords
• Statistics related to your subject, crucial for making informed decisions and drawing accurate conclusions.
• Common questions that can help you to optimize for featured snippets in Google search.

Generate a content briefing →

Title tag generator →

• Be descriptive
• Be less than 60 characters (including spaces and punctuation).
• Capture attention

Create 15 Title tag suggestions to optimize your content for search and to attract attention. A well optimized title should ideally →

Start creating Titles →

FAQ Generator

Generate 5 Q&A’s in just a couple of seconds with AI.

How to use FAQs for SEO?

FAQ stands for frequently asked questions and they can be used to target search queries that your audience is using to find more information about your industry, products, or services. By expanding existing content or by creating new pages that answer these specific questions you’re able to attract more visitors from search.

How to use the FAQ generator?

• Hit the “Generate FAQs” button
• And select the FAQ that are relevant for your article and audience.

How to create FAQs for SEO?

• Create a list of the most common questions your audience is asking or searching for.
• Provide complete and comprehensive answers for these questions.
• Use FAQ schema to markup your questions and answers to make it easier for search engines to show them directly in the SERP.

Why are FAQs important for SEO?

• Optimize for featured snippets in Google and Bing.
• Expand the content of category pages with relevant questions and answers.
• Optimize for the “People also ask” section in Google search.

Generate FAQs →

Related APIs

• SEO Content Analysis API →
• Readability score API →
• Keyword Research APIs →
• Text Rewriting API →

Feature list

SEO content checklist:

• Text (keyword density)
• Image alt tag
• Word count / Content length
• Content link count (internal and external)
• Page title length and keyword focus
• Meta description length and keyword focus

Warnings: (Warnings are triggered when over-optimization is detected)

• High keyword density aka Keyword stuffing
• Abnormal link count

Keyword research:

• Web search, Shopping, YouTube and News suggestions
• Google Trends / keyword search popularity

Document export options:

• Export your content including images to Word
• Copy the full document HTML source code

Related Content tools & Resources

• Featured Snippet Tool , Quickly identify featured snippet opportunities related to your main subject / keyword.
• Content idea Generator , Looking for inspiration before you start writing a new blog post? Get up to a 100 content ideas in just a matter of seconds.
• Keyword suggestion tool , Find related keywords and synonyms.
• Rank checker , See how well your content is ranking for multiple keywords.
• SEO Content Score checker , Check the SEO score for any public webpage.
• Keyword frequency checker , See how often keywords are used within the content of an URL or piece of text.
• Yoast API alternative , short introduction to the content analysis API

45 thoughts on “ SEO Content Editor 4.8 ”

Updates The latest version of the SEO content Assistant comes with..

• AI Content rewriter, to rewrite, translate or improve your article.
• AI FAQ generator, to optimized for featured snippets.
• The latest version of the SEO content Assistant comes with an integrated Content brief generator
• The integrated AI content completion tool is now powered by GPT-4
• Updates to the integrated Keyword research tool make it easier to discover related keywords

This one is pretty big ;-)) I’ve integrated ChatGPT into the SEO content editor’s interface. This means you can interact with ChatGPT to get; keyword suggestions, content ideas, translate content and much more… Here is a list of example prompts to help you on your way.

• Write a content section about: [keyword]
• Translate: [content]
• Create content brief for: [topic]
• Generate a list of related questions for: [topic]
• Get related statistics for: [topic] and add the source (URL)
• Add a list of 5 external resources for [subject] including an URL
• To make it easier to optimize your content for featured snippets, the SEO content editor now comes equipped with an integrated question generator.
• Improved copy to HTML option, this button lets you copy your content including HTML markup so you can copy paste the content from the editor to your CMS.
• New language specific keywords and trends settings. Now supporting these countries: United States, Canada, United Kingdom, Netherlands, Belgium (FR), Belgium (NL), Austria, Switzerland, Germany, France, Denmark, Sweden, Norway, Ireland, Italy, Spain, Portugal, Greece, Turkey, Ukraine, Russia, India, China, Thailand, Australia, New Zealand (EN)
• Numerous UX improvements
• Improved visualisation SEO content score and progress visualisation
• Improved UX for the keyword input and research tools (you can now use comma separated values and simply hit the enter button to submit keywords), making the tool much more intuitive.
• Additional code improvements to make the tool quicker and more efficient.
• Fixed a HTML formatting issue that was causing a incorrect keyword density score (under very specific conditions).

Thank you Aldo for reporting this problem.

I love the app very much, but it will be more convenient if you could let user add secondary keyword not one by one, but with coma separated. For example, in the current state, if I would add secondary keywords “wp theme”, “fast wp theme”, I need to add it one by one. It would be better if I just type them directly with coma, like ( wp theme, fast wp theme ) and when I hit enter, all secondary keywords I type in would be there in the box.

Thank you very much

Great suggestion, it’s now on the to-do-list.

Thanks for the valuable feedback!!!

How to view saved documents?

Please click the home icon, located at the left bottom of the page or go to: https://www.seoreviewtools.com/content-dashboard/ (make sure you’re logged in the see all your previously saved documents).

• When performing keyword research you can now enter your focus keyword into the content idea generator and get fresh content ideas
• You can now quickly access the Content analysis API
• You can now export your document to Word, or
• Copy the full HTML source of your document, and quickly past it to your favourite CMS (WordPress, Magento..) without having to redo your markup.

Thanks so much for all the hard work you’ve put into these tools over the past few years, much, much appreciated!

You’re welcome Greg ;-)) And thank you for the support!!

• Just did a major design update to make the content editor more user friendly
• Solved an issue with secondary keyword recognition
• Did some small tweaks to the scoring algorithm (won’t affect the score for well optimized content, but sets a threshold for low quality content)

How to see the document that I’ve already saved?

Hi Erna, to see the documents that you’ve created and saved, please go to “Dashboard” and make sure you’re logged-in (otherwise you won’t have access).

• Keyword tool functionality update – Quickly analyse the keyword suggestions and generate / discover new keyword opportunities.
• Design update – Improved UX to make the keyword tool more effective.
• Solved a caching problem with the dashboard page for registered members.
• Just did a small design update to the editor.
• You can now also incorporate the Content Analysis score into your own custom CMS using the API: https://www.seoreviewtools.com/seo-content-analysis-api/
• Updated meta description maximum length advice. This number is now to back to a maximum recommended length of 160characters based on this advice by Danny Sullivan ‏@Google: https://twitter.com/dannysullivan/status/996065145443893249
• Added an notification to prevent data loss when closing your document.
• Extended meta description maximum length advice to 320 characters
• Now supporting the new Google Trends Graph

These free tools are simply great guys, keep it up!

How do I check the authority of content I have written on my blog? I write long post from scratch but still goes to back pages of google search result. Thanks in advance

Hi Waqas, try the website authority checker: http://www.seoreviewtools.com/website-authority-checker/ to see your backlinks Domain Authority, Page Authority and social share count to get a nice indication how well you’re website is doing authority wise.

Great tool, to make a page SEO friendly!

Great tool!

But I have issues with umlauts like ä. Tested Keyword: “Oberflächentechnik”

The keyword density doesn’t work for this (always 0%). And the “first paragraph” list item never work for me (always no keyword in first paragraph; tested 4 times with different sites / keywords).

Thanks for mentioning this issue. I’ll have a closer look, to figure out a solution.

How do you load images? When I try insert image it doesn’t select anything

Hi charles,

You can use the “Insert/edit image” button and then past the image URL. You can’t upload an image, but you can use images that are already published online.

Hi, is it possible to delete some old posts?

Hi karibusana, at this moment you’re not able to delete old posts, because I simply haven’t had the time to build this functionality. But you can change: the keyword, title and content of an older post to keep using the tool.

Please keep in mind that the old post will removed from the dashboard overview.

Can I save more than 25 documents ?

Hi Ibnuhibban,

I’ve set 25 documents as a temporary limit for now. But you can reuse your current document by simply choosing a new keyword and add the corresponding text to get a relevant SEO Content score for your article.

Hey Jasja…

Thanks !! Following your advice as I am completely new to SEO but learning slowly.

Very nice tools.

Can you please let us upload an logo and customise for a report?

Thanks for the feedback. This will probably be a feature for the payed version. And to be realistic this will take me at least a couple of months.

Update 06/01/2016 :

• You can now save 25 documents

Update 05/23/2016 :

• Reset the Meta Description (back to 160 characters) and Title (back to 60 characters).
• You can now check the status of the meta Description and Title in real-time when writing or adjusting. The feedback will let you know if you need to do modifications for a better SEO Content score.
• Save your documents and go to the dashboard to get an general overview of your average SEO Content score for the number of pages you have created.
• You can login and access the dashboard using your free SEO Review Tools account.

Update 05/09/2016

• Added a special characters box to make your meta description and HTML Title stand out from the crowd ➊ ➋ ➌✔ 
• The Google search preview is now shown by default.
• Improved ending (…)  Meta description and HTML Titles
• Faster SEO score update when adding new elements (Title, keywords, etc.)
• Highlight secondary keyword matches  
• Save button always available on screen (UX improvement)
• Made it easier to save a document by launching Title modal when empty page title.  
• Fixed some minor scoring issues

Update 04/13/2016 :

• Save your content / optimized pages
• Improved design

how to make h1 on the post title ?

Just select the text you’d like to transform into a H1 and use the “Paragraph” dropdown and select “Heading 1”.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Numbers and stats

Registered Members

Avg. tool rating

Monthly visitors

Returning visitors

• Mobile Site
• Staff Directory
• Advertise with Ars

Filter by topic

• Biz & IT
• Gaming & Culture

Front page layout

NIGHTMARE SUPPLY CHAIN ATTACK SCENARIO —

What we know about the xz utils backdoor that almost infected the world, malicious updates made to a ubiquitous tool were a few weeks away from going mainstream..

Dan Goodin - Apr 1, 2024 6:55 am UTC

On Friday, a lone Microsoft developer rocked the world when he revealed a backdoor had been intentionally planted in xz Utils, an open source data compression utility available on almost all installations of Linux and other Unix-like operating systems. The person or people behind this project likely spent years on it. They were likely very close to seeing the backdoor update merged into Debian and Red Hat, the two biggest distributions of Linux, when an eagle-eyed software developer spotted something fishy.

Further Reading

Researchers have spent the weekend gathering clues. Here's what we know so far.

What is xz Utils?

xz Utils is nearly ubiquitous in Linux. It provides lossless data compression on virtually all Unix-like operating systems, including Linux. xz Utils provides critical functions for compressing and decompressing data during all kinds of operations. xz Utils also supports the legacy .lzma format, making this component even more crucial.

What happened?

Andres Freund, a developer and engineer working on Microsoft’s PostgreSQL offerings, was recently troubleshooting performance problems a Debian system was experiencing with SSH, the most widely used protocol for remotely logging in to devices over the Internet. Specifically, SSH logins were consuming too many CPU cycles and were generating errors with valgrind , a utility for monitoring computer memory.

