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How to Write an Abstract | Steps & Examples

Published on February 28, 2019 by Shona McCombes . Revised on July 18, 2023 by Eoghan Ryan.

An abstract is a short summary of a longer work (such as a thesis ,  dissertation or research paper ). The abstract concisely reports the aims and outcomes of your research, so that readers know exactly what your paper is about.

Although the structure may vary slightly depending on your discipline, your abstract should describe the purpose of your work, the methods you’ve used, and the conclusions you’ve drawn.

One common way to structure your abstract is to use the IMRaD structure. This stands for:

• Introduction

Abstracts are usually around 100–300 words, but there’s often a strict word limit, so make sure to check the relevant requirements.

In a dissertation or thesis , include the abstract on a separate page, after the title page and acknowledgements but before the table of contents .

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Table of contents

Abstract example, when to write an abstract, step 1: introduction, step 2: methods, step 3: results, step 4: discussion, tips for writing an abstract, other interesting articles, frequently asked questions about abstracts.

Hover over the different parts of the abstract to see how it is constructed.

This paper examines the role of silent movies as a mode of shared experience in the US during the early twentieth century. At this time, high immigration rates resulted in a significant percentage of non-English-speaking citizens. These immigrants faced numerous economic and social obstacles, including exclusion from public entertainment and modes of discourse (newspapers, theater, radio).

Incorporating evidence from reviews, personal correspondence, and diaries, this study demonstrates that silent films were an affordable and inclusive source of entertainment. It argues for the accessible economic and representational nature of early cinema. These concerns are particularly evident in the low price of admission and in the democratic nature of the actors’ exaggerated gestures, which allowed the plots and action to be easily grasped by a diverse audience despite language barriers.

Keywords: silent movies, immigration, public discourse, entertainment, early cinema, language barriers.

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You will almost always have to include an abstract when:

• Completing a thesis or dissertation
• Submitting a research paper to an academic journal
• Writing a book or research proposal
• Applying for research grants

It’s easiest to write your abstract last, right before the proofreading stage, because it’s a summary of the work you’ve already done. Your abstract should:

• Be a self-contained text, not an excerpt from your paper
• Be fully understandable on its own
• Reflect the structure of your larger work

Start by clearly defining the purpose of your research. What practical or theoretical problem does the research respond to, or what research question did you aim to answer?

You can include some brief context on the social or academic relevance of your dissertation topic , but don’t go into detailed background information. If your abstract uses specialized terms that would be unfamiliar to the average academic reader or that have various different meanings, give a concise definition.

After identifying the problem, state the objective of your research. Use verbs like “investigate,” “test,” “analyze,” or “evaluate” to describe exactly what you set out to do.

This part of the abstract can be written in the present or past simple tense  but should never refer to the future, as the research is already complete.

• This study will investigate the relationship between coffee consumption and productivity.
• This study investigates the relationship between coffee consumption and productivity.

Next, indicate the research methods that you used to answer your question. This part should be a straightforward description of what you did in one or two sentences. It is usually written in the past simple tense, as it refers to completed actions.

• Structured interviews will be conducted with 25 participants.
• Structured interviews were conducted with 25 participants.

Don’t evaluate validity or obstacles here — the goal is not to give an account of the methodology’s strengths and weaknesses, but to give the reader a quick insight into the overall approach and procedures you used.

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Next, summarize the main research results . This part of the abstract can be in the present or past simple tense.

• Our analysis has shown a strong correlation between coffee consumption and productivity.
• Our analysis shows a strong correlation between coffee consumption and productivity.
• Our analysis showed a strong correlation between coffee consumption and productivity.

Depending on how long and complex your research is, you may not be able to include all results here. Try to highlight only the most important findings that will allow the reader to understand your conclusions.

Finally, you should discuss the main conclusions of your research : what is your answer to the problem or question? The reader should finish with a clear understanding of the central point that your research has proved or argued. Conclusions are usually written in the present simple tense.

• We concluded that coffee consumption increases productivity.
• We conclude that coffee consumption increases productivity.

If there are important limitations to your research (for example, related to your sample size or methods), you should mention them briefly in the abstract. This allows the reader to accurately assess the credibility and generalizability of your research.

If your aim was to solve a practical problem, your discussion might include recommendations for implementation. If relevant, you can briefly make suggestions for further research.

If your paper will be published, you might have to add a list of keywords at the end of the abstract. These keywords should reference the most important elements of the research to help potential readers find your paper during their own literature searches.

Be aware that some publication manuals, such as APA Style , have specific formatting requirements for these keywords.

It can be a real challenge to condense your whole work into just a couple of hundred words, but the abstract will be the first (and sometimes only) part that people read, so it’s important to get it right. These strategies can help you get started.

Read other abstracts

The best way to learn the conventions of writing an abstract in your discipline is to read other people’s. You probably already read lots of journal article abstracts while conducting your literature review —try using them as a framework for structure and style.

You can also find lots of dissertation abstract examples in thesis and dissertation databases .

Reverse outline

Not all abstracts will contain precisely the same elements. For longer works, you can write your abstract through a process of reverse outlining.

For each chapter or section, list keywords and draft one to two sentences that summarize the central point or argument. This will give you a framework of your abstract’s structure. Next, revise the sentences to make connections and show how the argument develops.

Write clearly and concisely

A good abstract is short but impactful, so make sure every word counts. Each sentence should clearly communicate one main point.

To keep your abstract or summary short and clear:

• Avoid passive sentences: Passive constructions are often unnecessarily long. You can easily make them shorter and clearer by using the active voice.
• Avoid long sentences: Substitute longer expressions for concise expressions or single words (e.g., “In order to” for “To”).
• Avoid obscure jargon: The abstract should be understandable to readers who are not familiar with your topic.
• Avoid repetition and filler words: Replace nouns with pronouns when possible and eliminate unnecessary words.
• Avoid detailed descriptions: An abstract is not expected to provide detailed definitions, background information, or discussions of other scholars’ work. Instead, include this information in the body of your thesis or paper.

If you’re struggling to edit down to the required length, you can get help from expert editors with Scribbr’s professional proofreading services or use the paraphrasing tool .

Check your formatting

If you are writing a thesis or dissertation or submitting to a journal, there are often specific formatting requirements for the abstract—make sure to check the guidelines and format your work correctly. For APA research papers you can follow the APA abstract format .

Checklist: Abstract

The word count is within the required length, or a maximum of one page.

The abstract appears after the title page and acknowledgements and before the table of contents .

I have clearly stated my research problem and objectives.

I have briefly described my methodology .

I have summarized the most important results .

I have stated my main conclusions .

I have mentioned any important limitations and recommendations.

The abstract can be understood by someone without prior knowledge of the topic.

You've written a great abstract! Use the other checklists to continue improving your thesis or dissertation.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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An abstract is a concise summary of an academic text (such as a journal article or dissertation ). It serves two main purposes:

• To help potential readers determine the relevance of your paper for their own research.
• To communicate your key findings to those who don’t have time to read the whole paper.

Abstracts are often indexed along with keywords on academic databases, so they make your work more easily findable. Since the abstract is the first thing any reader sees, it’s important that it clearly and accurately summarizes the contents of your paper.

An abstract for a thesis or dissertation is usually around 200–300 words. There’s often a strict word limit, so make sure to check your university’s requirements.

The abstract is the very last thing you write. You should only write it after your research is complete, so that you can accurately summarize the entirety of your thesis , dissertation or research paper .

Avoid citing sources in your abstract . There are two reasons for this:

• The abstract should focus on your original research, not on the work of others.
• The abstract should be self-contained and fully understandable without reference to other sources.

There are some circumstances where you might need to mention other sources in an abstract: for example, if your research responds directly to another study or focuses on the work of a single theorist. In general, though, don’t include citations unless absolutely necessary.

The abstract appears on its own page in the thesis or dissertation , after the title page and acknowledgements but before the table of contents .

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Writing an Abstract for Your Research Paper

Definition and Purpose of Abstracts

An abstract is a short summary of your (published or unpublished) research paper, usually about a paragraph (c. 6-7 sentences, 150-250 words) long. A well-written abstract serves multiple purposes:

• an abstract lets readers get the gist or essence of your paper or article quickly, in order to decide whether to read the full paper;
• an abstract prepares readers to follow the detailed information, analyses, and arguments in your full paper;
• and, later, an abstract helps readers remember key points from your paper.

It’s also worth remembering that search engines and bibliographic databases use abstracts, as well as the title, to identify key terms for indexing your published paper. So what you include in your abstract and in your title are crucial for helping other researchers find your paper or article.

If you are writing an abstract for a course paper, your professor may give you specific guidelines for what to include and how to organize your abstract. Similarly, academic journals often have specific requirements for abstracts. So in addition to following the advice on this page, you should be sure to look for and follow any guidelines from the course or journal you’re writing for.

The Contents of an Abstract

Abstracts contain most of the following kinds of information in brief form. The body of your paper will, of course, develop and explain these ideas much more fully. As you will see in the samples below, the proportion of your abstract that you devote to each kind of information—and the sequence of that information—will vary, depending on the nature and genre of the paper that you are summarizing in your abstract. And in some cases, some of this information is implied, rather than stated explicitly. The Publication Manual of the American Psychological Association , which is widely used in the social sciences, gives specific guidelines for what to include in the abstract for different kinds of papers—for empirical studies, literature reviews or meta-analyses, theoretical papers, methodological papers, and case studies.

