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How to Write a Best Review Paper to Get More Citation

Review Paper Writing Guide

Table of contents

What is a review paper, difference between a review paper and a research paper.

• 6 Types of Review Papers

Purpose of Review Paper

Criteria for good review paper, step-by-step systematic procedure to write a review paper, title, abstract, keywords, introduction, various topics to discuss the critical issues, conclusion and future perspectives, acknowledgment       .

Are you new to academia? Do you want to learn how to write a good review paper to get in-depth knowledge about your domain? you are at the right place. In this article, you will learn how to write the best review paper in a step-by-step systematic procedure with a sample review article format to get more Citation.

A review paper, or a literature review , is a thorough, analytical examination of previously published literature. It also provides an overview of current research works on a particular topic in chronological order.

• The main objective of writing a review paper is to evaluate the existing data or results, which can be done through analysis, modeling, classification, comparison, and summary.  
• Review papers can help to identify the research gaps, to explore potential areas in a particular field.
• It helps to come out with new conclusions from already published works.
• Any scholar or researcher or scientist who wants to carry out research on a specific theme, first read the review articles relevant to that research area to understand the research gap for arriving at the problem statement.
• Writing a review article provides clarity, novelty, and contribution to the area of research and it demands a great level of in-depth understanding of the subject and a well-structured arrangement of discussions and arguments.
• Some journals publish only review papers, and they do not accept research articles. It is important to check the journal submission guidelines.

The difference between a review paper and a research paper is presented below.

6 T ypes of Review Papers

The review papers are classified in to six main categories based on the theme and it is presented in the figure below.

The purpose of a review paper is to assess a particular research question, theoretical or practical approach which provides readers with in-depth knowledge and state-of-the-art understanding of the research area.

The purpose of the review paper can vary based on their specific type and research needs.

• Provide a unified, collective overview of the current state of knowledge on a specific research topic and provide an inclusive foundation on a research theme.
• Identify ambiguity, and contradictions in existing results or data.
• Highlight the existing methodological approaches, research techniques, and unique perceptions.
• Develop theoretical outlines to resolve and work on published research.
• Discuss research gaps and future perspectives.

A good review paper needs to achieve three important criteria. ( Palmatier et al 2017 ).

• First, the area of research should be suitable for writing a review paper so that the author finds sufficient published literature.
• The review paper should be written with suitable literature, detailed discussion, sufficient data/results to support the interpretation, and persuasive language style.
• A completed review paper should provide substantial new innovative ideas to the readers based on the comparison of published works.

Review papers are widely read by many researchers and it helps to get more citations for author. So, it is important to learn how to write a review paper and find a journal to publish .

Time needed:  20 days and 7 hours

The systematic procedural steps to write the best review paper are as follows:

Select a suitable area in your research field formulate clear objectives, and prepare the specific research hypotheses that are to be explored.

Designing your research work is an important step for any researcher. Based on the objectives, develop a clear methodology or protocol to review a review paper.

Thorough analysis and understanding of different published works help the author to identify suitable and relevant data/results that will be used to write the paper.

The degree of analysis to evaluate the collected data varies by extensive review. The examination of trends, patterns, ideas, comparisons, and relationships in the study provides deeper knowledge on that area of research .

Interpretation of results is very important for a good review paper. The author should present the discussion systematically without any ambiguity. The results can be presented in descriptive form, tables, and figures. The new insights should have an in-depth discussion of the topic in line with fundamentals. Finally, the author is expected to present the limitations of the existing study with future perspectives.

Sample Review Article Format

Write an effective and suitable title, abstract, and keywords relevant to your review paper. This will maximize the visibility of your paper online for the readers to find your work. Your title and abstract should be clear, concise, appropriate, and informative.

Present a detailed introduction to your research which is published in chronological order in your own words. Don’t summarize the published literature. The introduction should encourage the readers to read your paper.

Make sure you present a critical discussion, not a descriptive summary of the topic. If there is contradictory research in your area of research, verify to include an element of debate and present both sides of the argument. A good review paper can resolve the conflict between contradictory works.

The written review paper should achieve your objectives. Hence, the review paper should leave the reader with a clear understanding of the following questions:

What they can understand from the review paper?

What still remains a requirement of further investigation in the research area?

This can include making suggestions for future scope on the theme as part of your conclusion.

The authors can submit a brief acknowledgment of any financial, instrumentation, and academic support received about research work.

Citing references at appropriate places in the article is necessary and important to avoid plagiarism. Each journal has its referencing style. Therefore, the references need to be listed at the end of the manuscript. The number of references in the review paper is usually higher than in a research paper .

I hope this article will give you a clear idea of how to write a review paper. Please give your valuable comments.

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• How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

• Search for relevant literature
• Evaluate sources
• Identify themes, debates, and gaps
• Outline the structure
• Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

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Table of contents

What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

• Quick Run-through
• Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

• Demonstrate your familiarity with the topic and its scholarly context
• Develop a theoretical framework and methodology for your research
• Position your work in relation to other researchers and theorists
• Show how your research addresses a gap or contributes to a debate
• Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

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Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

• Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
• Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
• Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
• Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

• Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
• Body image, self-perception, self-esteem, mental health
• Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

• Your university’s library catalogue
• Google Scholar
• Project Muse (humanities and social sciences)
• Medline (life sciences and biomedicine)
• EconLit (economics)
• Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

• What question or problem is the author addressing?
• What are the key concepts and how are they defined?
• What are the key theories, models, and methods?
• Does the research use established frameworks or take an innovative approach?
• What are the results and conclusions of the study?
• How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
• What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

Prevent plagiarism. Run a free check.

To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

• Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
• Themes: what questions or concepts recur across the literature?
• Debates, conflicts and contradictions: where do sources disagree?
• Pivotal publications: are there any influential theories or studies that changed the direction of the field?
• Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

• Most research has focused on young women.
• There is an increasing interest in the visual aspects of social media.
• But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

• Look at what results have emerged in qualitative versus quantitative research
• Discuss how the topic has been approached by empirical versus theoretical scholarship
• Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

• Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically evaluate: mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

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A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarize yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

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A Step-by-Step Guide to Writing a Scientific Review Article

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Manisha Bahl, A Step-by-Step Guide to Writing a Scientific Review Article, Journal of Breast Imaging , Volume 5, Issue 4, July/August 2023, Pages 480–485, https://doi.org/10.1093/jbi/wbad028

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Scientific review articles are comprehensive, focused reviews of the scientific literature written by subject matter experts. The task of writing a scientific review article can seem overwhelming; however, it can be managed by using an organized approach and devoting sufficient time to the process. The process involves selecting a topic about which the authors are knowledgeable and enthusiastic, conducting a literature search and critical analysis of the literature, and writing the article, which is composed of an abstract, introduction, body, and conclusion, with accompanying tables and figures. This article, which focuses on the narrative or traditional literature review, is intended to serve as a guide with practical steps for new writers. Tips for success are also discussed, including selecting a focused topic, maintaining objectivity and balance while writing, avoiding tedious data presentation in a laundry list format, moving from descriptions of the literature to critical analysis, avoiding simplistic conclusions, and budgeting time for the overall process.

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How to write a good scientific review article

Affiliation.

• 1 The FEBS Journal Editorial Office, Cambridge, UK.
• PMID: 35792782
• DOI: 10.1111/febs.16565

Literature reviews are valuable resources for the scientific community. With research accelerating at an unprecedented speed in recent years and more and more original papers being published, review articles have become increasingly important as a means to keep up to date with developments in a particular area of research. A good review article provides readers with an in-depth understanding of a field and highlights key gaps and challenges to address with future research. Writing a review article also helps to expand the writer's knowledge of their specialist area and to develop their analytical and communication skills, amongst other benefits. Thus, the importance of building review-writing into a scientific career cannot be overstated. In this instalment of The FEBS Journal's Words of Advice series, I provide detailed guidance on planning and writing an informative and engaging literature review.

© 2022 Federation of European Biochemical Societies.

Publication types

An editor’s guide to writing a review article

Lindsey Drayton

Matt Pavlovich

About this video

Writing a compelling review article is about more than picking an interesting topic and gathering the latest references. It’s an opportunity to share your views on the most recent trends in the area, discuss which hypotheses seem best supported or which technologies seem most promising, and even chart a course for how the field could develop in the future.

Matt Pavlovich and Lindsey Drayton, editors in the Trends reviews journals group with Cell Press, will give their editorial perspective on what they’re looking for in a review. This webinar will cover how to both conceptualize and write a review, how to distinguish your review by making a strong statement, and why writing a review is worth your time. It will also dispel some common myths about review articles—including that reviews must always originate from an editor’s invitation—and give advice for how to propose a review to an editor.

You will come away with a stronger understanding of how to plan and structure a review article, specific writing tips for writing the article itself, why writing a review is a good use of your time and what distinguishes an adequate review from an excellent one.