Through sheer luck and Freund’s careful eye, he eventually discovered the problems were the result of updates that had been made to xz Utils. On Friday, Freund took to the Open Source Security List to disclose the updates were the result of someone intentionally planting a backdoor in the compression software.

It's hard to overstate the complexity of the social engineering and the inner workings of the backdoor. Thomas Roccia, a researcher at Microsoft, published a graphic on Mastodon that helps visualize the sprawling extent of the nearly successful endeavor to spread a backdoor with a reach that would have dwarfed the SolarWinds event from 2020.

What does the backdoor do?

Malicious code added to xz Utils versions 5.6.0 and 5.6.1 modified the way the software functions. The backdoor manipulated sshd, the executable file used to make remote SSH connections. Anyone in possession of a predetermined encryption key could stash any code of their choice in an SSH login certificate, upload it, and execute it on the backdoored device. No one has actually seen code uploaded, so it's not known what code the attacker planned to run. In theory, the code could allow for just about anything, including stealing encryption keys or installing malware.

Wait, how can a compression utility manipulate a process as security sensitive as SSH?

Any library can tamper with the inner workings of any executable it is linked against. Often, the developer of the executable will establish a link to a library that's needed for it to work properly. OpenSSH, the most popular sshd implementation, doesn’t link the liblzma library, but Debian and many other Linux distributions add a patch to link sshd to systemd , a program that loads a variety of services during the system bootup. Systemd, in turn, links to liblzma, and this allows xz Utils to exert control over sshd.

How did this backdoor come to be?

It would appear that this backdoor was years in the making. In 2021, someone with the username JiaT75 made their first known commit to an open source project. In retrospect, the change to the libarchive project is suspicious, because it replaced the safe_fprint funcion with a variant that has long been recognized as less secure. No one noticed at the time.

The following year, JiaT75 submitted a patch over the xz Utils mailing list, and, almost immediately, a never-before-seen participant named Jigar Kumar joined the discussion and argued that Lasse Collin, the longtime maintainer of xz Utils, hadn’t been updating the software often or fast enough. Kumar, with the support of Dennis Ens and several other people who had never had a presence on the list, pressured Collin to bring on an additional developer to maintain the project.

In January 2023, JiaT75 made their first commit to xz Utils. In the months following, JiaT75, who used the name Jia Tan, became increasingly involved in xz Utils affairs. For instance, Tan replaced Collins' contact information with their own on oss-fuzz, a project that scans open source software for vulnerabilities that can be exploited. Tan also requested that oss-fuzz disable the ifunc function during testing, a change that prevented it from detecting the malicious changes Tan would soon make to xz Utils.

In February of this year, Tan issued commits for versions 5.6.0 and 5.6.1 of xz Utils. The updates implemented the backdoor. In the following weeks, Tan or others appealed to developers of Ubuntu, Red Hat, and Debian to merge the updates into their OSes. Eventually, one of the two updates made its way into the following releases, according to security firm Tenable:

There’s more about Tan and the timeline here .

reader comments

Promoted comments.

It should be noted that the attack only works because Debian and Redhat added functionality to sshd that is not present in it as distributed by its developers. The extra functionality adds systemd interaction, which requires libsystemd which requires liblzma, a component of the (compromised) xz package. One should be wary of distributions adding functionality. Often it increases the attack surface, not only because of the modifications/additions themselves, but also by adding dependencies.

So a prime reason this became potentially exploitable is libsystemd in OpenSSH. Need I say more.

The prime reason is a very well funded and capable attacker looked for a way in. if not xz or systemd then they would have attacked via the next candidate weak point.

"This developer persona has touched dozens of other pieces of open-source software in the past few years.". Well, I guess the Opensource community have some codes to review. Maybe the xz incident is only the tips of the iceberg.

Channel Ars Technica

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

• Michiel Schreurs   ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3   na1 ,
• Supinya Piampongsant 1 , 2 , 3   na1 ,
• Miguel Roncoroni   ORCID: orcid.org/0000-0001-7461-1427 1 , 2 , 3   na1 ,
• Lloyd Cool   ORCID: orcid.org/0000-0001-9936-3124 1 , 2 , 3 , 4 ,
• Beatriz Herrera-Malaver   ORCID: orcid.org/0000-0002-5096-9974 1 , 2 , 3 ,
• Christophe Vanderaa   ORCID: orcid.org/0000-0001-7443-5427 4 ,
• Florian A. Theßeling 1 , 2 , 3 ,
• Łukasz Kreft   ORCID: orcid.org/0000-0001-7620-4657 5 ,
• Alexander Botzki   ORCID: orcid.org/0000-0001-6691-4233 5 ,
• Philippe Malcorps 6 ,
• Luk Daenen 6 ,
• Tom Wenseleers   ORCID: orcid.org/0000-0002-1434-861X 4 &
• Kevin J. Verstrepen   ORCID: orcid.org/0000-0002-3077-6219 1 , 2 , 3  

Nature Communications volume  15 , Article number:  2368 ( 2024 ) Cite this article

49k Accesses

847 Altmetric

Metrics details

• Chemical engineering
• Gas chromatography
• Machine learning
• Metabolomics
• Taste receptors

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

Similar content being viewed by others

BitterSweet: Building machine learning models for predicting the bitter and sweet taste of small molecules

Rudraksh Tuwani, Somin Wadhwa & Ganesh Bagler

Sensory lexicon and aroma volatiles analysis of brewing malt

Xiaoxia Su, Miao Yu, … Tianyi Du

Predicting odor from molecular structure: a multi-label classification approach

Kushagra Saini & Venkatnarayan Ramanathan

Introduction

Predicting and understanding food perception and appreciation is one of the major challenges in food science. Accurate modeling of food flavor and appreciation could yield important opportunities for both producers and consumers, including quality control, product fingerprinting, counterfeit detection, spoilage detection, and the development of new products and product combinations (food pairing) 1 , 2 , 3 , 4 , 5 , 6 . Accurate models for flavor and consumer appreciation would contribute greatly to our scientific understanding of how humans perceive and appreciate flavor. Moreover, accurate predictive models would also facilitate and standardize existing food assessment methods and could supplement or replace assessments by trained and consumer tasting panels, which are variable, expensive and time-consuming 7 , 8 , 9 . Lastly, apart from providing objective, quantitative, accurate and contextual information that can help producers, models can also guide consumers in understanding their personal preferences 10 .

Despite the myriad of applications, predicting food flavor and appreciation from its chemical properties remains a largely elusive goal in sensory science, especially for complex food and beverages 11 , 12 . A key obstacle is the immense number of flavor-active chemicals underlying food flavor. Flavor compounds can vary widely in chemical structure and concentration, making them technically challenging and labor-intensive to quantify, even in the face of innovations in metabolomics, such as non-targeted metabolic fingerprinting 13 , 14 . Moreover, sensory analysis is perhaps even more complicated. Flavor perception is highly complex, resulting from hundreds of different molecules interacting at the physiochemical and sensorial level. Sensory perception is often non-linear, characterized by complex and concentration-dependent synergistic and antagonistic effects 15 , 16 , 17 , 18 , 19 , 20 , 21 that are further convoluted by the genetics, environment, culture and psychology of consumers 22 , 23 , 24 . Perceived flavor is therefore difficult to measure, with problems of sensitivity, accuracy, and reproducibility that can only be resolved by gathering sufficiently large datasets 25 . Trained tasting panels are considered the prime source of quality sensory data, but require meticulous training, are low throughput and high cost. Public databases containing consumer reviews of food products could provide a valuable alternative, especially for studying appreciation scores, which do not require formal training 25 . Public databases offer the advantage of amassing large amounts of data, increasing the statistical power to identify potential drivers of appreciation. However, public datasets suffer from biases, including a bias in the volunteers that contribute to the database, as well as confounding factors such as price, cult status and psychological conformity towards previous ratings of the product.

Classical multivariate statistics and machine learning methods have been used to predict flavor of specific compounds by, for example, linking structural properties of a compound to its potential biological activities or linking concentrations of specific compounds to sensory profiles 1 , 26 . Importantly, most previous studies focused on predicting organoleptic properties of single compounds (often based on their chemical structure) 27 , 28 , 29 , 30 , 31 , 32 , 33 , thus ignoring the fact that these compounds are present in a complex matrix in food or beverages and excluding complex interactions between compounds. Moreover, the classical statistics commonly used in sensory science 34 , 35 , 36 , 37 , 38 , 39 require a large sample size and sufficient variance amongst predictors to create accurate models. They are not fit for studying an extensive set of hundreds of interacting flavor compounds, since they are sensitive to outliers, have a high tendency to overfit and are less suited for non-linear and discontinuous relationships 40 .

In this study, we combine extensive chemical analyses and sensory data of a set of different commercial beers with machine learning approaches to develop models that predict taste, smell, mouthfeel and appreciation from compound concentrations. Beer is particularly suited to model the relationship between chemistry, flavor and appreciation. First, beer is a complex product, consisting of thousands of flavor compounds that partake in complex sensory interactions 41 , 42 , 43 . This chemical diversity arises from the raw materials (malt, yeast, hops, water and spices) and biochemical conversions during the brewing process (kilning, mashing, boiling, fermentation, maturation and aging) 44 , 45 . Second, the advent of the internet saw beer consumers embrace online review platforms, such as RateBeer (ZX Ventures, Anheuser-Busch InBev SA/NV) and BeerAdvocate (Next Glass, inc.). In this way, the beer community provides massive data sets of beer flavor and appreciation scores, creating extraordinarily large sensory databases to complement the analyses of our professional sensory panel. Specifically, we characterize over 200 chemical properties of 250 commercial beers, spread across 22 beer styles, and link these to the descriptive sensory profiling data of a 16-person in-house trained tasting panel and data acquired from over 180,000 public consumer reviews. These unique and extensive datasets enable us to train a suite of machine learning models to predict flavor and appreciation from a beer’s chemical profile. Dissection of the best-performing models allows us to pinpoint specific compounds as potential drivers of beer flavor and appreciation. Follow-up experiments confirm the importance of these compounds and ultimately allow us to significantly improve the flavor and appreciation of selected commercial beers. Together, our study represents a significant step towards understanding complex flavors and reinforces the value of machine learning to develop and refine complex foods. In this way, it represents a stepping stone for further computer-aided food engineering applications 46 .

To generate a comprehensive dataset on beer flavor, we selected 250 commercial Belgian beers across 22 different beer styles (Supplementary Fig.  S1 ). Beers with ≤ 4.2% alcohol by volume (ABV) were classified as non-alcoholic and low-alcoholic. Blonds and Tripels constitute a significant portion of the dataset (12.4% and 11.2%, respectively) reflecting their presence on the Belgian beer market and the heterogeneity of beers within these styles. By contrast, lager beers are less diverse and dominated by a handful of brands. Rare styles such as Brut or Faro make up only a small fraction of the dataset (2% and 1%, respectively) because fewer of these beers are produced and because they are dominated by distinct characteristics in terms of flavor and chemical composition.