Here are the typical kinds of information found in most abstracts:

• the context or background information for your research; the general topic under study; the specific topic of your research
• the central questions or statement of the problem your research addresses
• what’s already known about this question, what previous research has done or shown
• the main reason(s) , the exigency, the rationale , the goals for your research—Why is it important to address these questions? Are you, for example, examining a new topic? Why is that topic worth examining? Are you filling a gap in previous research? Applying new methods to take a fresh look at existing ideas or data? Resolving a dispute within the literature in your field? . . .
• your research and/or analytical methods
• your main findings , results , or arguments
• the significance or implications of your findings or arguments.

Your abstract should be intelligible on its own, without a reader’s having to read your entire paper. And in an abstract, you usually do not cite references—most of your abstract will describe what you have studied in your research and what you have found and what you argue in your paper. In the body of your paper, you will cite the specific literature that informs your research.

When to Write Your Abstract

Although you might be tempted to write your abstract first because it will appear as the very first part of your paper, it’s a good idea to wait to write your abstract until after you’ve drafted your full paper, so that you know what you’re summarizing.

What follows are some sample abstracts in published papers or articles, all written by faculty at UW-Madison who come from a variety of disciplines. We have annotated these samples to help you see the work that these authors are doing within their abstracts.

Choosing Verb Tenses within Your Abstract

The social science sample (Sample 1) below uses the present tense to describe general facts and interpretations that have been and are currently true, including the prevailing explanation for the social phenomenon under study. That abstract also uses the present tense to describe the methods, the findings, the arguments, and the implications of the findings from their new research study. The authors use the past tense to describe previous research.

The humanities sample (Sample 2) below uses the past tense to describe completed events in the past (the texts created in the pulp fiction industry in the 1970s and 80s) and uses the present tense to describe what is happening in those texts, to explain the significance or meaning of those texts, and to describe the arguments presented in the article.

The science samples (Samples 3 and 4) below use the past tense to describe what previous research studies have done and the research the authors have conducted, the methods they have followed, and what they have found. In their rationale or justification for their research (what remains to be done), they use the present tense. They also use the present tense to introduce their study (in Sample 3, “Here we report . . .”) and to explain the significance of their study (In Sample 3, This reprogramming . . . “provides a scalable cell source for. . .”).

Sample Abstract 1

From the social sciences.

Reporting new findings about the reasons for increasing economic homogamy among spouses

Gonalons-Pons, Pilar, and Christine R. Schwartz. “Trends in Economic Homogamy: Changes in Assortative Mating or the Division of Labor in Marriage?” Demography , vol. 54, no. 3, 2017, pp. 985-1005.

Sample Abstract 2

From the humanities.

Analyzing underground pulp fiction publications in Tanzania, this article makes an argument about the cultural significance of those publications

Emily Callaci. “Street Textuality: Socialism, Masculinity, and Urban Belonging in Tanzania’s Pulp Fiction Publishing Industry, 1975-1985.” Comparative Studies in Society and History , vol. 59, no. 1, 2017, pp. 183-210.

Sample Abstract/Summary 3

From the sciences.

Reporting a new method for reprogramming adult mouse fibroblasts into induced cardiac progenitor cells

Lalit, Pratik A., Max R. Salick, Daryl O. Nelson, Jayne M. Squirrell, Christina M. Shafer, Neel G. Patel, Imaan Saeed, Eric G. Schmuck, Yogananda S. Markandeya, Rachel Wong, Martin R. Lea, Kevin W. Eliceiri, Timothy A. Hacker, Wendy C. Crone, Michael Kyba, Daniel J. Garry, Ron Stewart, James A. Thomson, Karen M. Downs, Gary E. Lyons, and Timothy J. Kamp. “Lineage Reprogramming of Fibroblasts into Proliferative Induced Cardiac Progenitor Cells by Defined Factors.” Cell Stem Cell , vol. 18, 2016, pp. 354-367.

Sample Abstract 4, a Structured Abstract

Reporting results about the effectiveness of antibiotic therapy in managing acute bacterial sinusitis, from a rigorously controlled study

Note: This journal requires authors to organize their abstract into four specific sections, with strict word limits. Because the headings for this structured abstract are self-explanatory, we have chosen not to add annotations to this sample abstract.

Wald, Ellen R., David Nash, and Jens Eickhoff. “Effectiveness of Amoxicillin/Clavulanate Potassium in the Treatment of Acute Bacterial Sinusitis in Children.” Pediatrics , vol. 124, no. 1, 2009, pp. 9-15.

“OBJECTIVE: The role of antibiotic therapy in managing acute bacterial sinusitis (ABS) in children is controversial. The purpose of this study was to determine the effectiveness of high-dose amoxicillin/potassium clavulanate in the treatment of children diagnosed with ABS.

METHODS : This was a randomized, double-blind, placebo-controlled study. Children 1 to 10 years of age with a clinical presentation compatible with ABS were eligible for participation. Patients were stratified according to age (<6 or ≥6 years) and clinical severity and randomly assigned to receive either amoxicillin (90 mg/kg) with potassium clavulanate (6.4 mg/kg) or placebo. A symptom survey was performed on days 0, 1, 2, 3, 5, 7, 10, 20, and 30. Patients were examined on day 14. Children’s conditions were rated as cured, improved, or failed according to scoring rules.

RESULTS: Two thousand one hundred thirty-five children with respiratory complaints were screened for enrollment; 139 (6.5%) had ABS. Fifty-eight patients were enrolled, and 56 were randomly assigned. The mean age was 6630 months. Fifty (89%) patients presented with persistent symptoms, and 6 (11%) presented with nonpersistent symptoms. In 24 (43%) children, the illness was classified as mild, whereas in the remaining 32 (57%) children it was severe. Of the 28 children who received the antibiotic, 14 (50%) were cured, 4 (14%) were improved, 4(14%) experienced treatment failure, and 6 (21%) withdrew. Of the 28children who received placebo, 4 (14%) were cured, 5 (18%) improved, and 19 (68%) experienced treatment failure. Children receiving the antibiotic were more likely to be cured (50% vs 14%) and less likely to have treatment failure (14% vs 68%) than children receiving the placebo.

CONCLUSIONS : ABS is a common complication of viral upper respiratory infections. Amoxicillin/potassium clavulanate results in significantly more cures and fewer failures than placebo, according to parental report of time to resolution.” (9)

Some Excellent Advice about Writing Abstracts for Basic Science Research Papers, by Professor Adriano Aguzzi from the Institute of Neuropathology at the University of Zurich:

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Abstract Writing: A Step-by-Step Guide With Tips & Examples

Table of Contents

Introduction

Abstracts of research papers have always played an essential role in describing your research concisely and clearly to researchers and editors of journals, enticing them to continue reading. However, with the widespread availability of scientific databases, the need to write a convincing abstract is more crucial now than during the time of paper-bound manuscripts.

Abstracts serve to "sell" your research and can be compared with your "executive outline" of a resume or, rather, a formal summary of the critical aspects of your work. Also, it can be the "gist" of your study. Since most educational research is done online, it's a sign that you have a shorter time for impressing your readers, and have more competition from other abstracts that are available to be read.

The APCI (Academic Publishing and Conferences International) articulates 12 issues or points considered during the final approval process for conferences & journals and emphasises the importance of writing an abstract that checks all these boxes (12 points). Since it's the only opportunity you have to captivate your readers, you must invest time and effort in creating an abstract that accurately reflects the critical points of your research.

With that in mind, let’s head over to understand and discover the core concept and guidelines to create a substantial abstract. Also, learn how to organise the ideas or plots into an effective abstract that will be awe-inspiring to the readers you want to reach.

What is Abstract? Definition and Overview

The word "Abstract' is derived from Latin abstractus meaning "drawn off." This etymological meaning also applies to art movements as well as music, like abstract expressionism. In this context, it refers to the revealing of the artist's intention.

Based on this, you can determine the meaning of an abstract: A condensed research summary. It must be self-contained and independent of the body of the research. However, it should outline the subject, the strategies used to study the problem, and the methods implemented to attain the outcomes. The specific elements of the study differ based on the area of study; however, together, it must be a succinct summary of the entire research paper.

Abstracts are typically written at the end of the paper, even though it serves as a prologue. In general, the abstract must be in a position to:

• Describe the paper.
• Identify the problem or the issue at hand.
• Explain to the reader the research process, the results you came up with, and what conclusion you've reached using these results.
• Include keywords to guide your strategy and the content.

Furthermore, the abstract you submit should not reflect upon any of  the following elements:

• Examine, analyse or defend the paper or your opinion.
• What you want to study, achieve or discover.
• Be redundant or irrelevant.

After reading an abstract, your audience should understand the reason - what the research was about in the first place, what the study has revealed and how it can be utilised or can be used to benefit others. You can understand the importance of abstract by knowing the fact that the abstract is the most frequently read portion of any research paper. In simpler terms, it should contain all the main points of the research paper.

What is the Purpose of an Abstract?

Abstracts are typically an essential requirement for research papers; however, it's not an obligation to preserve traditional reasons without any purpose. Abstracts allow readers to scan the text to determine whether it is relevant to their research or studies. The abstract allows other researchers to decide if your research paper can provide them with some additional information. A good abstract paves the interest of the audience to pore through your entire paper to find the content or context they're searching for.