About the presenters

Editor, Trends in Cognitive Sciences

Lindsey Drayton is the editor of Trends in Cognitive Sciences, Cell Press’s home for reviews in cognitive psychology and cognitive neuroscience. She earned her BA in Psychology from Duke University and her PhD in Psychology from Yale University. At Yale, she studied the evolution and ontology of human social cognition using a variety of model primate species. She joined Cell Press in 2017. 

Editor, Trends in Biotechnology

Matt Pavlovich is the editor of Trends in Biotechnology, Cell Press’s home for reviews in applied biology. He earned his BS in chemical engineering from the Georgia Institute of Technology and his PhD in chemical engineering from the University of California at Berkeley, where his thesis project focused on the biological effects of air plasmas. He studied analytical chemistry as a postdoctoral researcher at Northeastern University, then joined Cell Press at the start of 2016.

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Ten Simple Rules for Writing a Literature Review

Marco pautasso.

1 Centre for Functional and Evolutionary Ecology (CEFE), CNRS, Montpellier, France

2 Centre for Biodiversity Synthesis and Analysis (CESAB), FRB, Aix-en-Provence, France

Literature reviews are in great demand in most scientific fields. Their need stems from the ever-increasing output of scientific publications [1] . For example, compared to 1991, in 2008 three, eight, and forty times more papers were indexed in Web of Science on malaria, obesity, and biodiversity, respectively [2] . Given such mountains of papers, scientists cannot be expected to examine in detail every single new paper relevant to their interests [3] . Thus, it is both advantageous and necessary to rely on regular summaries of the recent literature. Although recognition for scientists mainly comes from primary research, timely literature reviews can lead to new synthetic insights and are often widely read [4] . For such summaries to be useful, however, they need to be compiled in a professional way [5] .

When starting from scratch, reviewing the literature can require a titanic amount of work. That is why researchers who have spent their career working on a certain research issue are in a perfect position to review that literature. Some graduate schools are now offering courses in reviewing the literature, given that most research students start their project by producing an overview of what has already been done on their research issue [6] . However, it is likely that most scientists have not thought in detail about how to approach and carry out a literature review.

Reviewing the literature requires the ability to juggle multiple tasks, from finding and evaluating relevant material to synthesising information from various sources, from critical thinking to paraphrasing, evaluating, and citation skills [7] . In this contribution, I share ten simple rules I learned working on about 25 literature reviews as a PhD and postdoctoral student. Ideas and insights also come from discussions with coauthors and colleagues, as well as feedback from reviewers and editors.

Rule 1: Define a Topic and Audience

How to choose which topic to review? There are so many issues in contemporary science that you could spend a lifetime of attending conferences and reading the literature just pondering what to review. On the one hand, if you take several years to choose, several other people may have had the same idea in the meantime. On the other hand, only a well-considered topic is likely to lead to a brilliant literature review [8] . The topic must at least be:

• interesting to you (ideally, you should have come across a series of recent papers related to your line of work that call for a critical summary),
• an important aspect of the field (so that many readers will be interested in the review and there will be enough material to write it), and
• a well-defined issue (otherwise you could potentially include thousands of publications, which would make the review unhelpful).

Ideas for potential reviews may come from papers providing lists of key research questions to be answered [9] , but also from serendipitous moments during desultory reading and discussions. In addition to choosing your topic, you should also select a target audience. In many cases, the topic (e.g., web services in computational biology) will automatically define an audience (e.g., computational biologists), but that same topic may also be of interest to neighbouring fields (e.g., computer science, biology, etc.).

Rule 2: Search and Re-search the Literature

After having chosen your topic and audience, start by checking the literature and downloading relevant papers. Five pieces of advice here:

• keep track of the search items you use (so that your search can be replicated [10] ),
• keep a list of papers whose pdfs you cannot access immediately (so as to retrieve them later with alternative strategies),
• use a paper management system (e.g., Mendeley, Papers, Qiqqa, Sente),
• define early in the process some criteria for exclusion of irrelevant papers (these criteria can then be described in the review to help define its scope), and
• do not just look for research papers in the area you wish to review, but also seek previous reviews.

The chances are high that someone will already have published a literature review ( Figure 1 ), if not exactly on the issue you are planning to tackle, at least on a related topic. If there are already a few or several reviews of the literature on your issue, my advice is not to give up, but to carry on with your own literature review,

The bottom-right situation (many literature reviews but few research papers) is not just a theoretical situation; it applies, for example, to the study of the impacts of climate change on plant diseases, where there appear to be more literature reviews than research studies [33] .

• discussing in your review the approaches, limitations, and conclusions of past reviews,
• trying to find a new angle that has not been covered adequately in the previous reviews, and
• incorporating new material that has inevitably accumulated since their appearance.

When searching the literature for pertinent papers and reviews, the usual rules apply:

• be thorough,
• use different keywords and database sources (e.g., DBLP, Google Scholar, ISI Proceedings, JSTOR Search, Medline, Scopus, Web of Science), and
• look at who has cited past relevant papers and book chapters.

Rule 3: Take Notes While Reading

If you read the papers first, and only afterwards start writing the review, you will need a very good memory to remember who wrote what, and what your impressions and associations were while reading each single paper. My advice is, while reading, to start writing down interesting pieces of information, insights about how to organize the review, and thoughts on what to write. This way, by the time you have read the literature you selected, you will already have a rough draft of the review.

Of course, this draft will still need much rewriting, restructuring, and rethinking to obtain a text with a coherent argument [11] , but you will have avoided the danger posed by staring at a blank document. Be careful when taking notes to use quotation marks if you are provisionally copying verbatim from the literature. It is advisable then to reformulate such quotes with your own words in the final draft. It is important to be careful in noting the references already at this stage, so as to avoid misattributions. Using referencing software from the very beginning of your endeavour will save you time.

Rule 4: Choose the Type of Review You Wish to Write

After having taken notes while reading the literature, you will have a rough idea of the amount of material available for the review. This is probably a good time to decide whether to go for a mini- or a full review. Some journals are now favouring the publication of rather short reviews focusing on the last few years, with a limit on the number of words and citations. A mini-review is not necessarily a minor review: it may well attract more attention from busy readers, although it will inevitably simplify some issues and leave out some relevant material due to space limitations. A full review will have the advantage of more freedom to cover in detail the complexities of a particular scientific development, but may then be left in the pile of the very important papers “to be read” by readers with little time to spare for major monographs.

There is probably a continuum between mini- and full reviews. The same point applies to the dichotomy of descriptive vs. integrative reviews. While descriptive reviews focus on the methodology, findings, and interpretation of each reviewed study, integrative reviews attempt to find common ideas and concepts from the reviewed material [12] . A similar distinction exists between narrative and systematic reviews: while narrative reviews are qualitative, systematic reviews attempt to test a hypothesis based on the published evidence, which is gathered using a predefined protocol to reduce bias [13] , [14] . When systematic reviews analyse quantitative results in a quantitative way, they become meta-analyses. The choice between different review types will have to be made on a case-by-case basis, depending not just on the nature of the material found and the preferences of the target journal(s), but also on the time available to write the review and the number of coauthors [15] .

Rule 5: Keep the Review Focused, but Make It of Broad Interest

Whether your plan is to write a mini- or a full review, it is good advice to keep it focused 16 , 17 . Including material just for the sake of it can easily lead to reviews that are trying to do too many things at once. The need to keep a review focused can be problematic for interdisciplinary reviews, where the aim is to bridge the gap between fields [18] . If you are writing a review on, for example, how epidemiological approaches are used in modelling the spread of ideas, you may be inclined to include material from both parent fields, epidemiology and the study of cultural diffusion. This may be necessary to some extent, but in this case a focused review would only deal in detail with those studies at the interface between epidemiology and the spread of ideas.

While focus is an important feature of a successful review, this requirement has to be balanced with the need to make the review relevant to a broad audience. This square may be circled by discussing the wider implications of the reviewed topic for other disciplines.

Rule 6: Be Critical and Consistent

Reviewing the literature is not stamp collecting. A good review does not just summarize the literature, but discusses it critically, identifies methodological problems, and points out research gaps [19] . After having read a review of the literature, a reader should have a rough idea of:

• the major achievements in the reviewed field,
• the main areas of debate, and
• the outstanding research questions.

It is challenging to achieve a successful review on all these fronts. A solution can be to involve a set of complementary coauthors: some people are excellent at mapping what has been achieved, some others are very good at identifying dark clouds on the horizon, and some have instead a knack at predicting where solutions are going to come from. If your journal club has exactly this sort of team, then you should definitely write a review of the literature! In addition to critical thinking, a literature review needs consistency, for example in the choice of passive vs. active voice and present vs. past tense.