Extensive analysis identifies relationships between chemical compounds in beer

For each beer, we measured 226 different chemical properties, including common brewing parameters such as alcohol content, iso-alpha acids, pH, sugar concentration 47 , and over 200 flavor compounds (Methods, Supplementary Table  S1 ). A large portion (37.2%) are terpenoids arising from hopping, responsible for herbal and fruity flavors 16 , 48 . A second major category are yeast metabolites, such as esters and alcohols, that result in fruity and solvent notes 48 , 49 , 50 . Other measured compounds are primarily derived from malt, or other microbes such as non- Saccharomyces yeasts and bacteria (‘wild flora’). Compounds that arise from spices or staling are labeled under ‘Others’. Five attributes (caloric value, total acids and total ester, hop aroma and sulfur compounds) are calculated from multiple individually measured compounds.

As a first step in identifying relationships between chemical properties, we determined correlations between the concentrations of the compounds (Fig.  1 , upper panel, Supplementary Data  1 and 2 , and Supplementary Fig.  S2 . For the sake of clarity, only a subset of the measured compounds is shown in Fig.  1 ). Compounds of the same origin typically show a positive correlation, while absence of correlation hints at parameters varying independently. For example, the hop aroma compounds citronellol, and alpha-terpineol show moderate correlations with each other (Spearman’s rho=0.39 and 0.57), but not with the bittering hop component iso-alpha acids (Spearman’s rho=0.16 and −0.07). This illustrates how brewers can independently modify hop aroma and bitterness by selecting hop varieties and dosage time. If hops are added early in the boiling phase, chemical conversions increase bitterness while aromas evaporate, conversely, late addition of hops preserves aroma but limits bitterness 51 . Similarly, hop-derived iso-alpha acids show a strong anti-correlation with lactic acid and acetic acid, likely reflecting growth inhibition of lactic acid and acetic acid bacteria, or the consequent use of fewer hops in sour beer styles, such as West Flanders ales and Fruit beers, that rely on these bacteria for their distinct flavors 52 . Finally, yeast-derived esters (ethyl acetate, ethyl decanoate, ethyl hexanoate, ethyl octanoate) and alcohols (ethanol, isoamyl alcohol, isobutanol, and glycerol), correlate with Spearman coefficients above 0.5, suggesting that these secondary metabolites are correlated with the yeast genetic background and/or fermentation parameters and may be difficult to influence individually, although the choice of yeast strain may offer some control 53 .

Spearman rank correlations are shown. Descriptors are grouped according to their origin (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)), and sensory aspect (aroma, taste, palate, and overall appreciation). Please note that for the chemical compounds, for the sake of clarity, only a subset of the total number of measured compounds is shown, with an emphasis on the key compounds for each source. For more details, see the main text and Methods section. Chemical data can be found in Supplementary Data  1 , correlations between all chemical compounds are depicted in Supplementary Fig.  S2 and correlation values can be found in Supplementary Data  2 . See Supplementary Data  4 for sensory panel assessments and Supplementary Data  5 for correlation values between all sensory descriptors.

Interestingly, different beer styles show distinct patterns for some flavor compounds (Supplementary Fig.  S3 ). These observations agree with expectations for key beer styles, and serve as a control for our measurements. For instance, Stouts generally show high values for color (darker), while hoppy beers contain elevated levels of iso-alpha acids, compounds associated with bitter hop taste. Acetic and lactic acid are not prevalent in most beers, with notable exceptions such as Kriek, Lambic, Faro, West Flanders ales and Flanders Old Brown, which use acid-producing bacteria ( Lactobacillus and Pediococcus ) or unconventional yeast ( Brettanomyces ) 54 , 55 . Glycerol, ethanol and esters show similar distributions across all beer styles, reflecting their common origin as products of yeast metabolism during fermentation 45 , 53 . Finally, low/no-alcohol beers contain low concentrations of glycerol and esters. This is in line with the production process for most of the low/no-alcohol beers in our dataset, which are produced through limiting fermentation or by stripping away alcohol via evaporation or dialysis, with both methods having the unintended side-effect of reducing the amount of flavor compounds in the final beer 56 , 57 .

Besides expected associations, our data also reveals less trivial associations between beer styles and specific parameters. For example, geraniol and citronellol, two monoterpenoids responsible for citrus, floral and rose flavors and characteristic of Citra hops, are found in relatively high amounts in Christmas, Saison, and Brett/co-fermented beers, where they may originate from terpenoid-rich spices such as coriander seeds instead of hops 58 .

Tasting panel assessments reveal sensorial relationships in beer

To assess the sensory profile of each beer, a trained tasting panel evaluated each of the 250 beers for 50 sensory attributes, including different hop, malt and yeast flavors, off-flavors and spices. Panelists used a tasting sheet (Supplementary Data  3 ) to score the different attributes. Panel consistency was evaluated by repeating 12 samples across different sessions and performing ANOVA. In 95% of cases no significant difference was found across sessions ( p  > 0.05), indicating good panel consistency (Supplementary Table  S2 ).

Aroma and taste perception reported by the trained panel are often linked (Fig.  1 , bottom left panel and Supplementary Data  4 and 5 ), with high correlations between hops aroma and taste (Spearman’s rho=0.83). Bitter taste was found to correlate with hop aroma and taste in general (Spearman’s rho=0.80 and 0.69), and particularly with “grassy” noble hops (Spearman’s rho=0.75). Barnyard flavor, most often associated with sour beers, is identified together with stale hops (Spearman’s rho=0.97) that are used in these beers. Lactic and acetic acid, which often co-occur, are correlated (Spearman’s rho=0.66). Interestingly, sweetness and bitterness are anti-correlated (Spearman’s rho = −0.48), confirming the hypothesis that they mask each other 59 , 60 . Beer body is highly correlated with alcohol (Spearman’s rho = 0.79), and overall appreciation is found to correlate with multiple aspects that describe beer mouthfeel (alcohol, carbonation; Spearman’s rho= 0.32, 0.39), as well as with hop and ester aroma intensity (Spearman’s rho=0.39 and 0.35).

Similar to the chemical analyses, sensorial analyses confirmed typical features of specific beer styles (Supplementary Fig.  S4 ). For example, sour beers (Faro, Flanders Old Brown, Fruit beer, Kriek, Lambic, West Flanders ale) were rated acidic, with flavors of both acetic and lactic acid. Hoppy beers were found to be bitter and showed hop-associated aromas like citrus and tropical fruit. Malt taste is most detected among scotch, stout/porters, and strong ales, while low/no-alcohol beers, which often have a reputation for being ‘worty’ (reminiscent of unfermented, sweet malt extract) appear in the middle. Unsurprisingly, hop aromas are most strongly detected among hoppy beers. Like its chemical counterpart (Supplementary Fig.  S3 ), acidity shows a right-skewed distribution, with the most acidic beers being Krieks, Lambics, and West Flanders ales.

Tasting panel assessments of specific flavors correlate with chemical composition

We find that the concentrations of several chemical compounds strongly correlate with specific aroma or taste, as evaluated by the tasting panel (Fig.  2 , Supplementary Fig.  S5 , Supplementary Data  6 ). In some cases, these correlations confirm expectations and serve as a useful control for data quality. For example, iso-alpha acids, the bittering compounds in hops, strongly correlate with bitterness (Spearman’s rho=0.68), while ethanol and glycerol correlate with tasters’ perceptions of alcohol and body, the mouthfeel sensation of fullness (Spearman’s rho=0.82/0.62 and 0.72/0.57 respectively) and darker color from roasted malts is a good indication of malt perception (Spearman’s rho=0.54).

Heatmap colors indicate Spearman’s Rho. Axes are organized according to sensory categories (aroma, taste, mouthfeel, overall), chemical categories and chemical sources in beer (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)). See Supplementary Data  6 for all correlation values.

Interestingly, for some relationships between chemical compounds and perceived flavor, correlations are weaker than expected. For example, the rose-smelling phenethyl acetate only weakly correlates with floral aroma. This hints at more complex relationships and interactions between compounds and suggests a need for a more complex model than simple correlations. Lastly, we uncovered unexpected correlations. For instance, the esters ethyl decanoate and ethyl octanoate appear to correlate slightly with hop perception and bitterness, possibly due to their fruity flavor. Iron is anti-correlated with hop aromas and bitterness, most likely because it is also anti-correlated with iso-alpha acids. This could be a sign of metal chelation of hop acids 61 , given that our analyses measure unbound hop acids and total iron content, or could result from the higher iron content in dark and Fruit beers, which typically have less hoppy and bitter flavors 62 .

Public consumer reviews complement expert panel data

To complement and expand the sensory data of our trained tasting panel, we collected 180,000 reviews of our 250 beers from the online consumer review platform RateBeer. This provided numerical scores for beer appearance, aroma, taste, palate, overall quality as well as the average overall score.

Public datasets are known to suffer from biases, such as price, cult status and psychological conformity towards previous ratings of a product. For example, prices correlate with appreciation scores for these online consumer reviews (rho=0.49, Supplementary Fig.  S6 ), but not for our trained tasting panel (rho=0.19). This suggests that prices affect consumer appreciation, which has been reported in wine 63 , while blind tastings are unaffected. Moreover, we observe that some beer styles, like lagers and non-alcoholic beers, generally receive lower scores, reflecting that online reviewers are mostly beer aficionados with a preference for specialty beers over lager beers. In general, we find a modest correlation between our trained panel’s overall appreciation score and the online consumer appreciation scores (Fig.  3 , rho=0.29). Apart from the aforementioned biases in the online datasets, serving temperature, sample freshness and surroundings, which are all tightly controlled during the tasting panel sessions, can vary tremendously across online consumers and can further contribute to (among others, appreciation) differences between the two categories of tasters. Importantly, in contrast to the overall appreciation scores, for many sensory aspects the results from the professional panel correlated well with results obtained from RateBeer reviews. Correlations were highest for features that are relatively easy to recognize even for untrained tasters, like bitterness, sweetness, alcohol and malt aroma (Fig.  3 and below).

RateBeer text mining results can be found in Supplementary Data  7 . Rho values shown are Spearman correlation values, with asterisks indicating significant correlations ( p  < 0.05, two-sided). All p values were smaller than 0.001, except for Esters aroma (0.0553), Esters taste (0.3275), Esters aroma—banana (0.0019), Coriander (0.0508) and Diacetyl (0.0134).

Besides collecting consumer appreciation from these online reviews, we developed automated text analysis tools to gather additional data from review texts (Supplementary Data  7 ). Processing review texts on the RateBeer database yielded comparable results to the scores given by the trained panel for many common sensory aspects, including acidity, bitterness, sweetness, alcohol, malt, and hop tastes (Fig.  3 ). This is in line with what would be expected, since these attributes require less training for accurate assessment and are less influenced by environmental factors such as temperature, serving glass and odors in the environment. Consumer reviews also correlate well with our trained panel for 4-vinyl guaiacol, a compound associated with a very characteristic aroma. By contrast, correlations for more specific aromas like ester, coriander or diacetyl are underrepresented in the online reviews, underscoring the importance of using a trained tasting panel and standardized tasting sheets with explicit factors to be scored for evaluating specific aspects of a beer. Taken together, our results suggest that public reviews are trustworthy for some, but not all, flavor features and can complement or substitute taste panel data for these sensory aspects.