Abstract writing is essential for indexing, as well. The Digital Repository of academic papers makes use of abstracts to index the entire content of academic research papers. Like meta descriptions in the regular Google outcomes, abstracts must include keywords that help researchers locate what they seek.

Types of Abstract

Informative and Descriptive are two kinds of abstracts often used in scientific writing.

A descriptive abstract gives readers an outline of the author's main points in their study. The reader can determine if they want to stick to the research work, based on their interest in the topic. An abstract that is descriptive is similar to the contents table of books, however, the format of an abstract depicts complete sentences encapsulated in one paragraph. It is unfortunate that the abstract can't be used as a substitute for reading a piece of writing because it's just an overview, which omits readers from getting an entire view. Also, it cannot be a way to fill in the gaps the reader may have after reading this kind of abstract since it does not contain crucial information needed to evaluate the article.

To conclude, a descriptive abstract is:

• A simple summary of the task, just summarises the work, but some researchers think it is much more of an outline
• Typically, the length is approximately 100 words. It is too short when compared to an informative abstract.
• A brief explanation but doesn't provide the reader with the complete information they need;
• An overview that omits conclusions and results

An informative abstract is a comprehensive outline of the research. There are times when people rely on the abstract as an information source. And the reason is why it is crucial to provide entire data of particular research. A well-written, informative abstract could be a good substitute for the remainder of the paper on its own.

A well-written abstract typically follows a particular style. The author begins by providing the identifying information, backed by citations and other identifiers of the papers. Then, the major elements are summarised to make the reader aware of the study. It is followed by the methodology and all-important findings from the study. The conclusion then presents study results and ends the abstract with a comprehensive summary.

In a nutshell, an informative abstract:

• Has a length that can vary, based on the subject, but is not longer than 300 words.
• Contains all the content-like methods and intentions
• Offers evidence and possible recommendations.

Informative Abstracts are more frequent than descriptive abstracts because of their extensive content and linkage to the topic specifically. You should select different types of abstracts to papers based on their length: informative abstracts for extended and more complex abstracts and descriptive ones for simpler and shorter research papers.

What are the Characteristics of a Good Abstract?

• A good abstract clearly defines the goals and purposes of the study.
• It should clearly describe the research methodology with a primary focus on data gathering, processing, and subsequent analysis.
• A good abstract should provide specific research findings.
• It presents the principal conclusions of the systematic study.
• It should be concise, clear, and relevant to the field of study.
• A well-designed abstract should be unifying and coherent.
• It is easy to grasp and free of technical jargon.
• It is written impartially and objectively.

What are the various sections of an ideal Abstract?

By now, you must have gained some concrete idea of the essential elements that your abstract needs to convey . Accordingly, the information is broken down into six key sections of the abstract, which include:

An Introduction or Background

Research methodology, objectives and goals, limitations.

Let's go over them in detail.

The introduction, also known as background, is the most concise part of your abstract. Ideally, it comprises a couple of sentences. Some researchers only write one sentence to introduce their abstract. The idea behind this is to guide readers through the key factors that led to your study.

It's understandable that this information might seem difficult to explain in a couple of sentences. For example, think about the following two questions like the background of your study:

• What is currently available about the subject with respect to the paper being discussed?
• What isn't understood about this issue? (This is the subject of your research)

While writing the abstract’s introduction, make sure that it is not lengthy. Because if it crosses the word limit, it may eat up the words meant to be used for providing other key information.

Research methodology is where you describe the theories and techniques you used in your research. It is recommended that you describe what you have done and the method you used to get your thorough investigation results. Certainly, it is the second-longest paragraph in the abstract.

In the research methodology section, it is essential to mention the kind of research you conducted; for instance, qualitative research or quantitative research (this will guide your research methodology too) . If you've conducted quantitative research, your abstract should contain information like the sample size, data collection method, sampling techniques, and duration of the study. Likewise, your abstract should reflect observational data, opinions, questionnaires (especially the non-numerical data) if you work on qualitative research.

The research objectives and goals speak about what you intend to accomplish with your research. The majority of research projects focus on the long-term effects of a project, and the goals focus on the immediate, short-term outcomes of the research. It is possible to summarise both in just multiple sentences.

In stating your objectives and goals, you give readers a picture of the scope of the study, its depth and the direction your research ultimately follows. Your readers can evaluate the results of your research against the goals and stated objectives to determine if you have achieved the goal of your research.

In the end, your readers are more attracted by the results you've obtained through your study. Therefore, you must take the time to explain each relevant result and explain how they impact your research. The results section exists as the longest in your abstract, and nothing should diminish its reach or quality.

One of the most important things you should adhere to is to spell out details and figures on the results of your research.

Instead of making a vague assertion such as, "We noticed that response rates varied greatly between respondents with high incomes and those with low incomes", Try these: "The response rate was higher for high-income respondents than those with lower incomes (59 30 percent vs. 30 percent in both cases; P<0.01)."

You're likely to encounter certain obstacles during your research. It could have been during data collection or even during conducting the sample . Whatever the issue, it's essential to inform your readers about them and their effects on the research.

Research limitations offer an opportunity to suggest further and deep research. If, for instance, you were forced to change for convenient sampling and snowball samples because of difficulties in reaching well-suited research participants, then you should mention this reason when you write your research abstract. In addition, a lack of prior studies on the subject could hinder your research.

Your conclusion should include the same number of sentences to wrap the abstract as the introduction. The majority of researchers offer an idea of the consequences of their research in this case.

Your conclusion should include three essential components:

• A significant take-home message.
• Corresponding important findings.
• The Interpretation.

Even though the conclusion of your abstract needs to be brief, it can have an enormous influence on the way that readers view your research. Therefore, make use of this section to reinforce the central message from your research. Be sure that your statements reflect the actual results and the methods you used to conduct your research.

Good Abstract Examples

Abstract example #1.

Children’s consumption behavior in response to food product placements in movies.

The abstract:

"Almost all research into the effects of brand placements on children has focused on the brand's attitudes or behavior intentions. Based on the significant differences between attitudes and behavioral intentions on one hand and actual behavior on the other hand, this study examines the impact of placements by brands on children's eating habits. Children aged 6-14 years old were shown an excerpt from the popular film Alvin and the Chipmunks and were shown places for the item Cheese Balls. Three different versions were developed with no placements, one with moderately frequent placements and the third with the highest frequency of placement. The results revealed that exposure to high-frequency places had a profound effect on snack consumption, however, there was no impact on consumer attitudes towards brands or products. The effects were not dependent on the age of the children. These findings are of major importance to researchers studying consumer behavior as well as nutrition experts as well as policy regulators."

Abstract Example #2

Social comparisons on social media: The impact of Facebook on young women’s body image concerns and mood. The abstract:

"The research conducted in this study investigated the effects of Facebook use on women's moods and body image if the effects are different from an internet-based fashion journal and if the appearance comparison tendencies moderate one or more of these effects. Participants who were female ( N = 112) were randomly allocated to spend 10 minutes exploring their Facebook account or a magazine's website or an appearance neutral control website prior to completing state assessments of body dissatisfaction, mood, and differences in appearance (weight-related and facial hair, face, and skin). Participants also completed a test of the tendency to compare appearances. The participants who used Facebook were reported to be more depressed than those who stayed on the control site. In addition, women who have the tendency to compare appearances reported more facial, hair and skin-related issues following Facebook exposure than when they were exposed to the control site. Due to its popularity it is imperative to conduct more research to understand the effect that Facebook affects the way people view themselves."

Abstract Example #3

The Relationship Between Cell Phone Use and Academic Performance in a Sample of U.S. College Students

"The cellphone is always present on campuses of colleges and is often utilised in situations in which learning takes place. The study examined the connection between the use of cell phones and the actual grades point average (GPA) after adjusting for predictors that are known to be a factor. In the end 536 students in the undergraduate program from 82 self-reported majors of an enormous, public institution were studied. Hierarchical analysis ( R 2 = .449) showed that use of mobile phones is significantly ( p < .001) and negative (b equal to -.164) connected to the actual college GPA, after taking into account factors such as demographics, self-efficacy in self-regulated learning, self-efficacy to improve academic performance, and the actual high school GPA that were all important predictors ( p < .05). Therefore, after adjusting for other known predictors increasing cell phone usage was associated with lower academic performance. While more research is required to determine the mechanisms behind these results, they suggest the need to educate teachers and students to the possible academic risks that are associated with high-frequency mobile phone usage."

Quick tips on writing a good abstract

There exists a common dilemma among early age researchers whether to write the abstract at first or last? However, it's recommended to compose your abstract when you've completed the research since you'll have all the information to give to your readers. You can, however, write a draft at the beginning of your research and add in any gaps later.

If you find abstract writing a herculean task, here are the few tips to help you with it:

1. Always develop a framework to support your abstract

Before writing, ensure you create a clear outline for your abstract. Divide it into sections and draw the primary and supporting elements in each one. You can include keywords and a few sentences that convey the essence of your message.

2. Review Other Abstracts

Abstracts are among the most frequently used research documents, and thousands of them were written in the past. Therefore, prior to writing yours, take a look at some examples from other abstracts. There are plenty of examples of abstracts for dissertations in the dissertation and thesis databases.

3. Avoid Jargon To the Maximum

When you write your abstract, focus on simplicity over formality. You should  write in simple language, and avoid excessive filler words or ambiguous sentences. Keep in mind that your abstract must be readable to those who aren't acquainted with your subject.