Rule 7: Find a Logical Structure

Like a well-baked cake, a good review has a number of telling features: it is worth the reader's time, timely, systematic, well written, focused, and critical. It also needs a good structure. With reviews, the usual subdivision of research papers into introduction, methods, results, and discussion does not work or is rarely used. However, a general introduction of the context and, toward the end, a recapitulation of the main points covered and take-home messages make sense also in the case of reviews. For systematic reviews, there is a trend towards including information about how the literature was searched (database, keywords, time limits) [20] .

How can you organize the flow of the main body of the review so that the reader will be drawn into and guided through it? It is generally helpful to draw a conceptual scheme of the review, e.g., with mind-mapping techniques. Such diagrams can help recognize a logical way to order and link the various sections of a review [21] . This is the case not just at the writing stage, but also for readers if the diagram is included in the review as a figure. A careful selection of diagrams and figures relevant to the reviewed topic can be very helpful to structure the text too [22] .

Rule 8: Make Use of Feedback

Reviews of the literature are normally peer-reviewed in the same way as research papers, and rightly so [23] . As a rule, incorporating feedback from reviewers greatly helps improve a review draft. Having read the review with a fresh mind, reviewers may spot inaccuracies, inconsistencies, and ambiguities that had not been noticed by the writers due to rereading the typescript too many times. It is however advisable to reread the draft one more time before submission, as a last-minute correction of typos, leaps, and muddled sentences may enable the reviewers to focus on providing advice on the content rather than the form.

Feedback is vital to writing a good review, and should be sought from a variety of colleagues, so as to obtain a diversity of views on the draft. This may lead in some cases to conflicting views on the merits of the paper, and on how to improve it, but such a situation is better than the absence of feedback. A diversity of feedback perspectives on a literature review can help identify where the consensus view stands in the landscape of the current scientific understanding of an issue [24] .

Rule 9: Include Your Own Relevant Research, but Be Objective

In many cases, reviewers of the literature will have published studies relevant to the review they are writing. This could create a conflict of interest: how can reviewers report objectively on their own work [25] ? Some scientists may be overly enthusiastic about what they have published, and thus risk giving too much importance to their own findings in the review. However, bias could also occur in the other direction: some scientists may be unduly dismissive of their own achievements, so that they will tend to downplay their contribution (if any) to a field when reviewing it.

In general, a review of the literature should neither be a public relations brochure nor an exercise in competitive self-denial. If a reviewer is up to the job of producing a well-organized and methodical review, which flows well and provides a service to the readership, then it should be possible to be objective in reviewing one's own relevant findings. In reviews written by multiple authors, this may be achieved by assigning the review of the results of a coauthor to different coauthors.

Rule 10: Be Up-to-Date, but Do Not Forget Older Studies

Given the progressive acceleration in the publication of scientific papers, today's reviews of the literature need awareness not just of the overall direction and achievements of a field of inquiry, but also of the latest studies, so as not to become out-of-date before they have been published. Ideally, a literature review should not identify as a major research gap an issue that has just been addressed in a series of papers in press (the same applies, of course, to older, overlooked studies (“sleeping beauties” [26] )). This implies that literature reviewers would do well to keep an eye on electronic lists of papers in press, given that it can take months before these appear in scientific databases. Some reviews declare that they have scanned the literature up to a certain point in time, but given that peer review can be a rather lengthy process, a full search for newly appeared literature at the revision stage may be worthwhile. Assessing the contribution of papers that have just appeared is particularly challenging, because there is little perspective with which to gauge their significance and impact on further research and society.

Inevitably, new papers on the reviewed topic (including independently written literature reviews) will appear from all quarters after the review has been published, so that there may soon be the need for an updated review. But this is the nature of science [27] – [32] . I wish everybody good luck with writing a review of the literature.

Acknowledgments

Many thanks to M. Barbosa, K. Dehnen-Schmutz, T. Döring, D. Fontaneto, M. Garbelotto, O. Holdenrieder, M. Jeger, D. Lonsdale, A. MacLeod, P. Mills, M. Moslonka-Lefebvre, G. Stancanelli, P. Weisberg, and X. Xu for insights and discussions, and to P. Bourne, T. Matoni, and D. Smith for helpful comments on a previous draft.

Funding Statement

This work was funded by the French Foundation for Research on Biodiversity (FRB) through its Centre for Synthesis and Analysis of Biodiversity data (CESAB), as part of the NETSEED research project. The funders had no role in the preparation of the manuscript.

Page Content

Overview of the review report format, the first read-through, first read considerations, spotting potential major flaws, concluding the first reading, rejection after the first reading, before starting the second read-through, doing the second read-through, the second read-through: section by section guidance, how to structure your report, on presentation and style, criticisms & confidential comments to editors, the recommendation, when recommending rejection, additional resources, step by step guide to reviewing a manuscript.

When you receive an invitation to peer review, you should be sent a copy of the paper's abstract to help you decide whether you wish to do the review. Try to respond to invitations promptly - it will prevent delays. It is also important at this stage to declare any potential Conflict of Interest.

The structure of the review report varies between journals. Some follow an informal structure, while others have a more formal approach.

" Number your comments!!! " (Jonathon Halbesleben, former Editor of Journal of Occupational and Organizational Psychology)

Informal Structure

Many journals don't provide criteria for reviews beyond asking for your 'analysis of merits'. In this case, you may wish to familiarize yourself with examples of other reviews done for the journal, which the editor should be able to provide or, as you gain experience, rely on your own evolving style.

Formal Structure

Other journals require a more formal approach. Sometimes they will ask you to address specific questions in your review via a questionnaire. Or they might want you to rate the manuscript on various attributes using a scorecard. Often you can't see these until you log in to submit your review. So when you agree to the work, it's worth checking for any journal-specific guidelines and requirements. If there are formal guidelines, let them direct the structure of your review.

In Both Cases

Whether specifically required by the reporting format or not, you should expect to compile comments to authors and possibly confidential ones to editors only.

Following the invitation to review, when you'll have received the article abstract, you should already understand the aims, key data and conclusions of the manuscript. If you don't, make a note now that you need to feedback on how to improve those sections.

The first read-through is a skim-read. It will help you form an initial impression of the paper and get a sense of whether your eventual recommendation will be to accept or reject the paper.

Keep a pen and paper handy when skim-reading.

Try to bear in mind the following questions - they'll help you form your overall impression:

• What is the main question addressed by the research? Is it relevant and interesting?
• How original is the topic? What does it add to the subject area compared with other published material?
• Is the paper well written? Is the text clear and easy to read?
• Are the conclusions consistent with the evidence and arguments presented? Do they address the main question posed?
• If the author is disagreeing significantly with the current academic consensus, do they have a substantial case? If not, what would be required to make their case credible?
• If the paper includes tables or figures, what do they add to the paper? Do they aid understanding or are they superfluous?

While you should read the whole paper, making the right choice of what to read first can save time by flagging major problems early on.

Editors say, " Specific recommendations for remedying flaws are VERY welcome ."

Examples of possibly major flaws include:

• Drawing a conclusion that is contradicted by the author's own statistical or qualitative evidence
• The use of a discredited method
• Ignoring a process that is known to have a strong influence on the area under study

If experimental design features prominently in the paper, first check that the methodology is sound - if not, this is likely to be a major flaw.

You might examine:

• The sampling in analytical papers
• The sufficient use of control experiments
• The precision of process data
• The regularity of sampling in time-dependent studies
• The validity of questions, the use of a detailed methodology and the data analysis being done systematically (in qualitative research)
• That qualitative research extends beyond the author's opinions, with sufficient descriptive elements and appropriate quotes from interviews or focus groups

Major Flaws in Information

If methodology is less of an issue, it's often a good idea to look at the data tables, figures or images first. Especially in science research, it's all about the information gathered. If there are critical flaws in this, it's very likely the manuscript will need to be rejected. Such issues include:

• Insufficient data
• Unclear data tables
• Contradictory data that either are not self-consistent or disagree with the conclusions
• Confirmatory data that adds little, if anything, to current understanding - unless strong arguments for such repetition are made

If you find a major problem, note your reasoning and clear supporting evidence (including citations).

After the initial read and using your notes, including those of any major flaws you found, draft the first two paragraphs of your review - the first summarizing the research question addressed and the second the contribution of the work. If the journal has a prescribed reporting format, this draft will still help you compose your thoughts.

The First Paragraph

This should state the main question addressed by the research and summarize the goals, approaches, and conclusions of the paper. It should:

• Help the editor properly contextualize the research and add weight to your judgement
• Show the author what key messages are conveyed to the reader, so they can be sure they are achieving what they set out to do
• Focus on successful aspects of the paper so the author gets a sense of what they've done well

The Second Paragraph

This should provide a conceptual overview of the contribution of the research. So consider:

• Is the paper's premise interesting and important?
• Are the methods used appropriate?
• Do the data support the conclusions?