Models can predict beer sensory profiles from chemical data

The rich datasets of chemical analyses, tasting panel assessments and public reviews gathered in the first part of this study provided us with a unique opportunity to develop predictive models that link chemical data to sensorial features. Given the complexity of beer flavor, basic statistical tools such as correlations or linear regression may not always be the most suitable for making accurate predictions. Instead, we applied different machine learning models that can model both simple linear and complex interactive relationships. Specifically, we constructed a set of regression models to predict (a) trained panel scores for beer flavor and quality and (b) public reviews’ appreciation scores from beer chemical profiles. We trained and tested 10 different models (Methods), 3 linear regression-based models (simple linear regression with first-order interactions (LR), lasso regression with first-order interactions (Lasso), partial least squares regressor (PLSR)), 5 decision tree models (AdaBoost regressor (ABR), extra trees (ET), gradient boosting regressor (GBR), random forest (RF) and XGBoost regressor (XGBR)), 1 support vector regression (SVR), and 1 artificial neural network (ANN) model.

To compare the performance of our machine learning models, the dataset was randomly split into a training and test set, stratified by beer style. After a model was trained on data in the training set, its performance was evaluated on its ability to predict the test dataset obtained from multi-output models (based on the coefficient of determination, see Methods). Additionally, individual-attribute models were ranked per descriptor and the average rank was calculated, as proposed by Korneva et al. 64 . Importantly, both ways of evaluating the models’ performance agreed in general. Performance of the different models varied (Table  1 ). It should be noted that all models perform better at predicting RateBeer results than results from our trained tasting panel. One reason could be that sensory data is inherently variable, and this variability is averaged out with the large number of public reviews from RateBeer. Additionally, all tree-based models perform better at predicting taste than aroma. Linear models (LR) performed particularly poorly, with negative R 2 values, due to severe overfitting (training set R 2  = 1). Overfitting is a common issue in linear models with many parameters and limited samples, especially with interaction terms further amplifying the number of parameters. L1 regularization (Lasso) successfully overcomes this overfitting, out-competing multiple tree-based models on the RateBeer dataset. Similarly, the dimensionality reduction of PLSR avoids overfitting and improves performance, to some extent. Still, tree-based models (ABR, ET, GBR, RF and XGBR) show the best performance, out-competing the linear models (LR, Lasso, PLSR) commonly used in sensory science 65 .

GBR models showed the best overall performance in predicting sensory responses from chemical information, with R 2 values up to 0.75 depending on the predicted sensory feature (Supplementary Table  S4 ). The GBR models predict consumer appreciation (RateBeer) better than our trained panel’s appreciation (R 2 value of 0.67 compared to R 2 value of 0.09) (Supplementary Table  S3 and Supplementary Table  S4 ). ANN models showed intermediate performance, likely because neural networks typically perform best with larger datasets 66 . The SVR shows intermediate performance, mostly due to the weak predictions of specific attributes that lower the overall performance (Supplementary Table  S4 ).

Model dissection identifies specific, unexpected compounds as drivers of consumer appreciation

Next, we leveraged our models to infer important contributors to sensory perception and consumer appreciation. Consumer preference is a crucial sensory aspects, because a product that shows low consumer appreciation scores often does not succeed commercially 25 . Additionally, the requirement for a large number of representative evaluators makes consumer trials one of the more costly and time-consuming aspects of product development. Hence, a model for predicting chemical drivers of overall appreciation would be a welcome addition to the available toolbox for food development and optimization.

Since GBR models on our RateBeer dataset showed the best overall performance, we focused on these models. Specifically, we used two approaches to identify important contributors. First, rankings of the most important predictors for each sensorial trait in the GBR models were obtained based on impurity-based feature importance (mean decrease in impurity). High-ranked parameters were hypothesized to be either the true causal chemical properties underlying the trait, to correlate with the actual causal properties, or to take part in sensory interactions affecting the trait 67 (Fig.  4A ). In a second approach, we used SHAP 68 to determine which parameters contributed most to the model for making predictions of consumer appreciation (Fig.  4B ). SHAP calculates parameter contributions to model predictions on a per-sample basis, which can be aggregated into an importance score.

A The impurity-based feature importance (mean deviance in impurity, MDI) calculated from the Gradient Boosting Regression (GBR) model predicting RateBeer appreciation scores. The top 15 highest ranked chemical properties are shown. B SHAP summary plot for the top 15 parameters contributing to our GBR model. Each point on the graph represents a sample from our dataset. The color represents the concentration of that parameter, with bluer colors representing low values and redder colors representing higher values. Greater absolute values on the horizontal axis indicate a higher impact of the parameter on the prediction of the model. C Spearman correlations between the 15 most important chemical properties and consumer overall appreciation. Numbers indicate the Spearman Rho correlation coefficient, and the rank of this correlation compared to all other correlations. The top 15 important compounds were determined using SHAP (panel B).

Both approaches identified ethyl acetate as the most predictive parameter for beer appreciation (Fig.  4 ). Ethyl acetate is the most abundant ester in beer with a typical ‘fruity’, ‘solvent’ and ‘alcoholic’ flavor, but is often considered less important than other esters like isoamyl acetate. The second most important parameter identified by SHAP is ethanol, the most abundant beer compound after water. Apart from directly contributing to beer flavor and mouthfeel, ethanol drastically influences the physical properties of beer, dictating how easily volatile compounds escape the beer matrix to contribute to beer aroma 69 . Importantly, it should also be noted that the importance of ethanol for appreciation is likely inflated by the very low appreciation scores of non-alcoholic beers (Supplementary Fig.  S4 ). Despite not often being considered a driver of beer appreciation, protein level also ranks highly in both approaches, possibly due to its effect on mouthfeel and body 70 . Lactic acid, which contributes to the tart taste of sour beers, is the fourth most important parameter identified by SHAP, possibly due to the generally high appreciation of sour beers in our dataset.

Interestingly, some of the most important predictive parameters for our model are not well-established as beer flavors or are even commonly regarded as being negative for beer quality. For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .

To compare our approach to conventional statistics, we evaluated how well the 15 most important SHAP-derived parameters correlate with consumer appreciation (Fig.  4C ). Interestingly, only 6 of the properties derived by SHAP rank amongst the top 15 most correlated parameters. For some chemical compounds, the correlations are so low that they would have likely been considered unimportant. For example, lactic acid, the fourth most important parameter, shows a bimodal distribution for appreciation, with sour beers forming a separate cluster, that is missed entirely by the Spearman correlation. Additionally, the correlation plots reveal outliers, emphasizing the need for robust analysis tools. Together, this highlights the need for alternative models, like the Gradient Boosting model, that better grasp the complexity of (beer) flavor.

Finally, to observe the relationships between these chemical properties and their predicted targets, partial dependence plots were constructed for the six most important predictors of consumer appreciation 74 , 75 , 76 (Supplementary Fig.  S7 ). One-way partial dependence plots show how a change in concentration affects the predicted appreciation. These plots reveal an important limitation of our models: appreciation predictions remain constant at ever-increasing concentrations. This implies that once a threshold concentration is reached, further increasing the concentration does not affect appreciation. This is false, as it is well-documented that certain compounds become unpleasant at high concentrations, including ethyl acetate (‘nail polish’) 77 and methanethiol (‘sulfury’ and ‘rotten cabbage’) 78 . The inability of our models to grasp that flavor compounds have optimal levels, above which they become negative, is a consequence of working with commercial beer brands where (off-)flavors are rarely too high to negatively impact the product. The two-way partial dependence plots show how changing the concentration of two compounds influences predicted appreciation, visualizing their interactions (Supplementary Fig.  S7 ). In our case, the top 5 parameters are dominated by additive or synergistic interactions, with high concentrations for both compounds resulting in the highest predicted appreciation.

To assess the robustness of our best-performing models and model predictions, we performed 100 iterations of the GBR, RF and ET models. In general, all iterations of the models yielded similar performance (Supplementary Fig.  S8 ). Moreover, the main predictors (including the top predictors ethanol and ethyl acetate) remained virtually the same, especially for GBR and RF. For the iterations of the ET model, we did observe more variation in the top predictors, which is likely a consequence of the model’s inherent random architecture in combination with co-correlations between certain predictors. However, even in this case, several of the top predictors (ethanol and ethyl acetate) remain unchanged, although their rank in importance changes (Supplementary Fig.  S8 ).

Next, we investigated if a combination of RateBeer and trained panel data into one consolidated dataset would lead to stronger models, under the hypothesis that such a model would suffer less from bias in the datasets. A GBR model was trained to predict appreciation on the combined dataset. This model underperformed compared to the RateBeer model, both in the native case and when including a dataset identifier (R 2  = 0.67, 0.26 and 0.42 respectively). For the latter, the dataset identifier is the most important feature (Supplementary Fig.  S9 ), while most of the feature importance remains unchanged, with ethyl acetate and ethanol ranking highest, like in the original model trained only on RateBeer data. It seems that the large variation in the panel dataset introduces noise, weakening the models’ performances and reliability. In addition, it seems reasonable to assume that both datasets are fundamentally different, with the panel dataset obtained by blind tastings by a trained professional panel.

Lastly, we evaluated whether beer style identifiers would further enhance the model’s performance. A GBR model was trained with parameters that explicitly encoded the styles of the samples. This did not improve model performance (R2 = 0.66 with style information vs R2 = 0.67). The most important chemical features are consistent with the model trained without style information (eg. ethanol and ethyl acetate), and with the exception of the most preferred (strong ale) and least preferred (low/no-alcohol) styles, none of the styles were among the most important features (Supplementary Fig.  S9 , Supplementary Table  S5 and S6 ). This is likely due to a combination of style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original models, as well as the low number of samples belonging to some styles, making it difficult for the model to learn style-specific patterns. Moreover, beer styles are not rigorously defined, with some styles overlapping in features and some beers being misattributed to a specific style, all of which leads to more noise in models that use style parameters.

Model validation

To test if our predictive models give insight into beer appreciation, we set up experiments aimed at improving existing commercial beers. We specifically selected overall appreciation as the trait to be examined because of its complexity and commercial relevance. Beer flavor comprises a complex bouquet rather than single aromas and tastes 53 . Hence, adding a single compound to the extent that a difference is noticeable may lead to an unbalanced, artificial flavor. Therefore, we evaluated the effect of combinations of compounds. Because Blond beers represent the most extensive style in our dataset, we selected a beer from this style as the starting material for these experiments (Beer 64 in Supplementary Data  1 ).