4. Focus on Your Research

It's a given fact that the abstract you write should be about your research and the findings you've made. It is not the right time to mention secondary and primary data sources unless it's absolutely required.

Conclusion: How to Structure an Interesting Abstract?

Abstracts are a short outline of your essay. However, it's among the most important, if not the most important. The process of writing an abstract is not straightforward. A few early-age researchers tend to begin by writing it, thinking they are doing it to "tease" the next step (the document itself). However, it is better to treat it as a spoiler.

The simple, concise style of the abstract lends itself to a well-written and well-investigated study. If your research paper doesn't provide definitive results, or the goal of your research is questioned, so will the abstract. Thus, only write your abstract after witnessing your findings and put your findings in the context of a larger scenario.

The process of writing an abstract can be daunting, but with these guidelines, you will succeed. The most efficient method of writing an excellent abstract is to centre the primary points of your abstract, including the research question and goals methods, as well as key results.

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• How to Write an Abstract | Steps & Examples

How to Write an Abstract | Steps & Examples

Published on 1 March 2019 by Shona McCombes . Revised on 10 October 2022 by Eoghan Ryan.

An abstract is a short summary of a longer work (such as a dissertation or research paper ). The abstract concisely reports the aims and outcomes of your research, so that readers know exactly what your paper is about.

Although the structure may vary slightly depending on your discipline, your abstract should describe the purpose of your work, the methods you’ve used, and the conclusions you’ve drawn.

One common way to structure your abstract is to use the IMRaD structure. This stands for:

• Introduction

Abstracts are usually around 100–300 words, but there’s often a strict word limit, so make sure to check the relevant requirements.

In a dissertation or thesis , include the abstract on a separate page, after the title page and acknowledgements but before the table of contents .

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Table of contents

Abstract example, when to write an abstract, step 1: introduction, step 2: methods, step 3: results, step 4: discussion, tips for writing an abstract, frequently asked questions about abstracts.

Hover over the different parts of the abstract to see how it is constructed.

This paper examines the role of silent movies as a mode of shared experience in the UK during the early twentieth century. At this time, high immigration rates resulted in a significant percentage of non-English-speaking citizens. These immigrants faced numerous economic and social obstacles, including exclusion from public entertainment and modes of discourse (newspapers, theater, radio).

Incorporating evidence from reviews, personal correspondence, and diaries, this study demonstrates that silent films were an affordable and inclusive source of entertainment. It argues for the accessible economic and representational nature of early cinema. These concerns are particularly evident in the low price of admission and in the democratic nature of the actors’ exaggerated gestures, which allowed the plots and action to be easily grasped by a diverse audience despite language barriers.

Keywords: silent movies, immigration, public discourse, entertainment, early cinema, language barriers.

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You will almost always have to include an abstract when:

• Completing a thesis or dissertation
• Submitting a research paper to an academic journal
• Writing a book proposal
• Applying for research grants

It’s easiest to write your abstract last, because it’s a summary of the work you’ve already done. Your abstract should:

• Be a self-contained text, not an excerpt from your paper
• Be fully understandable on its own
• Reflect the structure of your larger work

Start by clearly defining the purpose of your research. What practical or theoretical problem does the research respond to, or what research question did you aim to answer?

You can include some brief context on the social or academic relevance of your topic, but don’t go into detailed background information. If your abstract uses specialised terms that would be unfamiliar to the average academic reader or that have various different meanings, give a concise definition.

After identifying the problem, state the objective of your research. Use verbs like “investigate,” “test,” “analyse,” or “evaluate” to describe exactly what you set out to do.

This part of the abstract can be written in the present or past simple tense  but should never refer to the future, as the research is already complete.

• This study will investigate the relationship between coffee consumption and productivity.
• This study investigates the relationship between coffee consumption and productivity.

Next, indicate the research methods that you used to answer your question. This part should be a straightforward description of what you did in one or two sentences. It is usually written in the past simple tense, as it refers to completed actions.

• Structured interviews will be conducted with 25 participants.
• Structured interviews were conducted with 25 participants.

Don’t evaluate validity or obstacles here — the goal is not to give an account of the methodology’s strengths and weaknesses, but to give the reader a quick insight into the overall approach and procedures you used.

Next, summarise the main research results . This part of the abstract can be in the present or past simple tense.

• Our analysis has shown a strong correlation between coffee consumption and productivity.
• Our analysis shows a strong correlation between coffee consumption and productivity.
• Our analysis showed a strong correlation between coffee consumption and productivity.

Depending on how long and complex your research is, you may not be able to include all results here. Try to highlight only the most important findings that will allow the reader to understand your conclusions.

Finally, you should discuss the main conclusions of your research : what is your answer to the problem or question? The reader should finish with a clear understanding of the central point that your research has proved or argued. Conclusions are usually written in the present simple tense.

• We concluded that coffee consumption increases productivity.
• We conclude that coffee consumption increases productivity.

If there are important limitations to your research (for example, related to your sample size or methods), you should mention them briefly in the abstract. This allows the reader to accurately assess the credibility and generalisability of your research.

If your aim was to solve a practical problem, your discussion might include recommendations for implementation. If relevant, you can briefly make suggestions for further research.

If your paper will be published, you might have to add a list of keywords at the end of the abstract. These keywords should reference the most important elements of the research to help potential readers find your paper during their own literature searches.

Be aware that some publication manuals, such as APA Style , have specific formatting requirements for these keywords.

It can be a real challenge to condense your whole work into just a couple of hundred words, but the abstract will be the first (and sometimes only) part that people read, so it’s important to get it right. These strategies can help you get started.

Read other abstracts

The best way to learn the conventions of writing an abstract in your discipline is to read other people’s. You probably already read lots of journal article abstracts while conducting your literature review —try using them as a framework for structure and style.

You can also find lots of dissertation abstract examples in thesis and dissertation databases .

Reverse outline

Not all abstracts will contain precisely the same elements. For longer works, you can write your abstract through a process of reverse outlining.

For each chapter or section, list keywords and draft one to two sentences that summarise the central point or argument. This will give you a framework of your abstract’s structure. Next, revise the sentences to make connections and show how the argument develops.

Write clearly and concisely

A good abstract is short but impactful, so make sure every word counts. Each sentence should clearly communicate one main point.

To keep your abstract or summary short and clear:

• Avoid passive sentences: Passive constructions are often unnecessarily long. You can easily make them shorter and clearer by using the active voice.
• Avoid long sentences: Substitute longer expressions for concise expressions or single words (e.g., “In order to” for “To”).
• Avoid obscure jargon: The abstract should be understandable to readers who are not familiar with your topic.
• Avoid repetition and filler words: Replace nouns with pronouns when possible and eliminate unnecessary words.
• Avoid detailed descriptions: An abstract is not expected to provide detailed definitions, background information, or discussions of other scholars’ work. Instead, include this information in the body of your thesis or paper.

If you’re struggling to edit down to the required length, you can get help from expert editors with Scribbr’s professional proofreading services .

Check your formatting

If you are writing a thesis or dissertation or submitting to a journal, there are often specific formatting requirements for the abstract—make sure to check the guidelines and format your work correctly. For APA research papers you can follow the APA abstract format .

Checklist: Abstract

The word count is within the required length, or a maximum of one page.

The abstract appears after the title page and acknowledgements and before the table of contents .

I have clearly stated my research problem and objectives.

I have briefly described my methodology .

I have summarized the most important results .

I have stated my main conclusions .

I have mentioned any important limitations and recommendations.

The abstract can be understood by someone without prior knowledge of the topic.

You've written a great abstract! Use the other checklists to continue improving your thesis or dissertation.

An abstract is a concise summary of an academic text (such as a journal article or dissertation ). It serves two main purposes:

• To help potential readers determine the relevance of your paper for their own research.
• To communicate your key findings to those who don’t have time to read the whole paper.

Abstracts are often indexed along with keywords on academic databases, so they make your work more easily findable. Since the abstract is the first thing any reader sees, it’s important that it clearly and accurately summarises the contents of your paper.

An abstract for a thesis or dissertation is usually around 150–300 words. There’s often a strict word limit, so make sure to check your university’s requirements.

The abstract is the very last thing you write. You should only write it after your research is complete, so that you can accurately summarize the entirety of your thesis or paper.

Avoid citing sources in your abstract . There are two reasons for this:

• The abstract should focus on your original research, not on the work of others.
• The abstract should be self-contained and fully understandable without reference to other sources.

There are some circumstances where you might need to mention other sources in an abstract: for example, if your research responds directly to another study or focuses on the work of a single theorist. In general, though, don’t include citations unless absolutely necessary.

The abstract appears on its own page, after the title page and acknowledgements but before the table of contents .

Cite this Scribbr article

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

McCombes, S. (2022, October 10). How to Write an Abstract | Steps & Examples. Scribbr. Retrieved 25 March 2024, from https://www.scribbr.co.uk/thesis-dissertation/abstract/

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How to Write an Abstract (With Examples)

Sarah Oakley

Table of Contents

What is an abstract in a paper, how long should an abstract be, 5 steps for writing an abstract, examples of an abstract, how prowritingaid can help you write an abstract.

If you are writing a scientific research paper or a book proposal, you need to know how to write an abstract, which summarizes the contents of the paper or book.

When researchers are looking for peer-reviewed papers to use in their studies, the first place they will check is the abstract to see if it applies to their work. Therefore, your abstract is one of the most important parts of your entire paper.

In this article, we’ll explain what an abstract is, what it should include, and how to write one.