After drafting these two paragraphs, you should be in a position to decide whether this manuscript is seriously flawed and should be rejected (see the next section). Or whether it is publishable in principle and merits a detailed, careful read through.

Even if you are coming to the opinion that an article has serious flaws, make sure you read the whole paper. This is very important because you may find some really positive aspects that can be communicated to the author. This could help them with future submissions.

A full read-through will also make sure that any initial concerns are indeed correct and fair. After all, you need the context of the whole paper before deciding to reject. If you still intend to recommend rejection, see the section "When recommending rejection."

Once the paper has passed your first read and you've decided the article is publishable in principle, one purpose of the second, detailed read-through is to help prepare the manuscript for publication. You may still decide to recommend rejection following a second reading.

" Offer clear suggestions for how the authors can address the concerns raised. In other words, if you're going to raise a problem, provide a solution ." (Jonathon Halbesleben, Editor of Journal of Occupational and Organizational Psychology)

Preparation

To save time and simplify the review:

• Don't rely solely upon inserting comments on the manuscript document - make separate notes
• Try to group similar concerns or praise together
• If using a review program to note directly onto the manuscript, still try grouping the concerns and praise in separate notes - it helps later
• Note line numbers of text upon which your notes are based - this helps you find items again and also aids those reading your review

Now that you have completed your preparations, you're ready to spend an hour or so reading carefully through the manuscript.

As you're reading through the manuscript for a second time, you'll need to keep in mind the argument's construction, the clarity of the language and content.

With regard to the argument’s construction, you should identify:

• Any places where the meaning is unclear or ambiguous
• Any factual errors
• Any invalid arguments

You may also wish to consider:

• Does the title properly reflect the subject of the paper?
• Does the abstract provide an accessible summary of the paper?
• Do the keywords accurately reflect the content?
• Is the paper an appropriate length?
• Are the key messages short, accurate and clear?

Not every submission is well written. Part of your role is to make sure that the text’s meaning is clear.

Editors say, " If a manuscript has many English language and editing issues, please do not try and fix it. If it is too bad, note that in your review and it should be up to the authors to have the manuscript edited ."

If the article is difficult to understand, you should have rejected it already. However, if the language is poor but you understand the core message, see if you can suggest improvements to fix the problem:

• Are there certain aspects that could be communicated better, such as parts of the discussion?
• Should the authors consider resubmitting to the same journal after language improvements?
• Would you consider looking at the paper again once these issues are dealt with?

On Grammar and Punctuation

Your primary role is judging the research content. Don't spend time polishing grammar or spelling. Editors will make sure that the text is at a high standard before publication. However, if you spot grammatical errors that affect clarity of meaning, then it's important to highlight these. Expect to suggest such amendments - it's rare for a manuscript to pass review with no corrections.

A 2010 study of nursing journals found that 79% of recommendations by reviewers were influenced by grammar and writing style (Shattel, et al., 2010).

1. The Introduction

A well-written introduction:

• Sets out the argument
• Summarizes recent research related to the topic
• Highlights gaps in current understanding or conflicts in current knowledge
• Establishes the originality of the research aims by demonstrating the need for investigations in the topic area
• Gives a clear idea of the target readership, why the research was carried out and the novelty and topicality of the manuscript

Originality and Topicality

Originality and topicality can only be established in the light of recent authoritative research. For example, it's impossible to argue that there is a conflict in current understanding by referencing articles that are 10 years old.

Authors may make the case that a topic hasn't been investigated in several years and that new research is required. This point is only valid if researchers can point to recent developments in data gathering techniques or to research in indirectly related fields that suggest the topic needs revisiting. Clearly, authors can only do this by referencing recent literature. Obviously, where older research is seminal or where aspects of the methodology rely upon it, then it is perfectly appropriate for authors to cite some older papers.

Editors say, "Is the report providing new information; is it novel or just confirmatory of well-known outcomes ?"

It's common for the introduction to end by stating the research aims. By this point you should already have a good impression of them - if the explicit aims come as a surprise, then the introduction needs improvement.

2. Materials and Methods

Academic research should be replicable, repeatable and robust - and follow best practice.

Replicable Research

This makes sufficient use of:

• Control experiments
• Repeated analyses
• Repeated experiments

These are used to make sure observed trends are not due to chance and that the same experiment could be repeated by other researchers - and result in the same outcome. Statistical analyses will not be sound if methods are not replicable. Where research is not replicable, the paper should be recommended for rejection.

Repeatable Methods

These give enough detail so that other researchers are able to carry out the same research. For example, equipment used or sampling methods should all be described in detail so that others could follow the same steps. Where methods are not detailed enough, it's usual to ask for the methods section to be revised.

Robust Research

This has enough data points to make sure the data are reliable. If there are insufficient data, it might be appropriate to recommend revision. You should also consider whether there is any in-built bias not nullified by the control experiments.

Best Practice

During these checks you should keep in mind best practice:

• Standard guidelines were followed (e.g. the CONSORT Statement for reporting randomized trials)
• The health and safety of all participants in the study was not compromised
• Ethical standards were maintained

If the research fails to reach relevant best practice standards, it's usual to recommend rejection. What's more, you don't then need to read any further.

3. Results and Discussion

This section should tell a coherent story - What happened? What was discovered or confirmed?

Certain patterns of good reporting need to be followed by the author:

• They should start by describing in simple terms what the data show
• They should make reference to statistical analyses, such as significance or goodness of fit
• Once described, they should evaluate the trends observed and explain the significance of the results to wider understanding. This can only be done by referencing published research
• The outcome should be a critical analysis of the data collected

Discussion should always, at some point, gather all the information together into a single whole. Authors should describe and discuss the overall story formed. If there are gaps or inconsistencies in the story, they should address these and suggest ways future research might confirm the findings or take the research forward.

4. Conclusions

This section is usually no more than a few paragraphs and may be presented as part of the results and discussion, or in a separate section. The conclusions should reflect upon the aims - whether they were achieved or not - and, just like the aims, should not be surprising. If the conclusions are not evidence-based, it's appropriate to ask for them to be re-written.

5. Information Gathered: Images, Graphs and Data Tables

If you find yourself looking at a piece of information from which you cannot discern a story, then you should ask for improvements in presentation. This could be an issue with titles, labels, statistical notation or image quality.

Where information is clear, you should check that:

• The results seem plausible, in case there is an error in data gathering
• The trends you can see support the paper's discussion and conclusions
• There are sufficient data. For example, in studies carried out over time are there sufficient data points to support the trends described by the author?

You should also check whether images have been edited or manipulated to emphasize the story they tell. This may be appropriate but only if authors report on how the image has been edited (e.g. by highlighting certain parts of an image). Where you feel that an image has been edited or manipulated without explanation, you should highlight this in a confidential comment to the editor in your report.

6. List of References

You will need to check referencing for accuracy, adequacy and balance.

Where a cited article is central to the author's argument, you should check the accuracy and format of the reference - and bear in mind different subject areas may use citations differently. Otherwise, it's the editor’s role to exhaustively check the reference section for accuracy and format.

You should consider if the referencing is adequate:

• Are important parts of the argument poorly supported?
• Are there published studies that show similar or dissimilar trends that should be discussed?
• If a manuscript only uses half the citations typical in its field, this may be an indicator that referencing should be improved - but don't be guided solely by quantity
• References should be relevant, recent and readily retrievable

Check for a well-balanced list of references that is:

• Helpful to the reader
• Fair to competing authors
• Not over-reliant on self-citation
• Gives due recognition to the initial discoveries and related work that led to the work under assessment

You should be able to evaluate whether the article meets the criteria for balanced referencing without looking up every reference.

7. Plagiarism

By now you will have a deep understanding of the paper's content - and you may have some concerns about plagiarism.

Identified Concern

If you find - or already knew of - a very similar paper, this may be because the author overlooked it in their own literature search. Or it may be because it is very recent or published in a journal slightly outside their usual field.

You may feel you can advise the author how to emphasize the novel aspects of their own study, so as to better differentiate it from similar research. If so, you may ask the author to discuss their aims and results, or modify their conclusions, in light of the similar article. Of course, the research similarities may be so great that they render the work unoriginal and you have no choice but to recommend rejection.

"It's very helpful when a reviewer can point out recent similar publications on the same topic by other groups, or that the authors have already published some data elsewhere ." (Editor feedback)

Suspected Concern

If you suspect plagiarism, including self-plagiarism, but cannot recall or locate exactly what is being plagiarized, notify the editor of your suspicion and ask for guidance.

Most editors have access to software that can check for plagiarism.

Editors are not out to police every paper, but when plagiarism is discovered during peer review it can be properly addressed ahead of publication. If plagiarism is discovered only after publication, the consequences are worse for both authors and readers, because a retraction may be necessary.