In the first set of experiments, we adjusted the concentrations of compounds that made up the most important predictors of overall appreciation (ethyl acetate, ethanol, lactic acid, ethyl phenyl acetate) together with correlated compounds (ethyl hexanoate, isoamyl acetate, glycerol), bringing them up to 95 th percentile ethanol-normalized concentrations (Methods) within the Blond group (‘Spiked’ concentration in Fig.  5A ). Compared to controls, the spiked beers were found to have significantly improved overall appreciation among trained panelists, with panelist noting increased intensity of ester flavors, sweetness, alcohol, and body fullness (Fig.  5B ). To disentangle the contribution of ethanol to these results, a second experiment was performed without the addition of ethanol. This resulted in a similar outcome, including increased perception of alcohol and overall appreciation.

Adding the top chemical compounds, identified as best predictors of appreciation by our model, into poorly appreciated beers results in increased appreciation from our trained panel. Results of sensory tests between base beers and those spiked with compounds identified as the best predictors by the model. A Blond and Non/Low-alcohol (0.0% ABV) base beers were brought up to 95th-percentile ethanol-normalized concentrations within each style. B For each sensory attribute, tasters indicated the more intense sample and selected the sample they preferred. The numbers above the bars correspond to the p values that indicate significant changes in perceived flavor (two-sided binomial test: alpha 0.05, n  = 20 or 13).

In a last experiment, we tested whether using the model’s predictions can boost the appreciation of a non-alcoholic beer (beer 223 in Supplementary Data  1 ). Again, the addition of a mixture of predicted compounds (omitting ethanol, in this case) resulted in a significant increase in appreciation, body, ester flavor and sweetness.

Predicting flavor and consumer appreciation from chemical composition is one of the ultimate goals of sensory science. A reliable, systematic and unbiased way to link chemical profiles to flavor and food appreciation would be a significant asset to the food and beverage industry. Such tools would substantially aid in quality control and recipe development, offer an efficient and cost-effective alternative to pilot studies and consumer trials and would ultimately allow food manufacturers to produce superior, tailor-made products that better meet the demands of specific consumer groups more efficiently.

A limited set of studies have previously tried, to varying degrees of success, to predict beer flavor and beer popularity based on (a limited set of) chemical compounds and flavors 79 , 80 . Current sensitive, high-throughput technologies allow measuring an unprecedented number of chemical compounds and properties in a large set of samples, yielding a dataset that can train models that help close the gaps between chemistry and flavor, even for a complex natural product like beer. To our knowledge, no previous research gathered data at this scale (250 samples, 226 chemical parameters, 50 sensory attributes and 5 consumer scores) to disentangle and validate the chemical aspects driving beer preference using various machine-learning techniques. We find that modern machine learning models outperform conventional statistical tools, such as correlations and linear models, and can successfully predict flavor appreciation from chemical composition. This could be attributed to the natural incorporation of interactions and non-linear or discontinuous effects in machine learning models, which are not easily grasped by the linear model architecture. While linear models and partial least squares regression represent the most widespread statistical approaches in sensory science, in part because they allow interpretation 65 , 81 , 82 , modern machine learning methods allow for building better predictive models while preserving the possibility to dissect and exploit the underlying patterns. Of the 10 different models we trained, tree-based models, such as our best performing GBR, showed the best overall performance in predicting sensory responses from chemical information, outcompeting artificial neural networks. This agrees with previous reports for models trained on tabular data 83 . Our results are in line with the findings of Colantonio et al. who also identified the gradient boosting architecture as performing best at predicting appreciation and flavor (of tomatoes and blueberries, in their specific study) 26 . Importantly, besides our larger experimental scale, we were able to directly confirm our models’ predictions in vivo.

Our study confirms that flavor compound concentration does not always correlate with perception, suggesting complex interactions that are often missed by more conventional statistics and simple models. Specifically, we find that tree-based algorithms may perform best in developing models that link complex food chemistry with aroma. Furthermore, we show that massive datasets of untrained consumer reviews provide a valuable source of data, that can complement or even replace trained tasting panels, especially for appreciation and basic flavors, such as sweetness and bitterness. This holds despite biases that are known to occur in such datasets, such as price or conformity bias. Moreover, GBR models predict taste better than aroma. This is likely because taste (e.g. bitterness) often directly relates to the corresponding chemical measurements (e.g., iso-alpha acids), whereas such a link is less clear for aromas, which often result from the interplay between multiple volatile compounds. We also find that our models are best at predicting acidity and alcohol, likely because there is a direct relation between the measured chemical compounds (acids and ethanol) and the corresponding perceived sensorial attribute (acidity and alcohol), and because even untrained consumers are generally able to recognize these flavors and aromas.

The predictions of our final models, trained on review data, hold even for blind tastings with small groups of trained tasters, as demonstrated by our ability to validate specific compounds as drivers of beer flavor and appreciation. Since adding a single compound to the extent of a noticeable difference may result in an unbalanced flavor profile, we specifically tested our identified key drivers as a combination of compounds. While this approach does not allow us to validate if a particular single compound would affect flavor and/or appreciation, our experiments do show that this combination of compounds increases consumer appreciation.

It is important to stress that, while it represents an important step forward, our approach still has several major limitations. A key weakness of the GBR model architecture is that amongst co-correlating variables, the largest main effect is consistently preferred for model building. As a result, co-correlating variables often have artificially low importance scores, both for impurity and SHAP-based methods, like we observed in the comparison to the more randomized Extra Trees models. This implies that chemicals identified as key drivers of a specific sensory feature by GBR might not be the true causative compounds, but rather co-correlate with the actual causative chemical. For example, the high importance of ethyl acetate could be (partially) attributed to the total ester content, ethanol or ethyl hexanoate (rho=0.77, rho=0.72 and rho=0.68), while ethyl phenylacetate could hide the importance of prenyl isobutyrate and ethyl benzoate (rho=0.77 and rho=0.76). Expanding our GBR model to include beer style as a parameter did not yield additional power or insight. This is likely due to style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original model, as well as the smaller sample size per style, limiting the power to uncover style-specific patterns. This can be partly attributed to the curse of dimensionality, where the high number of parameters results in the models mainly incorporating single parameter effects, rather than complex interactions such as style-dependent effects 67 . A larger number of samples may overcome some of these limitations and offer more insight into style-specific effects. On the other hand, beer style is not a rigid scientific classification, and beers within one style often differ a lot, which further complicates the analysis of style as a model factor.

Our study is limited to beers from Belgian breweries. Although these beers cover a large portion of the beer styles available globally, some beer styles and consumer patterns may be missing, while other features might be overrepresented. For example, many Belgian ales exhibit yeast-driven flavor profiles, which is reflected in the chemical drivers of appreciation discovered by this study. In future work, expanding the scope to include diverse markets and beer styles could lead to the identification of even more drivers of appreciation and better models for special niche products that were not present in our beer set.

In addition to inherent limitations of GBR models, there are also some limitations associated with studying food aroma. Even if our chemical analyses measured most of the known aroma compounds, the total number of flavor compounds in complex foods like beer is still larger than the subset we were able to measure in this study. For example, hop-derived thiols, that influence flavor at very low concentrations, are notoriously difficult to measure in a high-throughput experiment. Moreover, consumer perception remains subjective and prone to biases that are difficult to avoid. It is also important to stress that the models are still immature and that more extensive datasets will be crucial for developing more complete models in the future. Besides more samples and parameters, our dataset does not include any demographic information about the tasters. Including such data could lead to better models that grasp external factors like age and culture. Another limitation is that our set of beers consists of high-quality end-products and lacks beers that are unfit for sale, which limits the current model in accurately predicting products that are appreciated very badly. Finally, while models could be readily applied in quality control, their use in sensory science and product development is restrained by their inability to discern causal relationships. Given that the models cannot distinguish compounds that genuinely drive consumer perception from those that merely correlate, validation experiments are essential to identify true causative compounds.

Despite the inherent limitations, dissection of our models enabled us to pinpoint specific molecules as potential drivers of beer aroma and consumer appreciation, including compounds that were unexpected and would not have been identified using standard approaches. Important drivers of beer appreciation uncovered by our models include protein levels, ethyl acetate, ethyl phenyl acetate and lactic acid. Currently, many brewers already use lactic acid to acidify their brewing water and ensure optimal pH for enzymatic activity during the mashing process. Our results suggest that adding lactic acid can also improve beer appreciation, although its individual effect remains to be tested. Interestingly, ethanol appears to be unnecessary to improve beer appreciation, both for blond beer and alcohol-free beer. Given the growing consumer interest in alcohol-free beer, with a predicted annual market growth of >7% 84 , it is relevant for brewers to know what compounds can further increase consumer appreciation of these beers. Hence, our model may readily provide avenues to further improve the flavor and consumer appreciation of both alcoholic and non-alcoholic beers, which is generally considered one of the key challenges for future beer production.

Whereas we see a direct implementation of our results for the development of superior alcohol-free beverages and other food products, our study can also serve as a stepping stone for the development of novel alcohol-containing beverages. We want to echo the growing body of scientific evidence for the negative effects of alcohol consumption, both on the individual level by the mutagenic, teratogenic and carcinogenic effects of ethanol 85 , 86 , as well as the burden on society caused by alcohol abuse and addiction. We encourage the use of our results for the production of healthier, tastier products, including novel and improved beverages with lower alcohol contents. Furthermore, we strongly discourage the use of these technologies to improve the appreciation or addictive properties of harmful substances.

The present work demonstrates that despite some important remaining hurdles, combining the latest developments in chemical analyses, sensory analysis and modern machine learning methods offers exciting avenues for food chemistry and engineering. Soon, these tools may provide solutions in quality control and recipe development, as well as new approaches to sensory science and flavor research.

Beer selection

250 commercial Belgian beers were selected to cover the broad diversity of beer styles and corresponding diversity in chemical composition and aroma. See Supplementary Fig.  S1 .

Chemical dataset

Sample preparation.

Beers within their expiration date were purchased from commercial retailers. Samples were prepared in biological duplicates at room temperature, unless explicitly stated otherwise. Bottle pressure was measured with a manual pressure device (Steinfurth Mess-Systeme GmbH) and used to calculate CO 2 concentration. The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. Samples were then prepared for measurements by targeted Headspace-Gas Chromatography-Flame Ionization Detector/Flame Photometric Detector (HS-GC-FID/FPD), Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), colorimetric analysis, enzymatic analysis, Near-Infrared (NIR) analysis, as described in the sections below. The mean values of biological duplicates are reported for each compound.

HS-GC-FID/FPD

HS-GC-FID/FPD (Shimadzu GC 2010 Plus) was used to measure higher alcohols, acetaldehyde, esters, 4-vinyl guaicol, and sulfur compounds. Each measurement comprised 5 ml of sample pipetted into a 20 ml glass vial containing 1.75 g NaCl (VWR, 27810.295). 100 µl of 2-heptanol (Sigma-Aldrich, H3003) (internal standard) solution in ethanol (Fisher Chemical, E/0650DF/C17) was added for a final concentration of 2.44 mg/L. Samples were flushed with nitrogen for 10 s, sealed with a silicone septum, stored at −80 °C and analyzed in batches of 20.