An abstract is a concise summary of the details within a report. Some abstracts give more details than others, but the main things you’ll be talking about are why you conducted the research, what you did, and what the results show.

When a reader is deciding whether to read your paper completely, they will first look at the abstract. You need to be concise in your abstract and give the reader the most important information so they can determine if they want to read the whole paper.

Remember that an abstract is the last thing you’ll want to write for the research paper because it directly references parts of the report. If you haven’t written the report, you won’t know what to include in your abstract.

If you are writing a paper for a journal or an assignment, the publication or academic institution might have specific formatting rules for how long your abstract should be. However, if they don’t, most abstracts are between 150 and 300 words long.

A short word count means your writing has to be precise and without filler words or phrases. Once you’ve written a first draft, you can always use an editing tool, such as ProWritingAid, to identify areas where you can reduce words and increase readability.

If your abstract is over the word limit, and you’ve edited it but still can’t figure out how to reduce it further, your abstract might include some things that aren’t needed. Here’s a list of three elements you can remove from your abstract:

Discussion : You don’t need to go into detail about the findings of your research because your reader will find your discussion within the paper.

Definition of terms : Your readers are interested the field you are writing about, so they are likely to understand the terms you are using. If not, they can always look them up. Your readers do not expect you to give a definition of terms in your abstract.

References and citations : You can mention there have been studies that support or have inspired your research, but you do not need to give details as the reader will find them in your bibliography.

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If you’ve never written an abstract before, and you’re wondering how to write an abstract, we’ve got some steps for you to follow. It’s best to start with planning your abstract, so we’ve outlined the details you need to include in your plan before you write.

Remember to consider your audience when you’re planning and writing your abstract. They are likely to skim read your abstract, so you want to be sure your abstract delivers all the information they’re expecting to see at key points.

1. What Should an Abstract Include?

Abstracts have a lot of information to cover in a short number of words, so it’s important to know what to include. There are three elements that need to be present in your abstract:

Your context is the background for where your research sits within your field of study. You should briefly mention any previous scientific papers or experiments that have led to your hypothesis and how research develops in those studies.

Your hypothesis is your prediction of what your study will show. As you are writing your abstract after you have conducted your research, you should still include your hypothesis in your abstract because it shows the motivation for your paper.

Throughout your abstract, you also need to include keywords and phrases that will help researchers to find your article in the databases they’re searching. Make sure the keywords are specific to your field of study and the subject you’re reporting on, otherwise your article might not reach the relevant audience.

2. Can You Use First Person in an Abstract?

You might think that first person is too informal for a research paper, but it’s not. Historically, writers of academic reports avoided writing in first person to uphold the formality standards of the time. However, first person is more accepted in research papers in modern times.

If you’re still unsure whether to write in first person for your abstract, refer to any style guide rules imposed by the journal you’re writing for or your teachers if you are writing an assignment.

3. Abstract Structure

Some scientific journals have strict rules on how to structure an abstract, so it’s best to check those first. If you don’t have any style rules to follow, try using the IMRaD structure, which stands for Introduction, Methodology, Results, and Discussion.

Following the IMRaD structure, start with an introduction. The amount of background information you should include depends on your specific research area. Adding a broad overview gives you less room to include other details. Remember to include your hypothesis in this section.

The next part of your abstract should cover your methodology. Try to include the following details if they apply to your study:

What type of research was conducted?

How were the test subjects sampled?

What were the sample sizes?

What was done to each group?

How long was the experiment?

How was data recorded and interpreted?

Following the methodology, include a sentence or two about the results, which is where your reader will determine if your research supports or contradicts their own investigations.

The results are also where most people will want to find out what your outcomes were, even if they are just mildly interested in your research area. You should be specific about all the details but as concise as possible.

The last few sentences are your conclusion. It needs to explain how your findings affect the context and whether your hypothesis was correct. Include the primary take-home message, additional findings of importance, and perspective. Also explain whether there is scope for further research into the subject of your report.

Your conclusion should be honest and give the reader the ultimate message that your research shows. Readers trust the conclusion, so make sure you’re not fabricating the results of your research. Some readers won’t read your entire paper, but this section will tell them if it’s worth them referencing it in their own study.

4. How to Start an Abstract

The first line of your abstract should give your reader the context of your report by providing background information. You can use this sentence to imply the motivation for your research.

You don’t need to use a hook phrase or device in your first sentence to grab the reader’s attention. Your reader will look to establish relevance quickly, so readability and clarity are more important than trying to persuade the reader to read on.

5. How to Format an Abstract

Most abstracts use the same formatting rules, which help the reader identify the abstract so they know where to look for it.

Here’s a list of formatting guidelines for writing an abstract:

Stick to one paragraph

Use block formatting with no indentation at the beginning

Put your abstract straight after the title and acknowledgements pages

Use present or past tense, not future tense

There are two primary types of abstract you could write for your paper—descriptive and informative.

An informative abstract is the most common, and they follow the structure mentioned previously. They are longer than descriptive abstracts because they cover more details.

Descriptive abstracts differ from informative abstracts, as they don’t include as much discussion or detail. The word count for a descriptive abstract is between 50 and 150 words.

Here is an example of an informative abstract:

A growing trend exists for authors to employ a more informal writing style that uses “we” in academic writing to acknowledge one’s stance and engagement. However, few studies have compared the ways in which the first-person pronoun “we” is used in the abstracts and conclusions of empirical papers. To address this lacuna in the literature, this study conducted a systematic corpus analysis of the use of “we” in the abstracts and conclusions of 400 articles collected from eight leading electrical and electronic (EE) engineering journals. The abstracts and conclusions were extracted to form two subcorpora, and an integrated framework was applied to analyze and seek to explain how we-clusters and we-collocations were employed. Results revealed whether authors’ use of first-person pronouns partially depends on a journal policy. The trend of using “we” showed that a yearly increase occurred in the frequency of “we” in EE journal papers, as well as the existence of three “we-use” types in the article conclusions and abstracts: exclusive, inclusive, and ambiguous. Other possible “we-use” alternatives such as “I” and other personal pronouns were used very rarely—if at all—in either section. These findings also suggest that the present tense was used more in article abstracts, but the present perfect tense was the most preferred tense in article conclusions. Both research and pedagogical implications are proffered and critically discussed.

Wang, S., Tseng, W.-T., & Johanson, R. (2021). To We or Not to We: Corpus-Based Research on First-Person Pronoun Use in Abstracts and Conclusions. SAGE Open, 11(2).

Here is an example of a descriptive abstract:

From the 1850s to the present, considerable criminological attention has focused on the development of theoretically-significant systems for classifying crime. This article reviews and attempts to evaluate a number of these efforts, and we conclude that further work on this basic task is needed. The latter part of the article explicates a conceptual foundation for a crime pattern classification system, and offers a preliminary taxonomy of crime.

Farr, K. A., & Gibbons, D. C. (1990). Observations on the Development of Crime Categories. International Journal of Offender Therapy and Comparative Criminology, 34(3), 223–237.

If you want to ensure your abstract is grammatically correct and easy to read, you can use ProWritingAid to edit it. The software integrates with Microsoft Word, Google Docs, and most web browsers, so you can make the most of it wherever you’re writing your paper.

Before you edit with ProWritingAid, make sure the suggestions you are seeing are relevant for your document by changing the document type to “Abstract” within the Academic writing style section.

You can use the Readability report to check your abstract for places to improve the clarity of your writing. Some suggestions might show you where to remove words, which is great if you’re over your word count.

We hope the five steps and examples we’ve provided help you write a great abstract for your research paper.

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Research Paper Abstract – Writing Guide and Examples

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Research Paper Abstract

Research Paper Abstract is a brief summary of a research pape r that describes the study’s purpose, methods, findings, and conclusions . It is often the first section of the paper that readers encounter, and its purpose is to provide a concise and accurate overview of the paper’s content. The typical length of an abstract is usually around 150-250 words, and it should be written in a concise and clear manner.

Research Paper Abstract Structure

The structure of a research paper abstract usually includes the following elements:

• Background or Introduction: Briefly describe the problem or research question that the study addresses.
• Methods : Explain the methodology used to conduct the study, including the participants, materials, and procedures.
• Results : Summarize the main findings of the study, including statistical analyses and key outcomes.
• Conclusions : Discuss the implications of the study’s findings and their significance for the field, as well as any limitations or future directions for research.
• Keywords : List a few keywords that describe the main topics or themes of the research.

How to Write Research Paper Abstract

Here are the steps to follow when writing a research paper abstract:

• Start by reading your paper: Before you write an abstract, you should have a complete understanding of your paper. Read through the paper carefully, making sure you understand the purpose, methods, results, and conclusions.
• Identify the key components : Identify the key components of your paper, such as the research question, methods used, results obtained, and conclusion reached.
• Write a draft: Write a draft of your abstract, using concise and clear language. Make sure to include all the important information, but keep it short and to the point. A good rule of thumb is to keep your abstract between 150-250 words.
• Use clear and concise language : Use clear and concise language to explain the purpose of your study, the methods used, the results obtained, and the conclusions drawn.
• Emphasize your findings: Emphasize your findings in the abstract, highlighting the key results and the significance of your study.
• Revise and edit: Once you have a draft, revise and edit it to ensure that it is clear, concise, and free from errors.
• Check the formatting: Finally, check the formatting of your abstract to make sure it meets the requirements of the journal or conference where you plan to submit it.