For detailed guidelines see COPE's Ethical guidelines for reviewers and Wiley's Best Practice Guidelines on Publishing Ethics .

8. Search Engine Optimization (SEO)

After the detailed read-through, you will be in a position to advise whether the title, abstract and key words are optimized for search purposes. In order to be effective, good SEO terms will reflect the aims of the research.

A clear title and abstract will improve the paper's search engine rankings and will influence whether the user finds and then decides to navigate to the main article. The title should contain the relevant SEO terms early on. This has a major effect on the impact of a paper, since it helps it appear in search results. A poor abstract can then lose the reader's interest and undo the benefit of an effective title - whilst the paper's abstract may appear in search results, the potential reader may go no further.

So ask yourself, while the abstract may have seemed adequate during earlier checks, does it:

• Do justice to the manuscript in this context?
• Highlight important findings sufficiently?
• Present the most interesting data?

Editors say, " Does the Abstract highlight the important findings of the study ?"

If there is a formal report format, remember to follow it. This will often comprise a range of questions followed by comment sections. Try to answer all the questions. They are there because the editor felt that they are important. If you're following an informal report format you could structure your report in three sections: summary, major issues, minor issues.

• Give positive feedback first. Authors are more likely to read your review if you do so. But don't overdo it if you will be recommending rejection
• Briefly summarize what the paper is about and what the findings are
• Try to put the findings of the paper into the context of the existing literature and current knowledge
• Indicate the significance of the work and if it is novel or mainly confirmatory
• Indicate the work's strengths, its quality and completeness
• State any major flaws or weaknesses and note any special considerations. For example, if previously held theories are being overlooked

Major Issues

• Are there any major flaws? State what they are and what the severity of their impact is on the paper
• Has similar work already been published without the authors acknowledging this?
• Are the authors presenting findings that challenge current thinking? Is the evidence they present strong enough to prove their case? Have they cited all the relevant work that would contradict their thinking and addressed it appropriately?
• If major revisions are required, try to indicate clearly what they are
• Are there any major presentational problems? Are figures & tables, language and manuscript structure all clear enough for you to accurately assess the work?
• Are there any ethical issues? If you are unsure it may be better to disclose these in the confidential comments section

Minor Issues

• Are there places where meaning is ambiguous? How can this be corrected?
• Are the correct references cited? If not, which should be cited instead/also? Are citations excessive, limited, or biased?
• Are there any factual, numerical or unit errors? If so, what are they?
• Are all tables and figures appropriate, sufficient, and correctly labelled? If not, say which are not

Your review should ultimately help the author improve their article. So be polite, honest and clear. You should also try to be objective and constructive, not subjective and destructive.

You should also:

• Write clearly and so you can be understood by people whose first language is not English
• Avoid complex or unusual words, especially ones that would even confuse native speakers
• Number your points and refer to page and line numbers in the manuscript when making specific comments
• If you have been asked to only comment on specific parts or aspects of the manuscript, you should indicate clearly which these are
• Treat the author's work the way you would like your own to be treated

Most journals give reviewers the option to provide some confidential comments to editors. Often this is where editors will want reviewers to state their recommendation - see the next section - but otherwise this area is best reserved for communicating malpractice such as suspected plagiarism, fraud, unattributed work, unethical procedures, duplicate publication, bias or other conflicts of interest.

However, this doesn't give reviewers permission to 'backstab' the author. Authors can't see this feedback and are unable to give their side of the story unless the editor asks them to. So in the spirit of fairness, write comments to editors as though authors might read them too.

Reviewers should check the preferences of individual journals as to where they want review decisions to be stated. In particular, bear in mind that some journals will not want the recommendation included in any comments to authors, as this can cause editors difficulty later - see Section 11 for more advice about working with editors.

You will normally be asked to indicate your recommendation (e.g. accept, reject, revise and resubmit, etc.) from a fixed-choice list and then to enter your comments into a separate text box.

Recommending Acceptance

If you're recommending acceptance, give details outlining why, and if there are any areas that could be improved. Don't just give a short, cursory remark such as 'great, accept'. See Improving the Manuscript

Recommending Revision

Where improvements are needed, a recommendation for major or minor revision is typical. You may also choose to state whether you opt in or out of the post-revision review too. If recommending revision, state specific changes you feel need to be made. The author can then reply to each point in turn.

Some journals offer the option to recommend rejection with the possibility of resubmission – this is most relevant where substantial, major revision is necessary.

What can reviewers do to help? " Be clear in their comments to the author (or editor) which points are absolutely critical if the paper is given an opportunity for revisio n." (Jonathon Halbesleben, Editor of Journal of Occupational and Organizational Psychology)

Recommending Rejection

If recommending rejection or major revision, state this clearly in your review (and see the next section, 'When recommending rejection').

Where manuscripts have serious flaws you should not spend any time polishing the review you've drafted or give detailed advice on presentation.

Editors say, " If a reviewer suggests a rejection, but her/his comments are not detailed or helpful, it does not help the editor in making a decision ."

In your recommendations for the author, you should:

• Give constructive feedback describing ways that they could improve the research
• Keep the focus on the research and not the author. This is an extremely important part of your job as a reviewer
• Avoid making critical confidential comments to the editor while being polite and encouraging to the author - the latter may not understand why their manuscript has been rejected. Also, they won't get feedback on how to improve their research and it could trigger an appeal

Remember to give constructive criticism even if recommending rejection. This helps developing researchers improve their work and explains to the editor why you felt the manuscript should not be published.

" When the comments seem really positive, but the recommendation is rejection…it puts the editor in a tough position of having to reject a paper when the comments make it sound like a great paper ." (Jonathon Halbesleben, Editor of Journal of Occupational and Organizational Psychology)

Visit our Wiley Author Learning and Training Channel for expert advice on peer review.

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• Research Process

Writing a good review article

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Table of Contents

As a young researcher, you might wonder how to start writing your first review article, and the extent of the information that it should contain. A review article is a comprehensive summary of the current understanding of a specific research topic and is based on previously published research. Unlike research papers, it does not contain new results, but can propose new inferences based on the combined findings of previous research.

Types of review articles

Review articles are typically of three types: literature reviews, systematic reviews, and meta-analyses.

A literature review is a general survey of the research topic and aims to provide a reliable and unbiased account of the current understanding of the topic.

A systematic review , in contrast, is more specific and attempts to address a highly focused research question. Its presentation is more detailed, with information on the search strategy used, the eligibility criteria for inclusion of studies, the methods utilized to review the collected information, and more.

A meta-analysis is similar to a systematic review in that both are systematically conducted with a properly defined research question. However, unlike the latter, a meta-analysis compares and evaluates a defined number of similar studies. It is quantitative in nature and can help assess contrasting study findings.

Tips for writing a good review article

Here are a few practices that can make the time-consuming process of writing a review article easier:

• Define your question: Take your time to identify the research question and carefully articulate the topic of your review paper. A good review should also add something new to the field in terms of a hypothesis, inference, or conclusion. A carefully defined scientific question will give you more clarity in determining the novelty of your inferences.
• Identify credible sources: Identify relevant as well as credible studies that you can base your review on, with the help of multiple databases or search engines. It is also a good idea to conduct another search once you have finished your article to avoid missing relevant studies published during the course of your writing.
• Take notes: A literature search involves extensive reading, which can make it difficult to recall relevant information subsequently. Therefore, make notes while conducting the literature search and note down the source references. This will ensure that you have sufficient information to start with when you finally get to writing.
• Describe the title, abstract, and introduction: A good starting point to begin structuring your review is by drafting the title, abstract, and introduction. Explicitly writing down what your review aims to address in the field will help shape the rest of your article.
• Be unbiased and critical: Evaluate every piece of evidence in a critical but unbiased manner. This will help you present a proper assessment and a critical discussion in your article.
• Include a good summary: End by stating the take-home message and identify the limitations of existing studies that need to be addressed through future studies.
• Ask for feedback: Ask a colleague to provide feedback on both the content and the language or tone of your article before you submit it.
• Check your journal’s guidelines: Some journals only publish reviews, while some only publish research articles. Further, all journals clearly indicate their aims and scope. Therefore, make sure to check the appropriateness of a journal before submitting your article.

Writing review articles, especially systematic reviews or meta-analyses, can seem like a daunting task. However, Elsevier Author Services can guide you by providing useful tips on how to write an impressive review article that stands out and gets published!

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How to Write a Peer Review

When you write a peer review for a manuscript, what should you include in your comments? What should you leave out? And how should the review be formatted?

This guide provides quick tips for writing and organizing your reviewer report.

Review Outline

Use an outline for your reviewer report so it’s easy for the editors and author to follow. This will also help you keep your comments organized.

Think about structuring your review like an inverted pyramid. Put the most important information at the top, followed by details and examples in the center, and any additional points at the very bottom.