The GC was equipped with a DB-WAXetr column (length, 30 m; internal diameter, 0.32 mm; layer thickness, 0.50 µm; Agilent Technologies, Santa Clara, CA, USA) to the FID and an HP-5 column (length, 30 m; internal diameter, 0.25 mm; layer thickness, 0.25 µm; Agilent Technologies, Santa Clara, CA, USA) to the FPD. N 2 was used as the carrier gas. Samples were incubated for 20 min at 70 °C in the headspace autosampler (Flow rate, 35 cm/s; Injection volume, 1000 µL; Injection mode, split; Combi PAL autosampler, CTC analytics, Switzerland). The injector, FID and FPD temperatures were kept at 250 °C. The GC oven temperature was first held at 50 °C for 5 min and then allowed to rise to 80 °C at a rate of 5 °C/min, followed by a second ramp of 4 °C/min until 200 °C kept for 3 min and a final ramp of (4 °C/min) until 230 °C for 1 min. Results were analyzed with the GCSolution software version 2.4 (Shimadzu, Kyoto, Japan). The GC was calibrated with a 5% EtOH solution (VWR International) containing the volatiles under study (Supplementary Table  S7 ).

HS-SPME-GC-MS

HS-SPME-GC-MS (Shimadzu GCMS-QP-2010 Ultra) was used to measure additional volatile compounds, mainly comprising terpenoids and esters. Samples were analyzed by HS-SPME using a triphase DVB/Carboxen/PDMS 50/30 μm SPME fiber (Supelco Co., Bellefonte, PA, USA) followed by gas chromatography (Thermo Fisher Scientific Trace 1300 series, USA) coupled to a mass spectrometer (Thermo Fisher Scientific ISQ series MS) equipped with a TriPlus RSH autosampler. 5 ml of degassed beer sample was placed in 20 ml vials containing 1.75 g NaCl (VWR, 27810.295). 5 µl internal standard mix was added, containing 2-heptanol (1 g/L) (Sigma-Aldrich, H3003), 4-fluorobenzaldehyde (1 g/L) (Sigma-Aldrich, 128376), 2,3-hexanedione (1 g/L) (Sigma-Aldrich, 144169) and guaiacol (1 g/L) (Sigma-Aldrich, W253200) in ethanol (Fisher Chemical, E/0650DF/C17). Each sample was incubated at 60 °C in the autosampler oven with constant agitation. After 5 min equilibration, the SPME fiber was exposed to the sample headspace for 30 min. The compounds trapped on the fiber were thermally desorbed in the injection port of the chromatograph by heating the fiber for 15 min at 270 °C.

The GC-MS was equipped with a low polarity RXi-5Sil MS column (length, 20 m; internal diameter, 0.18 mm; layer thickness, 0.18 µm; Restek, Bellefonte, PA, USA). Injection was performed in splitless mode at 320 °C, a split flow of 9 ml/min, a purge flow of 5 ml/min and an open valve time of 3 min. To obtain a pulsed injection, a programmed gas flow was used whereby the helium gas flow was set at 2.7 mL/min for 0.1 min, followed by a decrease in flow of 20 ml/min to the normal 0.9 mL/min. The temperature was first held at 30 °C for 3 min and then allowed to rise to 80 °C at a rate of 7 °C/min, followed by a second ramp of 2 °C/min till 125 °C and a final ramp of 8 °C/min with a final temperature of 270 °C.

Mass acquisition range was 33 to 550 amu at a scan rate of 5 scans/s. Electron impact ionization energy was 70 eV. The interface and ion source were kept at 275 °C and 250 °C, respectively. A mix of linear n-alkanes (from C7 to C40, Supelco Co.) was injected into the GC-MS under identical conditions to serve as external retention index markers. Identification and quantification of the compounds were performed using an in-house developed R script as described in Goelen et al. and Reher et al. 87 , 88 (for package information, see Supplementary Table  S8 ). Briefly, chromatograms were analyzed using AMDIS (v2.71) 89 to separate overlapping peaks and obtain pure compound spectra. The NIST MS Search software (v2.0 g) in combination with the NIST2017, FFNSC3 and Adams4 libraries were used to manually identify the empirical spectra, taking into account the expected retention time. After background subtraction and correcting for retention time shifts between samples run on different days based on alkane ladders, compound elution profiles were extracted and integrated using a file with 284 target compounds of interest, which were either recovered in our identified AMDIS list of spectra or were known to occur in beer. Compound elution profiles were estimated for every peak in every chromatogram over a time-restricted window using weighted non-negative least square analysis after which peak areas were integrated 87 , 88 . Batch effect correction was performed by normalizing against the most stable internal standard compound, 4-fluorobenzaldehyde. Out of all 284 target compounds that were analyzed, 167 were visually judged to have reliable elution profiles and were used for final analysis.

Discrete photometric and enzymatic analysis

Discrete photometric and enzymatic analysis (Thermo Scientific TM Gallery TM Plus Beermaster Discrete Analyzer) was used to measure acetic acid, ammonia, beta-glucan, iso-alpha acids, color, sugars, glycerol, iron, pH, protein, and sulfite. 2 ml of sample volume was used for the analyses. Information regarding the reagents and standard solutions used for analyses and calibrations is included in Supplementary Table  S7 and Supplementary Table  S9 .

NIR analyses

NIR analysis (Anton Paar Alcolyzer Beer ME System) was used to measure ethanol. Measurements comprised 50 ml of sample, and a 10% EtOH solution was used for calibration.

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

Our trained tasting panel consisted of volunteers who gave prior verbal informed consent. All compounds used for the validation experiment were of food-grade quality. The tasting sessions were approved by the Social and Societal Ethics Committee of the KU Leuven (G-2022-5677-R2(MAR)). All online reviewers agreed to the Terms and Conditions of the RateBeer website.

Sensory analysis was performed according to the American Society of Brewing Chemists (ASBC) Sensory Analysis Methods 90 . 30 volunteers were screened through a series of triangle tests. The sixteen most sensitive and consistent tasters were retained as taste panel members. The resulting panel was diverse in age [22–42, mean: 29], sex [56% male] and nationality [7 different countries]. The panel developed a consensus vocabulary to describe beer aroma, taste and mouthfeel. Panelists were trained to identify and score 50 different attributes, using a 7-point scale to rate attributes’ intensity. The scoring sheet is included as Supplementary Data  3 . Sensory assessments took place between 10–12 a.m. The beers were served in black-colored glasses. Per session, between 5 and 12 beers of the same style were tasted at 12 °C to 16 °C. Two reference beers were added to each set and indicated as ‘Reference 1 & 2’, allowing panel members to calibrate their ratings. Not all panelists were present at every tasting. Scores were scaled by standard deviation and mean-centered per taster. Values are represented as z-scores and clustered by Euclidean distance. Pairwise Spearman correlations were calculated between taste and aroma sensory attributes. Panel consistency was evaluated by repeating samples on different sessions and performing ANOVA to identify differences, using the ‘stats’ package (v4.2.2) in R (for package information, see Supplementary Table  S8 ).

Online reviews from a public database

The ‘scrapy’ package in Python (v3.6) (for package information, see Supplementary Table  S8 ). was used to collect 232,288 online reviews (mean=922, min=6, max=5343) from RateBeer, an online beer review database. Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately. Numerical scores were scaled and centered per rater, and mean scores were calculated per beer.

For the review texts, the language was estimated using the packages ‘langdetect’ and ‘langid’ in Python. Reviews that were classified as English by both packages were kept. Reviewers with fewer than 100 entries overall were discarded. 181,025 reviews from >6000 reviewers from >40 countries remained. Text processing was done using the ‘nltk’ package in Python. Texts were corrected for slang and misspellings; proper nouns and rare words that are relevant to the beer context were specified and kept as-is (‘Chimay’,’Lambic’, etc.). A dictionary of semantically similar sensorial terms, for example ‘floral’ and ‘flower’, was created and collapsed together into one term. Words were stemmed and lemmatized to avoid identifying words such as ‘acid’ and ‘acidity’ as separate terms. Numbers and punctuation were removed.

Sentences from up to 50 randomly chosen reviews per beer were manually categorized according to the aspect of beer they describe (appearance, aroma, taste, palate, overall quality—not to be confused with the 5 numerical scores described above) or flagged as irrelevant if they contained no useful information. If a beer contained fewer than 50 reviews, all reviews were manually classified. This labeled data set was used to train a model that classified the rest of the sentences for all beers 91 . Sentences describing taste and aroma were extracted, and term frequency–inverse document frequency (TFIDF) was implemented to calculate enrichment scores for sensorial words per beer.

The sex of the tasting subject was not considered when building our sensory database. Instead, results from different panelists were averaged, both for our trained panel (56% male, 44% female) and the RateBeer reviews (70% male, 30% female for RateBeer as a whole).

Beer price collection and processing

Beer prices were collected from the following stores: Colruyt, Delhaize, Total Wine, BeerHawk, The Belgian Beer Shop, The Belgian Shop, and Beer of Belgium. Where applicable, prices were converted to Euros and normalized per liter. Spearman correlations were calculated between these prices and mean overall appreciation scores from RateBeer and the taste panel, respectively.

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

Regression models were constructed to predict (a) trained panel scores for beer flavors and quality from beer chemical profiles and (b) public reviews’ appreciation scores from beer chemical profiles. Z-scores were used to represent sensory attributes in both data sets. Chemical properties with log-normal distributions (Shapiro-Wilk test, p  <  0.05 ) were log-transformed. Missing chemical measurements (0.1% of all data) were replaced with mean values per attribute. Observations from 250 beers were randomly separated into a training set (70%, 175 beers) and a test set (30%, 75 beers), stratified per beer style. Chemical measurements (p = 231) were normalized based on the training set average and standard deviation. In total, three linear regression-based models: linear regression with first-order interaction terms (LR), lasso regression with first-order interaction terms (Lasso) and partial least squares regression (PLSR); five decision tree models, Adaboost regressor (ABR), Extra Trees (ET), Gradient Boosting regressor (GBR), Random Forest (RF) and XGBoost regressor (XGBR); one support vector machine model (SVR) and one artificial neural network model (ANN) were trained. The models were implemented using the ‘scikit-learn’ package (v1.2.2) and ‘xgboost’ package (v1.7.3) in Python (v3.9.16). Models were trained, and hyperparameters optimized, using five-fold cross-validated grid search with the coefficient of determination (R 2 ) as the evaluation metric. The ANN (scikit-learn’s MLPRegressor) was optimized using Bayesian Tree-Structured Parzen Estimator optimization with the ‘Optuna’ Python package (v3.2.0). Individual models were trained per attribute, and a multi-output model was trained on all attributes simultaneously.