Research Paper Abstract Examples

Research Paper Abstract Examples could be following:

Title : “The Effectiveness of Cognitive-Behavioral Therapy for Treating Anxiety Disorders: A Meta-Analysis”

Abstract : This meta-analysis examines the effectiveness of cognitive-behavioral therapy (CBT) in treating anxiety disorders. Through the analysis of 20 randomized controlled trials, we found that CBT is a highly effective treatment for anxiety disorders, with large effect sizes across a range of anxiety disorders, including generalized anxiety disorder, panic disorder, and social anxiety disorder. Our findings support the use of CBT as a first-line treatment for anxiety disorders and highlight the importance of further research to identify the mechanisms underlying its effectiveness.

Title : “Exploring the Role of Parental Involvement in Children’s Education: A Qualitative Study”

Abstract : This qualitative study explores the role of parental involvement in children’s education. Through in-depth interviews with 20 parents of children in elementary school, we found that parental involvement takes many forms, including volunteering in the classroom, helping with homework, and communicating with teachers. We also found that parental involvement is influenced by a range of factors, including parent and child characteristics, school culture, and socio-economic status. Our findings suggest that schools and educators should prioritize building strong partnerships with parents to support children’s academic success.

Title : “The Impact of Exercise on Cognitive Function in Older Adults: A Systematic Review and Meta-Analysis”

Abstract : This paper presents a systematic review and meta-analysis of the existing literature on the impact of exercise on cognitive function in older adults. Through the analysis of 25 randomized controlled trials, we found that exercise is associated with significant improvements in cognitive function, particularly in the domains of executive function and attention. Our findings highlight the potential of exercise as a non-pharmacological intervention to support cognitive health in older adults.

When to Write Research Paper Abstract

The abstract of a research paper should typically be written after you have completed the main body of the paper. This is because the abstract is intended to provide a brief summary of the key points and findings of the research, and you can’t do that until you have completed the research and written about it in detail.

Once you have completed your research paper, you can begin writing your abstract. It is important to remember that the abstract should be a concise summary of your research paper, and should be written in a way that is easy to understand for readers who may not have expertise in your specific area of research.

Purpose of Research Paper Abstract

The purpose of a research paper abstract is to provide a concise summary of the key points and findings of a research paper. It is typically a brief paragraph or two that appears at the beginning of the paper, before the introduction, and is intended to give readers a quick overview of the paper’s content.

The abstract should include a brief statement of the research problem, the methods used to investigate the problem, the key results and findings, and the main conclusions and implications of the research. It should be written in a clear and concise manner, avoiding jargon and technical language, and should be understandable to a broad audience.

The abstract serves as a way to quickly and easily communicate the main points of a research paper to potential readers, such as academics, researchers, and students, who may be looking for information on a particular topic. It can also help researchers determine whether a paper is relevant to their own research interests and whether they should read the full paper.

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Expedite peer review, increase search-ability, and set the tone for your study

The abstract is your chance to let your readers know what they can expect from your article. Learn how to write a clear, and concise abstract that will keep your audience reading.

How your abstract impacts editorial evaluation and future readership

After the title , the abstract is the second-most-read part of your article. A good abstract can help to expedite peer review and, if your article is accepted for publication, it’s an important tool for readers to find and evaluate your work. Editors use your abstract when they first assess your article. Prospective reviewers see it when they decide whether to accept an invitation to review. Once published, the abstract gets indexed in PubMed and Google Scholar , as well as library systems and other popular databases. Like the title, your abstract influences keyword search results. Readers will use it to decide whether to read the rest of your article. Other researchers will use it to evaluate your work for inclusion in systematic reviews and meta-analysis. It should be a concise standalone piece that accurately represents your research. 

What to include in an abstract

The main challenge you’ll face when writing your abstract is keeping it concise AND fitting in all the information you need. Depending on your subject area the journal may require a structured abstract following specific headings. A structured abstract helps your readers understand your study more easily. If your journal doesn’t require a structured abstract it’s still a good idea to follow a similar format, just present the abstract as one paragraph without headings. 

Background or Introduction – What is currently known? Start with a brief, 2 or 3 sentence, introduction to the research area. 

Objectives or Aims – What is the study and why did you do it? Clearly state the research question you’re trying to answer.

Methods – What did you do? Explain what you did and how you did it. Include important information about your methods, but avoid the low-level specifics. Some disciplines have specific requirements for abstract methods. 

• CONSORT for randomized trials.
• STROBE for observational studies
• PRISMA for systematic reviews and meta-analyses

Results – What did you find? Briefly give the key findings of your study. Include key numeric data (including confidence intervals or p values), where possible.

Conclusions – What did you conclude? Tell the reader why your findings matter, and what this could mean for the ‘bigger picture’ of this area of research. 

Writing tips

The main challenge you may find when writing your abstract is keeping it concise AND convering all the information you need to.

• Keep it concise and to the point. Most journals have a maximum word count, so check guidelines before you write the abstract to save time editing it later.
• Write for your audience. Are they specialists in your specific field? Are they cross-disciplinary? Are they non-specialists? If you’re writing for a general audience, or your research could be of interest to the public keep your language as straightforward as possible. If you’re writing in English, do remember that not all of your readers will necessarily be native English speakers.
• Focus on key results, conclusions and take home messages.
• Write your paper first, then create the abstract as a summary.
• Check the journal requirements before you write your abstract, eg. required subheadings.
• Include keywords or phrases to help readers search for your work in indexing databases like PubMed or Google Scholar.
• Double and triple check your abstract for spelling and grammar errors. These kind of errors can give potential reviewers the impression that your research isn’t sound, and can make it easier to find reviewers who accept the invitation to review your manuscript. Your abstract should be a taste of what is to come in the rest of your article.

Don’t

• Sensationalize your research.
• Speculate about where this research might lead in the future.
• Use abbreviations or acronyms (unless absolutely necessary or unless they’re widely known, eg. DNA).
• Repeat yourself unnecessarily, eg. “Methods: We used X technique. Results: Using X technique, we found…”
• Contradict anything in the rest of your manuscript.
• Include content that isn’t also covered in the main manuscript.
• Include citations or references.

Tip: How to edit your work

Editing is challenging, especially if you are acting as both a writer and an editor. Read our guidelines for advice on how to refine your work, including useful tips for setting your intentions, re-review, and consultation with colleagues.

• How to Write a Great Title
• How to Write Your Methods
• How to Report Statistics
• How to Write Discussions and Conclusions
• How to Edit Your Work

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…

What this handout is about

This handout provides definitions and examples of the two main types of abstracts: descriptive and informative. It also provides guidelines for constructing an abstract and general tips for you to keep in mind when drafting. Finally, it includes a few examples of abstracts broken down into their component parts.

What is an abstract?

An abstract is a self-contained, short, and powerful statement that describes a larger work. Components vary according to discipline. An abstract of a social science or scientific work may contain the scope, purpose, results, and contents of the work. An abstract of a humanities work may contain the thesis, background, and conclusion of the larger work. An abstract is not a review, nor does it evaluate the work being abstracted. While it contains key words found in the larger work, the abstract is an original document rather than an excerpted passage.

Why write an abstract?

You may write an abstract for various reasons. The two most important are selection and indexing. Abstracts allow readers who may be interested in a longer work to quickly decide whether it is worth their time to read it. Also, many online databases use abstracts to index larger works. Therefore, abstracts should contain keywords and phrases that allow for easy searching.

Say you are beginning a research project on how Brazilian newspapers helped Brazil’s ultra-liberal president Luiz Ignácio da Silva wrest power from the traditional, conservative power base. A good first place to start your research is to search Dissertation Abstracts International for all dissertations that deal with the interaction between newspapers and politics. “Newspapers and politics” returned 569 hits. A more selective search of “newspapers and Brazil” returned 22 hits. That is still a fair number of dissertations. Titles can sometimes help winnow the field, but many titles are not very descriptive. For example, one dissertation is titled “Rhetoric and Riot in Rio de Janeiro.” It is unclear from the title what this dissertation has to do with newspapers in Brazil. One option would be to download or order the entire dissertation on the chance that it might speak specifically to the topic. A better option is to read the abstract. In this case, the abstract reveals the main focus of the dissertation:

This dissertation examines the role of newspaper editors in the political turmoil and strife that characterized late First Empire Rio de Janeiro (1827-1831). Newspaper editors and their journals helped change the political culture of late First Empire Rio de Janeiro by involving the people in the discussion of state. This change in political culture is apparent in Emperor Pedro I’s gradual loss of control over the mechanisms of power. As the newspapers became more numerous and powerful, the Emperor lost his legitimacy in the eyes of the people. To explore the role of the newspapers in the political events of the late First Empire, this dissertation analyzes all available newspapers published in Rio de Janeiro from 1827 to 1831. Newspapers and their editors were leading forces in the effort to remove power from the hands of the ruling elite and place it under the control of the people. In the process, newspapers helped change how politics operated in the constitutional monarchy of Brazil.

From this abstract you now know that although the dissertation has nothing to do with modern Brazilian politics, it does cover the role of newspapers in changing traditional mechanisms of power. After reading the abstract, you can make an informed judgment about whether the dissertation would be worthwhile to read.