Here’s how your outline might look:

1. Summary of the research and your overall impression

In your own words, summarize what the manuscript claims to report. This shows the editor how you interpreted the manuscript and will highlight any major differences in perspective between you and the other reviewers. Give an overview of the manuscript’s strengths and weaknesses. Think about this as your “take-home” message for the editors. End this section with your recommended course of action.

2. Discussion of specific areas for improvement

It’s helpful to divide this section into two parts: one for major issues and one for minor issues. Within each section, you can talk about the biggest issues first or go systematically figure-by-figure or claim-by-claim. Number each item so that your points are easy to follow (this will also make it easier for the authors to respond to each point). Refer to specific lines, pages, sections, or figure and table numbers so the authors (and editors) know exactly what you’re talking about.

Major vs. minor issues

What’s the difference between a major and minor issue? Major issues should consist of the essential points the authors need to address before the manuscript can proceed. Make sure you focus on what is  fundamental for the current study . In other words, it’s not helpful to recommend additional work that would be considered the “next step” in the study. Minor issues are still important but typically will not affect the overall conclusions of the manuscript. Here are some examples of what would might go in the “minor” category:

• Missing references (but depending on what is missing, this could also be a major issue)
• Technical clarifications (e.g., the authors should clarify how a reagent works)
• Data presentation (e.g., the authors should present p-values differently)
• Typos, spelling, grammar, and phrasing issues

3. Any other points

Confidential comments for the editors.

Some journals have a space for reviewers to enter confidential comments about the manuscript. Use this space to mention concerns about the submission that you’d want the editors to consider before sharing your feedback with the authors, such as concerns about ethical guidelines or language quality. Any serious issues should be raised directly and immediately with the journal as well.

This section is also where you will disclose any potentially competing interests, and mention whether you’re willing to look at a revised version of the manuscript.

Do not use this space to critique the manuscript, since comments entered here will not be passed along to the authors.  If you’re not sure what should go in the confidential comments, read the reviewer instructions or check with the journal first before submitting your review. If you are reviewing for a journal that does not offer a space for confidential comments, consider writing to the editorial office directly with your concerns.

Get this outline in a template

Giving Feedback

Giving feedback is hard. Giving effective feedback can be even more challenging. Remember that your ultimate goal is to discuss what the authors would need to do in order to qualify for publication. The point is not to nitpick every piece of the manuscript. Your focus should be on providing constructive and critical feedback that the authors can use to improve their study.

If you’ve ever had your own work reviewed, you already know that it’s not always easy to receive feedback. Follow the golden rule: Write the type of review you’d want to receive if you were the author. Even if you decide not to identify yourself in the review, you should write comments that you would be comfortable signing your name to.

In your comments, use phrases like “ the authors’ discussion of X” instead of “ your discussion of X .” This will depersonalize the feedback and keep the focus on the manuscript instead of the authors.

General guidelines for effective feedback

• Justify your recommendation with concrete evidence and specific examples.
• Be specific so the authors know what they need to do to improve.
• Be thorough. This might be the only time you read the manuscript.
• Be professional and respectful. The authors will be reading these comments too.
• Remember to say what you liked about the manuscript!

Don’t

• Recommend additional experiments or  unnecessary elements that are out of scope for the study or for the journal criteria.
• Tell the authors exactly how to revise their manuscript—you don’t need to do their work for them.
• Use the review to promote your own research or hypotheses.
• Focus on typos and grammar. If the manuscript needs significant editing for language and writing quality, just mention this in your comments.
• Submit your review without proofreading it and checking everything one more time.

Before and After: Sample Reviewer Comments

Keeping in mind the guidelines above, how do you put your thoughts into words? Here are some sample “before” and “after” reviewer comments

✗ Before

“The authors appear to have no idea what they are talking about. I don’t think they have read any of the literature on this topic.”

✓ After

“The study fails to address how the findings relate to previous research in this area. The authors should rewrite their Introduction and Discussion to reference the related literature, especially recently published work such as Darwin et al.”

“The writing is so bad, it is practically unreadable. I could barely bring myself to finish it.”

“While the study appears to be sound, the language is unclear, making it difficult to follow. I advise the authors work with a writing coach or copyeditor to improve the flow and readability of the text.”

“It’s obvious that this type of experiment should have been included. I have no idea why the authors didn’t use it. This is a big mistake.”

“The authors are off to a good start, however, this study requires additional experiments, particularly [type of experiment]. Alternatively, the authors should include more information that clarifies and justifies their choice of methods.”

Suggested Language for Tricky Situations

You might find yourself in a situation where you’re not sure how to explain the problem or provide feedback in a constructive and respectful way. Here is some suggested language for common issues you might experience.

What you think : The manuscript is fatally flawed. What you could say: “The study does not appear to be sound” or “the authors have missed something crucial”.

What you think : You don’t completely understand the manuscript. What you could say : “The authors should clarify the following sections to avoid confusion…”

What you think : The technical details don’t make sense. What you could say : “The technical details should be expanded and clarified to ensure that readers understand exactly what the researchers studied.”

What you think: The writing is terrible. What you could say : “The authors should revise the language to improve readability.”

What you think : The authors have over-interpreted the findings. What you could say : “The authors aim to demonstrate [XYZ], however, the data does not fully support this conclusion. Specifically…”

What does a good review look like?

Check out the peer review examples at F1000 Research to see how other reviewers write up their reports and give constructive feedback to authors.

Time to Submit the Review!

Be sure you turn in your report on time. Need an extension? Tell the journal so that they know what to expect. If you need a lot of extra time, the journal might need to contact other reviewers or notify the author about the delay.

Tip: Building a relationship with an editor

You’ll be more likely to be asked to review again if you provide high-quality feedback and if you turn in the review on time. Especially if it’s your first review for a journal, it’s important to show that you are reliable. Prove yourself once and you’ll get asked to review again!

• Getting started as a reviewer
• Responding to an invitation
• Reading a manuscript
• Writing a peer review

The contents of the Peer Review Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

The contents of the Writing Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

There’s a lot to consider when deciding where to submit your work. Learn how to choose a journal that will help your study reach its audience, while reflecting your values as a researcher…

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• Critical Reviews

How to Write an Article Review

Last Updated: September 8, 2023 Fact Checked

This article was co-authored by Jake Adams . Jake Adams is an academic tutor and the owner of Simplifi EDU, a Santa Monica, California based online tutoring business offering learning resources and online tutors for academic subjects K-College, SAT & ACT prep, and college admissions applications. With over 14 years of professional tutoring experience, Jake is dedicated to providing his clients the very best online tutoring experience and access to a network of excellent undergraduate and graduate-level tutors from top colleges all over the nation. Jake holds a BS in International Business and Marketing from Pepperdine University. There are 13 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 3,087,987 times.

An article review is both a summary and an evaluation of another writer's article. Teachers often assign article reviews to introduce students to the work of experts in the field. Experts also are often asked to review the work of other professionals. Understanding the main points and arguments of the article is essential for an accurate summation. Logical evaluation of the article's main theme, supporting arguments, and implications for further research is an important element of a review . Here are a few guidelines for writing an article review.

Education specialist Alexander Peterman recommends: "In the case of a review, your objective should be to reflect on the effectiveness of what has already been written, rather than writing to inform your audience about a subject."

Things You Should Know

• Read the article very closely, and then take time to reflect on your evaluation. Consider whether the article effectively achieves what it set out to.
• Write out a full article review by completing your intro, summary, evaluation, and conclusion. Don't forget to add a title, too!
• Proofread your review for mistakes (like grammar and usage), while also cutting down on needless information. [1] X Research source

Preparing to Write Your Review

• Article reviews present more than just an opinion. You will engage with the text to create a response to the scholarly writer's ideas. You will respond to and use ideas, theories, and research from your studies. Your critique of the article will be based on proof and your own thoughtful reasoning.
• An article review only responds to the author's research. It typically does not provide any new research. However, if you are correcting misleading or otherwise incorrect points, some new data may be presented.
• An article review both summarizes and evaluates the article.

• Summarize the article. Focus on the important points, claims, and information.
• Discuss the positive aspects of the article. Think about what the author does well, good points she makes, and insightful observations.
• Identify contradictions, gaps, and inconsistencies in the text. Determine if there is enough data or research included to support the author's claims. Find any unanswered questions left in the article.

• Make note of words or issues you don't understand and questions you have.
• Look up terms or concepts you are unfamiliar with, so you can fully understand the article. Read about concepts in-depth to make sure you understand their full context.

• Pay careful attention to the meaning of the article. Make sure you fully understand the article. The only way to write a good article review is to understand the article.