Model dissection

GBR was found to outperform other methods, resulting in models with the highest average R 2 values in both trained panel and public review data sets. Impurity-based rankings of the most important predictors for each predicted sensorial trait were obtained using the ‘scikit-learn’ package. To observe the relationships between these chemical properties and their predicted targets, partial dependence plots (PDP) were constructed for the six most important predictors of consumer appreciation 74 , 75 .

The ‘SHAP’ package in Python (v0.41.0) was implemented to provide an alternative ranking of predictor importance and to visualize the predictors’ effects as a function of their concentration 68 .

Validation of causal chemical properties

To validate the effects of the most important model features on predicted sensory attributes, beers were spiked with the chemical compounds identified by the models and descriptive sensory analyses were carried out according to the American Society of Brewing Chemists (ASBC) protocol 90 .

Compound spiking was done 30 min before tasting. Compounds were spiked into fresh beer bottles, that were immediately resealed and inverted three times. Fresh bottles of beer were opened for the same duration, resealed, and inverted thrice, to serve as controls. Pairs of spiked samples and controls were served simultaneously, chilled and in dark glasses as outlined in the Trained panel section above. Tasters were instructed to select the glass with the higher flavor intensity for each attribute (directional difference test 92 ) and to select the glass they prefer.

The final concentration after spiking was equal to the within-style average, after normalizing by ethanol concentration. This was done to ensure balanced flavor profiles in the final spiked beer. The same methods were applied to improve a non-alcoholic beer. Compounds were the following: ethyl acetate (Merck KGaA, W241415), ethyl hexanoate (Merck KGaA, W243906), isoamyl acetate (Merck KGaA, W205508), phenethyl acetate (Merck KGaA, W285706), ethanol (96%, Colruyt), glycerol (Merck KGaA, W252506), lactic acid (Merck KGaA, 261106).

Significant differences in preference or perceived intensity were determined by performing the two-sided binomial test on each attribute.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support the findings of this work are available in the Supplementary Data files and have been deposited to Zenodo under accession code 10653704 93 . The RateBeer scores data are under restricted access, they are not publicly available as they are property of RateBeer (ZX Ventures, USA). Access can be obtained from the authors upon reasonable request and with permission of RateBeer (ZX Ventures, USA).  Source data are provided with this paper.

Code availability

The code for training the machine learning models, analyzing the models, and generating the figures has been deposited to Zenodo under accession code 10653704 93 .

Tieman, D. et al. A chemical genetic roadmap to improved tomato flavor. Science 355 , 391–394 (2017).

Article   ADS   CAS   PubMed   Google Scholar  

Plutowska, B. & Wardencki, W. Application of gas chromatography–olfactometry (GC–O) in analysis and quality assessment of alcoholic beverages – A review. Food Chem. 107 , 449–463 (2008).

Article   CAS   Google Scholar  

Legin, A., Rudnitskaya, A., Seleznev, B. & Vlasov, Y. Electronic tongue for quality assessment of ethanol, vodka and eau-de-vie. Anal. Chim. Acta 534 , 129–135 (2005).

Loutfi, A., Coradeschi, S., Mani, G. K., Shankar, P. & Rayappan, J. B. B. Electronic noses for food quality: A review. J. Food Eng. 144 , 103–111 (2015).

Ahn, Y.-Y., Ahnert, S. E., Bagrow, J. P. & Barabási, A.-L. Flavor network and the principles of food pairing. Sci. Rep. 1 , 196 (2011).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Bartoshuk, L. M. & Klee, H. J. Better fruits and vegetables through sensory analysis. Curr. Biol. 23 , R374–R378 (2013).

Article   CAS   PubMed   Google Scholar  

Piggott, J. R. Design questions in sensory and consumer science. Food Qual. Prefer. 3293 , 217–220 (1995).

Article   Google Scholar  

Kermit, M. & Lengard, V. Assessing the performance of a sensory panel-panellist monitoring and tracking. J. Chemom. 19 , 154–161 (2005).

Cook, D. J., Hollowood, T. A., Linforth, R. S. T. & Taylor, A. J. Correlating instrumental measurements of texture and flavour release with human perception. Int. J. Food Sci. Technol. 40 , 631–641 (2005).

Chinchanachokchai, S., Thontirawong, P. & Chinchanachokchai, P. A tale of two recommender systems: The moderating role of consumer expertise on artificial intelligence based product recommendations. J. Retail. Consum. Serv. 61 , 1–12 (2021).

Ross, C. F. Sensory science at the human-machine interface. Trends Food Sci. Technol. 20 , 63–72 (2009).

Chambers, E. IV & Koppel, K. Associations of volatile compounds with sensory aroma and flavor: The complex nature of flavor. Molecules 18 , 4887–4905 (2013).

Pinu, F. R. Metabolomics—The new frontier in food safety and quality research. Food Res. Int. 72 , 80–81 (2015).

Danezis, G. P., Tsagkaris, A. S., Brusic, V. & Georgiou, C. A. Food authentication: state of the art and prospects. Curr. Opin. Food Sci. 10 , 22–31 (2016).

Shepherd, G. M. Smell images and the flavour system in the human brain. Nature 444 , 316–321 (2006).

Meilgaard, M. C. Prediction of flavor differences between beers from their chemical composition. J. Agric. Food Chem. 30 , 1009–1017 (1982).

Xu, L. et al. Widespread receptor-driven modulation in peripheral olfactory coding. Science 368 , eaaz5390 (2020).

Kupferschmidt, K. Following the flavor. Science 340 , 808–809 (2013).

Billesbølle, C. B. et al. Structural basis of odorant recognition by a human odorant receptor. Nature 615 , 742–749 (2023).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Smith, B. Perspective: Complexities of flavour. Nature 486 , S6–S6 (2012).

Pfister, P. et al. Odorant receptor inhibition is fundamental to odor encoding. Curr. Biol. 30 , 2574–2587 (2020).

Moskowitz, H. W., Kumaraiah, V., Sharma, K. N., Jacobs, H. L. & Sharma, S. D. Cross-cultural differences in simple taste preferences. Science 190 , 1217–1218 (1975).

Eriksson, N. et al. A genetic variant near olfactory receptor genes influences cilantro preference. Flavour 1 , 22 (2012).

Ferdenzi, C. et al. Variability of affective responses to odors: Culture, gender, and olfactory knowledge. Chem. Senses 38 , 175–186 (2013).

Article   PubMed   Google Scholar  

Lawless, H. T. & Heymann, H. Sensory evaluation of food: Principles and practices. (Springer, New York, NY). https://doi.org/10.1007/978-1-4419-6488-5 (2010).

Colantonio, V. et al. Metabolomic selection for enhanced fruit flavor. Proc. Natl. Acad. Sci. 119 , e2115865119 (2022).

Fritz, F., Preissner, R. & Banerjee, P. VirtualTaste: a web server for the prediction of organoleptic properties of chemical compounds. Nucleic Acids Res 49 , W679–W684 (2021).

Tuwani, R., Wadhwa, S. & Bagler, G. BitterSweet: Building machine learning models for predicting the bitter and sweet taste of small molecules. Sci. Rep. 9 , 1–13 (2019).

Dagan-Wiener, A. et al. Bitter or not? BitterPredict, a tool for predicting taste from chemical structure. Sci. Rep. 7 , 1–13 (2017).

Pallante, L. et al. Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach. Sci. Rep. 12 , 1–11 (2022).

Malavolta, M. et al. A survey on computational taste predictors. Eur. Food Res. Technol. 248 , 2215–2235 (2022).

Lee, B. K. et al. A principal odor map unifies diverse tasks in olfactory perception. Science 381 , 999–1006 (2023).

Mayhew, E. J. et al. Transport features predict if a molecule is odorous. Proc. Natl. Acad. Sci. 119 , e2116576119 (2022).

Niu, Y. et al. Sensory evaluation of the synergism among ester odorants in light aroma-type liquor by odor threshold, aroma intensity and flash GC electronic nose. Food Res. Int. 113 , 102–114 (2018).

Yu, P., Low, M. Y. & Zhou, W. Design of experiments and regression modelling in food flavour and sensory analysis: A review. Trends Food Sci. Technol. 71 , 202–215 (2018).

Oladokun, O. et al. The impact of hop bitter acid and polyphenol profiles on the perceived bitterness of beer. Food Chem. 205 , 212–220 (2016).

Linforth, R., Cabannes, M., Hewson, L., Yang, N. & Taylor, A. Effect of fat content on flavor delivery during consumption: An in vivo model. J. Agric. Food Chem. 58 , 6905–6911 (2010).

Guo, S., Na Jom, K. & Ge, Y. Influence of roasting condition on flavor profile of sunflower seeds: A flavoromics approach. Sci. Rep. 9 , 11295 (2019).

Ren, Q. et al. The changes of microbial community and flavor compound in the fermentation process of Chinese rice wine using Fagopyrum tataricum grain as feedstock. Sci. Rep. 9 , 3365 (2019).

Hastie, T., Friedman, J. & Tibshirani, R. The Elements of Statistical Learning. (Springer, New York, NY). https://doi.org/10.1007/978-0-387-21606-5 (2001).

Dietz, C., Cook, D., Huismann, M., Wilson, C. & Ford, R. The multisensory perception of hop essential oil: a review. J. Inst. Brew. 126 , 320–342 (2020).

CAS   Google Scholar  

Roncoroni, Miguel & Verstrepen, Kevin Joan. Belgian Beer: Tested and Tasted. (Lannoo, 2018).

Meilgaard, M. Flavor chemistry of beer: Part II: Flavor and threshold of 239 aroma volatiles. in (1975).

Bokulich, N. A. & Bamforth, C. W. The microbiology of malting and brewing. Microbiol. Mol. Biol. Rev. MMBR 77 , 157–172 (2013).

Dzialo, M. C., Park, R., Steensels, J., Lievens, B. & Verstrepen, K. J. Physiology, ecology and industrial applications of aroma formation in yeast. FEMS Microbiol. Rev. 41 , S95–S128 (2017).

Article   PubMed   PubMed Central   Google Scholar  

Datta, A. et al. Computer-aided food engineering. Nat. Food 3 , 894–904 (2022).

American Society of Brewing Chemists. Beer Methods. (American Society of Brewing Chemists, St. Paul, MN, U.S.A.).

Olaniran, A. O., Hiralal, L., Mokoena, M. P. & Pillay, B. Flavour-active volatile compounds in beer: production, regulation and control. J. Inst. Brew. 123 , 13–23 (2017).

Verstrepen, K. J. et al. Flavor-active esters: Adding fruitiness to beer. J. Biosci. Bioeng. 96 , 110–118 (2003).

Meilgaard, M. C. Flavour chemistry of beer. part I: flavour interaction between principal volatiles. Master Brew. Assoc. Am. Tech. Q 12 , 107–117 (1975).

Briggs, D. E., Boulton, C. A., Brookes, P. A. & Stevens, R. Brewing 227–254. (Woodhead Publishing). https://doi.org/10.1533/9781855739062.227 (2004).

Bossaert, S., Crauwels, S., De Rouck, G. & Lievens, B. The power of sour - A review: Old traditions, new opportunities. BrewingScience 72 , 78–88 (2019).