Besides selection, the other main purpose of the abstract is for indexing. Most article databases in the online catalog of the library enable you to search abstracts. This allows for quick retrieval by users and limits the extraneous items recalled by a “full-text” search. However, for an abstract to be useful in an online retrieval system, it must incorporate the key terms that a potential researcher would use to search. For example, if you search Dissertation Abstracts International using the keywords “France” “revolution” and “politics,” the search engine would search through all the abstracts in the database that included those three words. Without an abstract, the search engine would be forced to search titles, which, as we have seen, may not be fruitful, or else search the full text. It’s likely that a lot more than 60 dissertations have been written with those three words somewhere in the body of the entire work. By incorporating keywords into the abstract, the author emphasizes the central topics of the work and gives prospective readers enough information to make an informed judgment about the applicability of the work.

When do people write abstracts?

• when submitting articles to journals, especially online journals
• when applying for research grants
• when writing a book proposal
• when completing the Ph.D. dissertation or M.A. thesis
• when writing a proposal for a conference paper
• when writing a proposal for a book chapter

Most often, the author of the entire work (or prospective work) writes the abstract. However, there are professional abstracting services that hire writers to draft abstracts of other people’s work. In a work with multiple authors, the first author usually writes the abstract. Undergraduates are sometimes asked to draft abstracts of books/articles for classmates who have not read the larger work.

Types of abstracts

There are two types of abstracts: descriptive and informative. They have different aims, so as a consequence they have different components and styles. There is also a third type called critical, but it is rarely used. If you want to find out more about writing a critique or a review of a work, see the UNC Writing Center handout on writing a literature review . If you are unsure which type of abstract you should write, ask your instructor (if the abstract is for a class) or read other abstracts in your field or in the journal where you are submitting your article.

Descriptive abstracts

A descriptive abstract indicates the type of information found in the work. It makes no judgments about the work, nor does it provide results or conclusions of the research. It does incorporate key words found in the text and may include the purpose, methods, and scope of the research. Essentially, the descriptive abstract describes the work being abstracted. Some people consider it an outline of the work, rather than a summary. Descriptive abstracts are usually very short—100 words or less.

Informative abstracts

The majority of abstracts are informative. While they still do not critique or evaluate a work, they do more than describe it. A good informative abstract acts as a surrogate for the work itself. That is, the writer presents and explains all the main arguments and the important results and evidence in the complete article/paper/book. An informative abstract includes the information that can be found in a descriptive abstract (purpose, methods, scope) but also includes the results and conclusions of the research and the recommendations of the author. The length varies according to discipline, but an informative abstract is rarely more than 10% of the length of the entire work. In the case of a longer work, it may be much less.

Here are examples of a descriptive and an informative abstract of this handout on abstracts . Descriptive abstract:

The two most common abstract types—descriptive and informative—are described and examples of each are provided.

Informative abstract:

Abstracts present the essential elements of a longer work in a short and powerful statement. The purpose of an abstract is to provide prospective readers the opportunity to judge the relevance of the longer work to their projects. Abstracts also include the key terms found in the longer work and the purpose and methods of the research. Authors abstract various longer works, including book proposals, dissertations, and online journal articles. There are two main types of abstracts: descriptive and informative. A descriptive abstract briefly describes the longer work, while an informative abstract presents all the main arguments and important results. This handout provides examples of various types of abstracts and instructions on how to construct one.

Which type should I use?

Your best bet in this case is to ask your instructor or refer to the instructions provided by the publisher. You can also make a guess based on the length allowed; i.e., 100-120 words = descriptive; 250+ words = informative.

How do I write an abstract?

The format of your abstract will depend on the work being abstracted. An abstract of a scientific research paper will contain elements not found in an abstract of a literature article, and vice versa. However, all abstracts share several mandatory components, and there are also some optional parts that you can decide to include or not. When preparing to draft your abstract, keep the following key process elements in mind:

• Reason for writing: What is the importance of the research? Why would a reader be interested in the larger work?
• Problem: What problem does this work attempt to solve? What is the scope of the project? What is the main argument/thesis/claim?
• Methodology: An abstract of a scientific work may include specific models or approaches used in the larger study. Other abstracts may describe the types of evidence used in the research.
• Results: Again, an abstract of a scientific work may include specific data that indicates the results of the project. Other abstracts may discuss the findings in a more general way.
• Implications: What changes should be implemented as a result of the findings of the work? How does this work add to the body of knowledge on the topic?

(This list of elements is adapted with permission from Philip Koopman, “How to Write an Abstract.” )

All abstracts include:

• A full citation of the source, preceding the abstract.
• The most important information first.
• The same type and style of language found in the original, including technical language.
• Key words and phrases that quickly identify the content and focus of the work.
• Clear, concise, and powerful language.

Abstracts may include:

• The thesis of the work, usually in the first sentence.
• Background information that places the work in the larger body of literature.
• The same chronological structure as the original work.

How not to write an abstract:

• Do not refer extensively to other works.
• Do not add information not contained in the original work.
• Do not define terms.

If you are abstracting your own writing

When abstracting your own work, it may be difficult to condense a piece of writing that you have agonized over for weeks (or months, or even years) into a 250-word statement. There are some tricks that you could use to make it easier, however.

Reverse outlining:

This technique is commonly used when you are having trouble organizing your own writing. The process involves writing down the main idea of each paragraph on a separate piece of paper– see our short video . For the purposes of writing an abstract, try grouping the main ideas of each section of the paper into a single sentence. Practice grouping ideas using webbing or color coding .

For a scientific paper, you may have sections titled Purpose, Methods, Results, and Discussion. Each one of these sections will be longer than one paragraph, but each is grouped around a central idea. Use reverse outlining to discover the central idea in each section and then distill these ideas into one statement.

Cut and paste:

To create a first draft of an abstract of your own work, you can read through the entire paper and cut and paste sentences that capture key passages. This technique is useful for social science research with findings that cannot be encapsulated by neat numbers or concrete results. A well-written humanities draft will have a clear and direct thesis statement and informative topic sentences for paragraphs or sections. Isolate these sentences in a separate document and work on revising them into a unified paragraph.

If you are abstracting someone else’s writing

When abstracting something you have not written, you cannot summarize key ideas just by cutting and pasting. Instead, you must determine what a prospective reader would want to know about the work. There are a few techniques that will help you in this process:

Identify key terms:

Search through the entire document for key terms that identify the purpose, scope, and methods of the work. Pay close attention to the Introduction (or Purpose) and the Conclusion (or Discussion). These sections should contain all the main ideas and key terms in the paper. When writing the abstract, be sure to incorporate the key terms.

Highlight key phrases and sentences:

Instead of cutting and pasting the actual words, try highlighting sentences or phrases that appear to be central to the work. Then, in a separate document, rewrite the sentences and phrases in your own words.

Don’t look back:

After reading the entire work, put it aside and write a paragraph about the work without referring to it. In the first draft, you may not remember all the key terms or the results, but you will remember what the main point of the work was. Remember not to include any information you did not get from the work being abstracted.

Revise, revise, revise

No matter what type of abstract you are writing, or whether you are abstracting your own work or someone else’s, the most important step in writing an abstract is to revise early and often. When revising, delete all extraneous words and incorporate meaningful and powerful words. The idea is to be as clear and complete as possible in the shortest possible amount of space. The Word Count feature of Microsoft Word can help you keep track of how long your abstract is and help you hit your target length.

Example 1: Humanities abstract

Kenneth Tait Andrews, “‘Freedom is a constant struggle’: The dynamics and consequences of the Mississippi Civil Rights Movement, 1960-1984” Ph.D. State University of New York at Stony Brook, 1997 DAI-A 59/02, p. 620, Aug 1998

This dissertation examines the impacts of social movements through a multi-layered study of the Mississippi Civil Rights Movement from its peak in the early 1960s through the early 1980s. By examining this historically important case, I clarify the process by which movements transform social structures and the constraints movements face when they try to do so. The time period studied includes the expansion of voting rights and gains in black political power, the desegregation of public schools and the emergence of white-flight academies, and the rise and fall of federal anti-poverty programs. I use two major research strategies: (1) a quantitative analysis of county-level data and (2) three case studies. Data have been collected from archives, interviews, newspapers, and published reports. This dissertation challenges the argument that movements are inconsequential. Some view federal agencies, courts, political parties, or economic elites as the agents driving institutional change, but typically these groups acted in response to the leverage brought to bear by the civil rights movement. The Mississippi movement attempted to forge independent structures for sustaining challenges to local inequities and injustices. By propelling change in an array of local institutions, movement infrastructures had an enduring legacy in Mississippi.

Now let’s break down this abstract into its component parts to see how the author has distilled his entire dissertation into a ~200 word abstract.

What the dissertation does This dissertation examines the impacts of social movements through a multi-layered study of the Mississippi Civil Rights Movement from its peak in the early 1960s through the early 1980s. By examining this historically important case, I clarify the process by which movements transform social structures and the constraints movements face when they try to do so.

How the dissertation does it The time period studied in this dissertation includes the expansion of voting rights and gains in black political power, the desegregation of public schools and the emergence of white-flight academies, and the rise and fall of federal anti-poverty programs. I use two major research strategies: (1) a quantitative analysis of county-level data and (2) three case studies.

What materials are used Data have been collected from archives, interviews, newspapers, and published reports.

Conclusion This dissertation challenges the argument that movements are inconsequential. Some view federal agencies, courts, political parties, or economic elites as the agents driving institutional change, but typically these groups acted in response to movement demands and the leverage brought to bear by the civil rights movement. The Mississippi movement attempted to forge independent structures for sustaining challenges to local inequities and injustices. By propelling change in an array of local institutions, movement infrastructures had an enduring legacy in Mississippi.