• With either method, make an outline of the main points made in the article and the supporting research or arguments. It is strictly a restatement of the main points of the article and does not include your opinions.
• After putting the article in your own words, decide which parts of the article you want to discuss in your review. You can focus on the theoretical approach, the content, the presentation or interpretation of evidence, or the style. You will always discuss the main issues of the article, but you can sometimes also focus on certain aspects. This comes in handy if you want to focus the review towards the content of a course.
• Review the summary outline to eliminate unnecessary items. Erase or cross out the less important arguments or supplemental information. Your revised summary can serve as the basis for the summary you provide at the beginning of your review.

• What does the article set out to do?
• What is the theoretical framework or assumptions?
• Are the central concepts clearly defined?
• How adequate is the evidence?
• How does the article fit into the literature and field?
• Does it advance the knowledge of the subject?
• How clear is the author's writing? Don't: include superficial opinions or your personal reaction. Do: pay attention to your biases, so you can overcome them.

Writing the Article Review

• For example, in MLA , a citation may look like: Duvall, John N. "The (Super)Marketplace of Images: Television as Unmediated Mediation in DeLillo's White Noise ." Arizona Quarterly 50.3 (1994): 127-53. Print. [10] X Trustworthy Source Purdue Online Writing Lab Trusted resource for writing and citation guidelines Go to source

• For example: The article, "Condom use will increase the spread of AIDS," was written by Anthony Zimmerman, a Catholic priest.

• Your introduction should only be 10-25% of your review.
• End the introduction with your thesis. Your thesis should address the above issues. For example: Although the author has some good points, his article is biased and contains some misinterpretation of data from others’ analysis of the effectiveness of the condom.

• Use direct quotes from the author sparingly.
• Review the summary you have written. Read over your summary many times to ensure that your words are an accurate description of the author's article.

• Support your critique with evidence from the article or other texts.
• The summary portion is very important for your critique. You must make the author's argument clear in the summary section for your evaluation to make sense.
• Remember, this is not where you say if you liked the article or not. You are assessing the significance and relevance of the article.
• Use a topic sentence and supportive arguments for each opinion. For example, you might address a particular strength in the first sentence of the opinion section, followed by several sentences elaborating on the significance of the point.

• This should only be about 10% of your overall essay.
• For example: This critical review has evaluated the article "Condom use will increase the spread of AIDS" by Anthony Zimmerman. The arguments in the article show the presence of bias, prejudice, argumentative writing without supporting details, and misinformation. These points weaken the author’s arguments and reduce his credibility.

• Make sure you have identified and discussed the 3-4 key issues in the article.

Sample Article Reviews

Expert Q&A

You Might Also Like

• ↑ https://writing.wisc.edu/handbook/grammarpunct/proofreading/
• ↑ https://libguides.cmich.edu/writinghelp/articlereview
• ↑ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4548566/
• ↑ Jake Adams. Academic Tutor & Test Prep Specialist. Expert Interview. 24 July 2020.
• ↑ https://guides.library.queensu.ca/introduction-research/writing/critical
• ↑ https://www.iup.edu/writingcenter/writing-resources/organization-and-structure/creating-an-outline.html
• ↑ https://writing.umn.edu/sws/assets/pdf/quicktips/titles.pdf
• ↑ https://owl.purdue.edu/owl/research_and_citation/mla_style/mla_formatting_and_style_guide/mla_works_cited_periodicals.html
• ↑ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4548565/
• ↑ https://writingcenter.uconn.edu/wp-content/uploads/sites/593/2014/06/How_to_Summarize_a_Research_Article1.pdf
• ↑ https://www.uis.edu/learning-hub/writing-resources/handouts/learning-hub/how-to-review-a-journal-article
• ↑ https://writingcenter.unc.edu/tips-and-tools/editing-and-proofreading/

About This Article

If you have to write an article review, read through the original article closely, taking notes and highlighting important sections as you read. Next, rewrite the article in your own words, either in a long paragraph or as an outline. Open your article review by citing the article, then write an introduction which states the article’s thesis. Next, summarize the article, followed by your opinion about whether the article was clear, thorough, and useful. Finish with a paragraph that summarizes the main points of the article and your opinions. To learn more about what to include in your personal critique of the article, keep reading the article! Did this summary help you? Yes No

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Writing a Literature Review

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A literature review is a document or section of a document that collects key sources on a topic and discusses those sources in conversation with each other (also called synthesis ). The lit review is an important genre in many disciplines, not just literature (i.e., the study of works of literature such as novels and plays). When we say “literature review” or refer to “the literature,” we are talking about the research ( scholarship ) in a given field. You will often see the terms “the research,” “the scholarship,” and “the literature” used mostly interchangeably.

Where, when, and why would I write a lit review?

There are a number of different situations where you might write a literature review, each with slightly different expectations; different disciplines, too, have field-specific expectations for what a literature review is and does. For instance, in the humanities, authors might include more overt argumentation and interpretation of source material in their literature reviews, whereas in the sciences, authors are more likely to report study designs and results in their literature reviews; these differences reflect these disciplines’ purposes and conventions in scholarship. You should always look at examples from your own discipline and talk to professors or mentors in your field to be sure you understand your discipline’s conventions, for literature reviews as well as for any other genre.

A literature review can be a part of a research paper or scholarly article, usually falling after the introduction and before the research methods sections. In these cases, the lit review just needs to cover scholarship that is important to the issue you are writing about; sometimes it will also cover key sources that informed your research methodology.

Lit reviews can also be standalone pieces, either as assignments in a class or as publications. In a class, a lit review may be assigned to help students familiarize themselves with a topic and with scholarship in their field, get an idea of the other researchers working on the topic they’re interested in, find gaps in existing research in order to propose new projects, and/or develop a theoretical framework and methodology for later research. As a publication, a lit review usually is meant to help make other scholars’ lives easier by collecting and summarizing, synthesizing, and analyzing existing research on a topic. This can be especially helpful for students or scholars getting into a new research area, or for directing an entire community of scholars toward questions that have not yet been answered.

What are the parts of a lit review?

Most lit reviews use a basic introduction-body-conclusion structure; if your lit review is part of a larger paper, the introduction and conclusion pieces may be just a few sentences while you focus most of your attention on the body. If your lit review is a standalone piece, the introduction and conclusion take up more space and give you a place to discuss your goals, research methods, and conclusions separately from where you discuss the literature itself.

Introduction:

• An introductory paragraph that explains what your working topic and thesis is
• A forecast of key topics or texts that will appear in the review
• Potentially, a description of how you found sources and how you analyzed them for inclusion and discussion in the review (more often found in published, standalone literature reviews than in lit review sections in an article or research paper)
• Summarize and synthesize: Give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: Don’t just paraphrase other researchers – add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically Evaluate: Mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: Use transition words and topic sentence to draw connections, comparisons, and contrasts.

Conclusion:

• Summarize the key findings you have taken from the literature and emphasize their significance
• Connect it back to your primary research question

How should I organize my lit review?

Lit reviews can take many different organizational patterns depending on what you are trying to accomplish with the review. Here are some examples:

• Chronological : The simplest approach is to trace the development of the topic over time, which helps familiarize the audience with the topic (for instance if you are introducing something that is not commonly known in your field). If you choose this strategy, be careful to avoid simply listing and summarizing sources in order. Try to analyze the patterns, turning points, and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred (as mentioned previously, this may not be appropriate in your discipline — check with a teacher or mentor if you’re unsure).
• Thematic : If you have found some recurring central themes that you will continue working with throughout your piece, you can organize your literature review into subsections that address different aspects of the topic. For example, if you are reviewing literature about women and religion, key themes can include the role of women in churches and the religious attitude towards women.
• Qualitative versus quantitative research
• Empirical versus theoretical scholarship
• Divide the research by sociological, historical, or cultural sources
• Theoretical : In many humanities articles, the literature review is the foundation for the theoretical framework. You can use it to discuss various theories, models, and definitions of key concepts. You can argue for the relevance of a specific theoretical approach or combine various theorical concepts to create a framework for your research.

What are some strategies or tips I can use while writing my lit review?

Any lit review is only as good as the research it discusses; make sure your sources are well-chosen and your research is thorough. Don’t be afraid to do more research if you discover a new thread as you’re writing. More info on the research process is available in our "Conducting Research" resources .

As you’re doing your research, create an annotated bibliography ( see our page on the this type of document ). Much of the information used in an annotated bibliography can be used also in a literature review, so you’ll be not only partially drafting your lit review as you research, but also developing your sense of the larger conversation going on among scholars, professionals, and any other stakeholders in your topic.

Usually you will need to synthesize research rather than just summarizing it. This means drawing connections between sources to create a picture of the scholarly conversation on a topic over time. Many student writers struggle to synthesize because they feel they don’t have anything to add to the scholars they are citing; here are some strategies to help you:

• It often helps to remember that the point of these kinds of syntheses is to show your readers how you understand your research, to help them read the rest of your paper.
• Writing teachers often say synthesis is like hosting a dinner party: imagine all your sources are together in a room, discussing your topic. What are they saying to each other?
• Look at the in-text citations in each paragraph. Are you citing just one source for each paragraph? This usually indicates summary only. When you have multiple sources cited in a paragraph, you are more likely to be synthesizing them (not always, but often
• Read more about synthesis here.