Google Scholar  

Verstrepen, K. J. et al. Flavor active esters: Adding fruitiness to beer. J. Biosci. Bioeng. 96 , 110–118 (2003).

Snauwaert, I. et al. Microbial diversity and metabolite composition of Belgian red-brown acidic ales. Int. J. Food Microbiol. 221 , 1–11 (2016).

Spitaels, F. et al. The microbial diversity of traditional spontaneously fermented lambic beer. PLoS ONE 9 , e95384 (2014).

Blanco, C. A., Andrés-Iglesias, C. & Montero, O. Low-alcohol Beers: Flavor Compounds, Defects, and Improvement Strategies. Crit. Rev. Food Sci. Nutr. 56 , 1379–1388 (2016).

Jackowski, M. & Trusek, A. Non-Alcohol. beer Prod. – Overv. 20 , 32–38 (2018).

Takoi, K. et al. The contribution of geraniol metabolism to the citrus flavour of beer: Synergy of geraniol and β-citronellol under coexistence with excess linalool. J. Inst. Brew. 116 , 251–260 (2010).

Kroeze, J. H. & Bartoshuk, L. M. Bitterness suppression as revealed by split-tongue taste stimulation in humans. Physiol. Behav. 35 , 779–783 (1985).

Mennella, J. A. et al. A spoonful of sugar helps the medicine go down”: Bitter masking bysucrose among children and adults. Chem. Senses 40 , 17–25 (2015).

Wietstock, P., Kunz, T., Perreira, F. & Methner, F.-J. Metal chelation behavior of hop acids in buffered model systems. BrewingScience 69 , 56–63 (2016).

Sancho, D., Blanco, C. A., Caballero, I. & Pascual, A. Free iron in pale, dark and alcohol-free commercial lager beers. J. Sci. Food Agric. 91 , 1142–1147 (2011).

Rodrigues, H. & Parr, W. V. Contribution of cross-cultural studies to understanding wine appreciation: A review. Food Res. Int. 115 , 251–258 (2019).

Korneva, E. & Blockeel, H. Towards better evaluation of multi-target regression models. in ECML PKDD 2020 Workshops (eds. Koprinska, I. et al.) 353–362 (Springer International Publishing, Cham, 2020). https://doi.org/10.1007/978-3-030-65965-3_23 .

Gastón Ares. Mathematical and Statistical Methods in Food Science and Technology. (Wiley, 2013).

Grinsztajn, L., Oyallon, E. & Varoquaux, G. Why do tree-based models still outperform deep learning on tabular data? Preprint at http://arxiv.org/abs/2207.08815 (2022).

Gries, S. T. Statistics for Linguistics with R: A Practical Introduction. in Statistics for Linguistics with R (De Gruyter Mouton, 2021). https://doi.org/10.1515/9783110718256 .

Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2 , 56–67 (2020).

Ickes, C. M. & Cadwallader, K. R. Effects of ethanol on flavor perception in alcoholic beverages. Chemosens. Percept. 10 , 119–134 (2017).

Kato, M. et al. Influence of high molecular weight polypeptides on the mouthfeel of commercial beer. J. Inst. Brew. 127 , 27–40 (2021).

Wauters, R. et al. Novel Saccharomyces cerevisiae variants slow down the accumulation of staling aldehydes and improve beer shelf-life. Food Chem. 398 , 1–11 (2023).

Li, H., Jia, S. & Zhang, W. Rapid determination of low-level sulfur compounds in beer by headspace gas chromatography with a pulsed flame photometric detector. J. Am. Soc. Brew. Chem. 66 , 188–191 (2008).

Dercksen, A., Laurens, J., Torline, P., Axcell, B. C. & Rohwer, E. Quantitative analysis of volatile sulfur compounds in beer using a membrane extraction interface. J. Am. Soc. Brew. Chem. 54 , 228–233 (1996).

Molnar, C. Interpretable Machine Learning: A Guide for Making Black-Box Models Interpretable. (2020).

Zhao, Q. & Hastie, T. Causal interpretations of black-box models. J. Bus. Econ. Stat. Publ. Am. Stat. Assoc. 39 , 272–281 (2019).

Article   MathSciNet   Google Scholar  

Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning. (Springer, 2019).

Labrado, D. et al. Identification by NMR of key compounds present in beer distillates and residual phases after dealcoholization by vacuum distillation. J. Sci. Food Agric. 100 , 3971–3978 (2020).

Lusk, L. T., Kay, S. B., Porubcan, A. & Ryder, D. S. Key olfactory cues for beer oxidation. J. Am. Soc. Brew. Chem. 70 , 257–261 (2012).

Gonzalez Viejo, C., Torrico, D. D., Dunshea, F. R. & Fuentes, S. Development of artificial neural network models to assess beer acceptability based on sensory properties using a robotic pourer: A comparative model approach to achieve an artificial intelligence system. Beverages 5 , 33 (2019).

Gonzalez Viejo, C., Fuentes, S., Torrico, D. D., Godbole, A. & Dunshea, F. R. Chemical characterization of aromas in beer and their effect on consumers liking. Food Chem. 293 , 479–485 (2019).

Gilbert, J. L. et al. Identifying breeding priorities for blueberry flavor using biochemical, sensory, and genotype by environment analyses. PLOS ONE 10 , 1–21 (2015).

Goulet, C. et al. Role of an esterase in flavor volatile variation within the tomato clade. Proc. Natl. Acad. Sci. 109 , 19009–19014 (2012).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Borisov, V. et al. Deep Neural Networks and Tabular Data: A Survey. IEEE Trans. Neural Netw. Learn. Syst. 1–21 https://doi.org/10.1109/TNNLS.2022.3229161 (2022).

Statista. Statista Consumer Market Outlook: Beer - Worldwide.

Seitz, H. K. & Stickel, F. Molecular mechanisms of alcoholmediated carcinogenesis. Nat. Rev. Cancer 7 , 599–612 (2007).

Voordeckers, K. et al. Ethanol exposure increases mutation rate through error-prone polymerases. Nat. Commun. 11 , 3664 (2020).

Goelen, T. et al. Bacterial phylogeny predicts volatile organic compound composition and olfactory response of an aphid parasitoid. Oikos 129 , 1415–1428 (2020).

Article   ADS   Google Scholar  

Reher, T. et al. Evaluation of hop (Humulus lupulus) as a repellent for the management of Drosophila suzukii. Crop Prot. 124 , 104839 (2019).

Stein, S. E. An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data. J. Am. Soc. Mass Spectrom. 10 , 770–781 (1999).

American Society of Brewing Chemists. Sensory Analysis Methods. (American Society of Brewing Chemists, St. Paul, MN, U.S.A., 1992).

McAuley, J., Leskovec, J. & Jurafsky, D. Learning Attitudes and Attributes from Multi-Aspect Reviews. Preprint at https://doi.org/10.48550/arXiv.1210.3926 (2012).

Meilgaard, M. C., Carr, B. T. & Carr, B. T. Sensory Evaluation Techniques. (CRC Press, Boca Raton). https://doi.org/10.1201/b16452 (2014).

Schreurs, M. et al. Data from: Predicting and improving complex beer flavor through machine learning. Zenodo https://doi.org/10.5281/zenodo.10653704 (2024).

Download references

Acknowledgements

We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

Author information

These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

Authors and Affiliations

VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium

Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

AB InBev SA/NV, Brouwerijplein 1, B-3000, Leuven, Belgium

Philippe Malcorps & Luk Daenen

You can also search for this author in PubMed   Google Scholar

Contributions

S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

Corresponding author

Correspondence to Kevin J. Verstrepen .

Ethics declarations

Competing interests.

K.J.V. is affiliated with bar.on. The other authors declare no competing interests.

Peer review

Peer review information.

Nature Communications thanks Florian Bauer, Andrew John Macintosh and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, peer review file, description of additional supplementary files, supplementary data 1, supplementary data 2, supplementary data 3, supplementary data 4, supplementary data 5, supplementary data 6, supplementary data 7, reporting summary, source data, source data, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

Download citation

Received : 30 October 2023

Accepted : 21 February 2024

Published : 26 March 2024

DOI : https://doi.org/10.1038/s41467-024-46346-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Watch CBS News

A lawyer used ChatGPT to prepare a court filing. It went horribly awry.

By Megan Cerullo

Updated on: May 29, 2023 / 5:40 PM EDT / MoneyWatch

A lawyer who relied on ChatGPT to prepare a court filing on behalf of a man suing an airline is now all too familiar with the artificial intelligence tool's shortcomings — including its propensity to invent facts. 

Roberto Mata sued Colombian airline Avianca last year, alleging that a metal food and beverage cart injured his knee on a flight to Kennedy International Airport in New York. When Avianca asked a Manhattan judge to dismiss the lawsuit based on the statute of limitations, Mata's lawyer, Steven A. Schwartz, submitted a brief based on research done by ChatGPT, Schwartz, of the law firm Levidow, Levidow & Oberman, said in an affidavit . 

While ChatGPT can be useful to professionals in numerous industries, including the legal profession, it has proved itself to be both limited and unreliable. In this case, the AI invented court cases that didn't exist, and asserted that they were real.

The fabrications were revealed when Avianca's lawyers approached the case's judge, Kevin Castel of the Southern District of New York, saying they couldn't locate the cases cited in Mata's lawyers' brief in legal databases.

The made-up decisions included cases titled Martinez v. Delta Air Lines, Zicherman v. Korean Air Lines and Varghese v. China Southern Airlines.

"It seemed clear when we didn't recognize any of the cases in their opposition brief that something was amiss," Avianca's lawyer Bart Banino, of Condon & Forsyth, told CBS MoneyWatch. "We figured it was some sort of chatbot of some kind." 

Schwartz responded in an affidavit last week, saying he had "consulted" ChatGPT to "supplement" his legal research, and that the AI tool was "a source that has revealed itself to be unreliable." He added that it was the first time he'd used ChatGPT for work and "therefore was unaware of the possibility that its content could be false."

He said he even pressed the AI to confirm that the cases it cited were real. ChatGPT confirmed it was. Schwartz then asked the AI for its source. 

ChatGPT's response? "I apologize for the confusion earlier," it said. The AI then said the Varghese case could be located in the Westlaw and LexisNexis databases.

Judge Castel has set a hearing regarding the legal snafu for June 8 and has ordered Schwartz and the law firm Levidow, Levidow & Oberman to argue why they should not be sanctioned.

Levidow, Levidow & Oberman could not immediately be reached for comment. 

Megan Cerullo is a New York-based reporter for CBS MoneyWatch covering small business, workplace, health care, consumer spending and personal finance topics. She regularly appears on CBS News Streaming to discuss her reporting.

More from CBS News

Kiss sells its music catalog and brand for over $300 million

Hotel prices soar as tourists flock to see solar eclipse

Terrified of flying? British Airways says it can help.

3 reasons to invest in gold with prices high