The most interesting literature reviews are often written as arguments (again, as mentioned at the beginning of the page, this is discipline-specific and doesn’t work for all situations). Often, the literature review is where you can establish your research as filling a particular gap or as relevant in a particular way. You have some chance to do this in your introduction in an article, but the literature review section gives a more extended opportunity to establish the conversation in the way you would like your readers to see it. You can choose the intellectual lineage you would like to be part of and whose definitions matter most to your thinking (mostly humanities-specific, but this goes for sciences as well). In addressing these points, you argue for your place in the conversation, which tends to make the lit review more compelling than a simple reporting of other sources.

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• Volume 24, Issue 2
• Five tips for developing useful literature summary tables for writing review articles
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• http://orcid.org/0000-0003-0157-5319 Ahtisham Younas 1 , 2 ,
• http://orcid.org/0000-0002-7839-8130 Parveen Ali 3 , 4
• 1 Memorial University of Newfoundland , St John's , Newfoundland , Canada
• 2 Swat College of Nursing , Pakistan
• 3 School of Nursing and Midwifery , University of Sheffield , Sheffield , South Yorkshire , UK
• 4 Sheffield University Interpersonal Violence Research Group , Sheffield University , Sheffield , UK
• Correspondence to Ahtisham Younas, Memorial University of Newfoundland, St John's, NL A1C 5C4, Canada; ay6133{at}mun.ca

https://doi.org/10.1136/ebnurs-2021-103417

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Introduction

Literature reviews offer a critical synthesis of empirical and theoretical literature to assess the strength of evidence, develop guidelines for practice and policymaking, and identify areas for future research. 1 It is often essential and usually the first task in any research endeavour, particularly in masters or doctoral level education. For effective data extraction and rigorous synthesis in reviews, the use of literature summary tables is of utmost importance. A literature summary table provides a synopsis of an included article. It succinctly presents its purpose, methods, findings and other relevant information pertinent to the review. The aim of developing these literature summary tables is to provide the reader with the information at one glance. Since there are multiple types of reviews (eg, systematic, integrative, scoping, critical and mixed methods) with distinct purposes and techniques, 2 there could be various approaches for developing literature summary tables making it a complex task specialty for the novice researchers or reviewers. Here, we offer five tips for authors of the review articles, relevant to all types of reviews, for creating useful and relevant literature summary tables. We also provide examples from our published reviews to illustrate how useful literature summary tables can be developed and what sort of information should be provided.

Tip 1: provide detailed information about frameworks and methods

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Tabular literature summaries from a scoping review. Source: Rasheed et al . 3

The provision of information about conceptual and theoretical frameworks and methods is useful for several reasons. First, in quantitative (reviews synthesising the results of quantitative studies) and mixed reviews (reviews synthesising the results of both qualitative and quantitative studies to address a mixed review question), it allows the readers to assess the congruence of the core findings and methods with the adapted framework and tested assumptions. In qualitative reviews (reviews synthesising results of qualitative studies), this information is beneficial for readers to recognise the underlying philosophical and paradigmatic stance of the authors of the included articles. For example, imagine the authors of an article, included in a review, used phenomenological inquiry for their research. In that case, the review authors and the readers of the review need to know what kind of (transcendental or hermeneutic) philosophical stance guided the inquiry. Review authors should, therefore, include the philosophical stance in their literature summary for the particular article. Second, information about frameworks and methods enables review authors and readers to judge the quality of the research, which allows for discerning the strengths and limitations of the article. For example, if authors of an included article intended to develop a new scale and test its psychometric properties. To achieve this aim, they used a convenience sample of 150 participants and performed exploratory (EFA) and confirmatory factor analysis (CFA) on the same sample. Such an approach would indicate a flawed methodology because EFA and CFA should not be conducted on the same sample. The review authors must include this information in their summary table. Omitting this information from a summary could lead to the inclusion of a flawed article in the review, thereby jeopardising the review’s rigour.

Tip 2: include strengths and limitations for each article

Critical appraisal of individual articles included in a review is crucial for increasing the rigour of the review. Despite using various templates for critical appraisal, authors often do not provide detailed information about each reviewed article’s strengths and limitations. Merely noting the quality score based on standardised critical appraisal templates is not adequate because the readers should be able to identify the reasons for assigning a weak or moderate rating. Many recent critical appraisal checklists (eg, Mixed Methods Appraisal Tool) discourage review authors from assigning a quality score and recommend noting the main strengths and limitations of included studies. It is also vital that methodological and conceptual limitations and strengths of the articles included in the review are provided because not all review articles include empirical research papers. Rather some review synthesises the theoretical aspects of articles. Providing information about conceptual limitations is also important for readers to judge the quality of foundations of the research. For example, if you included a mixed-methods study in the review, reporting the methodological and conceptual limitations about ‘integration’ is critical for evaluating the study’s strength. Suppose the authors only collected qualitative and quantitative data and did not state the intent and timing of integration. In that case, the strength of the study is weak. Integration only occurred at the levels of data collection. However, integration may not have occurred at the analysis, interpretation and reporting levels.

Tip 3: write conceptual contribution of each reviewed article

While reading and evaluating review papers, we have observed that many review authors only provide core results of the article included in a review and do not explain the conceptual contribution offered by the included article. We refer to conceptual contribution as a description of how the article’s key results contribute towards the development of potential codes, themes or subthemes, or emerging patterns that are reported as the review findings. For example, the authors of a review article noted that one of the research articles included in their review demonstrated the usefulness of case studies and reflective logs as strategies for fostering compassion in nursing students. The conceptual contribution of this research article could be that experiential learning is one way to teach compassion to nursing students, as supported by case studies and reflective logs. This conceptual contribution of the article should be mentioned in the literature summary table. Delineating each reviewed article’s conceptual contribution is particularly beneficial in qualitative reviews, mixed-methods reviews, and critical reviews that often focus on developing models and describing or explaining various phenomena. Figure 2 offers an example of a literature summary table. 4

Tabular literature summaries from a critical review. Source: Younas and Maddigan. 4

Tip 4: compose potential themes from each article during summary writing

While developing literature summary tables, many authors use themes or subthemes reported in the given articles as the key results of their own review. Such an approach prevents the review authors from understanding the article’s conceptual contribution, developing rigorous synthesis and drawing reasonable interpretations of results from an individual article. Ultimately, it affects the generation of novel review findings. For example, one of the articles about women’s healthcare-seeking behaviours in developing countries reported a theme ‘social-cultural determinants of health as precursors of delays’. Instead of using this theme as one of the review findings, the reviewers should read and interpret beyond the given description in an article, compare and contrast themes, findings from one article with findings and themes from another article to find similarities and differences and to understand and explain bigger picture for their readers. Therefore, while developing literature summary tables, think twice before using the predeveloped themes. Including your themes in the summary tables (see figure 1 ) demonstrates to the readers that a robust method of data extraction and synthesis has been followed.

Tip 5: create your personalised template for literature summaries

Often templates are available for data extraction and development of literature summary tables. The available templates may be in the form of a table, chart or a structured framework that extracts some essential information about every article. The commonly used information may include authors, purpose, methods, key results and quality scores. While extracting all relevant information is important, such templates should be tailored to meet the needs of the individuals’ review. For example, for a review about the effectiveness of healthcare interventions, a literature summary table must include information about the intervention, its type, content timing, duration, setting, effectiveness, negative consequences, and receivers and implementers’ experiences of its usage. Similarly, literature summary tables for articles included in a meta-synthesis must include information about the participants’ characteristics, research context and conceptual contribution of each reviewed article so as to help the reader make an informed decision about the usefulness or lack of usefulness of the individual article in the review and the whole review.

In conclusion, narrative or systematic reviews are almost always conducted as a part of any educational project (thesis or dissertation) or academic or clinical research. Literature reviews are the foundation of research on a given topic. Robust and high-quality reviews play an instrumental role in guiding research, practice and policymaking. However, the quality of reviews is also contingent on rigorous data extraction and synthesis, which require developing literature summaries. We have outlined five tips that could enhance the quality of the data extraction and synthesis process by developing useful literature summaries.

• Aromataris E ,
• Rasheed SP ,

Twitter @Ahtisham04, @parveenazamali

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient consent for publication Not required.

Provenance and peer review Not commissioned; externally peer reviewed.

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• 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

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• 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.

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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 .

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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).

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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

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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.

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Correspondence to Kevin J. Verstrepen .

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K.J.V. is affiliated with bar.on. The other authors declare no competing interests.

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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

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DOI : https://doi.org/10.1038/s41467-024-46346-0

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