Keywords social movements Civil Rights Movement Mississippi voting rights desegregation

Example 2: Science Abstract

Luis Lehner, “Gravitational radiation from black hole spacetimes” Ph.D. University of Pittsburgh, 1998 DAI-B 59/06, p. 2797, Dec 1998

The problem of detecting gravitational radiation is receiving considerable attention with the construction of new detectors in the United States, Europe, and Japan. The theoretical modeling of the wave forms that would be produced in particular systems will expedite the search for and analysis of detected signals. The characteristic formulation of GR is implemented to obtain an algorithm capable of evolving black holes in 3D asymptotically flat spacetimes. Using compactification techniques, future null infinity is included in the evolved region, which enables the unambiguous calculation of the radiation produced by some compact source. A module to calculate the waveforms is constructed and included in the evolution algorithm. This code is shown to be second-order convergent and to handle highly non-linear spacetimes. In particular, we have shown that the code can handle spacetimes whose radiation is equivalent to a galaxy converting its whole mass into gravitational radiation in one second. We further use the characteristic formulation to treat the region close to the singularity in black hole spacetimes. The code carefully excises a region surrounding the singularity and accurately evolves generic black hole spacetimes with apparently unlimited stability.

This science abstract covers much of the same ground as the humanities one, but it asks slightly different questions.

Why do this study The problem of detecting gravitational radiation is receiving considerable attention with the construction of new detectors in the United States, Europe, and Japan. The theoretical modeling of the wave forms that would be produced in particular systems will expedite the search and analysis of the detected signals.

What the study does The characteristic formulation of GR is implemented to obtain an algorithm capable of evolving black holes in 3D asymptotically flat spacetimes. Using compactification techniques, future null infinity is included in the evolved region, which enables the unambiguous calculation of the radiation produced by some compact source. A module to calculate the waveforms is constructed and included in the evolution algorithm.

Results This code is shown to be second-order convergent and to handle highly non-linear spacetimes. In particular, we have shown that the code can handle spacetimes whose radiation is equivalent to a galaxy converting its whole mass into gravitational radiation in one second. We further use the characteristic formulation to treat the region close to the singularity in black hole spacetimes. The code carefully excises a region surrounding the singularity and accurately evolves generic black hole spacetimes with apparently unlimited stability.

Keywords gravitational radiation (GR) spacetimes black holes

Works consulted

We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.

Belcher, Wendy Laura. 2009. Writing Your Journal Article in Twelve Weeks: A Guide to Academic Publishing Success. Thousand Oaks, CA: Sage Press.

Koopman, Philip. 1997. “How to Write an Abstract.” Carnegie Mellon University. October 1997. http://users.ece.cmu.edu/~koopman/essays/abstract.html .

Lancaster, F.W. 2003. Indexing And Abstracting in Theory and Practice , 3rd ed. London: Facet Publishing.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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How to craft an APA abstract

Last updated

16 December 2023

Reviewed by

An APA abstract is a brief but thorough summary of a scientific paper. It gives readers a clear overview of what the paper is about and what it intends to prove.

The purpose of an abstract is to allow researchers to quickly understand the paper's topic and purpose so they can decide whether it will be useful to them.

• What is the APA style?

APA style is a method of formatting and documentation used by the American Psychological Association. This style is used primarily for papers in the field of education and in the social sciences, including:

Anthropology

What is an abstract in APA format?

Writing an abstract in APA format requires you to conform to the writing rules for APA-style papers, including the following guidelines:

The abstract should be 150–250 words

It should be brief but concise, containing all the paper's main points

The abstract is a separate page that comes after the title page and before the paper's main content

• Key elements of an APA abstract 

While the rules for constructing an APA abstract are straightforward, the process can be challenging. You need to pack a great deal of relevant content into a short piece.

The essential elements of an APA abstract are:

Running header containing the title of the paper and page number

Section label, centered and in bold, containing the word "abstract"

The main content of the abstract, 150–250 words in length and double-spaced

A list of keywords, indented and introduced with the word "keywords" in italics

Essential points to cover in an APA abstract  

When you’re creating your APA abstract, consider the following questions.

What is the main topic the paper is addressing?

People searching for research on your topic will probably be browsing many papers and studies. The way your abstract is crafted will help to determine whether they feel your paper is worth reading.

Are your research methods quantitative or qualitative?

Quantitative research is focused on numbers and statistics, typically gathered from studies and polls where the questions are in yes/no or multiple-choice format.

Qualitative research is based on language and gathered using methods such as interviews and focus groups. It is more detailed and time-consuming to gather than quantitative research but can yield more complex and nuanced results.

Did you use primary or secondary sources?

Another key element is whether your research is based on primary or secondary sources. 

Primary research is data that you or your research team gathered. Secondary research is gathered from existing sources, such as databases or previously published studies.

Is your research descriptive or experimental?

Your research may be descriptive, experimental, or both.

With descriptive research , you’re describing or analyzing existing studies or theories on the topic. You may be using surveys, case studies, or observation to study the topic.

Experimental research studies variables using the scientific method. With an experiment, your objective is to establish a cause-and-effect relationship between two variables (or show the lack of one).

What conclusion did you reach?

Readers will want to know upfront what your paper is claiming or proving. Your APA abstract should give them a condensed version of your conclusions. Summarize your most significant findings.

It's customary to place your findings and conclusion in the final sentence of the abstract. This should be directly related to the main topic of the paper.

What is the relevance of your findings?

Show readers that your paper is a significant contribution to the field. While staying accurate and not overstating your case, boast a bit about why people need to read your paper.

Briefly describe the implications and importance of your findings. You can also point out any further research that is needed concerning this topic.

Did you choose the most appropriate keywords?

Including keywords is useful for indexing if your paper is eventually included in a database. Choose keywords that are relevant to the paper and as specific as possible.

For example, if your paper is about signs of learning disabilities in elementary-age children, your keyword list might include:

Learning disability symptoms

Elementary education

Language-based learning disabilities

Any other terms discussed in the paper

• How to format an APA abstract

Use standard APA formatting with double spacing, 12pt Times New Roman font, and one-inch margins.

Place a running head at the top left-hand side of the page. This is an abbreviated version of the paper's title. Use all capital letters for the running header. This is not usually required for academic papers but is essential if you are submitting the paper for publication. The page number “2” should follow the running header (Page 1 is the title page).

Just under the running head, in the center, place the word "abstract."

Place your list of keywords at the end. The list should be indented and, according to APA guidelines, contain three to five keywords.

• What are the 3 types of abstracts?

There are certain variations in different types of APA abstracts. Here are three of the most common ones.

Experimental or lab report abstracts

An abstract for an experimental or lab report needs to communicate the key purpose and findings of the experiment. Include the following:

Purpose and importance of the experiment

Hypothesis of the experiment

Methods used to test the hypothesis

Summary of the results of the experiment, including whether you proved or rejected the hypothesis

Literature review abstracts

A literature review is a survey of published work on a work of literature. It may be part of a thesis, dissertation, or research paper .

The abstract for a literature review should contain:

A description of your purpose for covering the research topic

Your thesis statement

A description of the sources used in the review

Your conclusions based on the findings

Psychology lab reports

Psychology lab reports are part of the experiment report category. Psychology experiments, however, may contain distinctive elements.

Describe the goal or purpose of the experiment

If the experiment includes human subjects, describe them. Mention the number of participants and what demographic they fit

Describe any tools, equipment, or apparatus you used for the experiment. For example, some experiments use electroencephalography (EEG) to measure brain waves. You may have also used tools such as questionnaires , case studies , or naturalistic observation. Describe the procedure and parameters of the experiment.

Summarize your conclusions

• What not to include in an APA abstract

As this section is 250 words maximum, it's important to know what should not be included.

Avoid the following in an APA abstract:

Jargon, acronyms, or abbreviations

Citations. These should appear in the body of the paper.

Lengthy or secondary information. Keep it brief and stick to the main points. Readers should want to read your paper for more detailed information.

Opinions or subjective comments

Anything not covered in the paper

• Guidelines for writing an APA abstract

While an abstract is the shortest section of your paper, it is nevertheless one of the most important parts. It determines whether or not someone decides that the paper is worth reading or not. What follows are some guidelines to keep in mind when creating your APA abstract. 

Focus on your main point. Don't try to fit in multiple conclusions. The idea is to give readers a clear idea of what your main point or conclusion is. On a similar note, be explicit about the implications and significance of your findings. This is what will motivate people to read your paper.

Write the abstract last. Ensure the abstract accurately conveys the content and conclusions of your paper. You may want to start with a rough draft of the abstract, which you can use as an outline to guide you when writing your paper. If you do this, make sure you edit and update the abstract after the full paper is complete.

Proofread your abstract. As the abstract is short and the first part of the paper people will read, it's especially important to make it clear and free of spelling, grammatical, or factual errors. Ask someone in your field to read through it.

Write the abstract for a general audience. While the paper may be aimed at academics, scientists, or specialists in your field, the abstract should be accessible to a broad audience. Minimize jargon and acronyms. This will make the paper easier to find by people looking for information on the topic.

Choose your keywords with care. The more relevant keywords you include, the more searchable your paper will be. Look up papers on comparable topics for guidance.

Follow any specific guidelines that apply to your paper. Requirements for the abstract may differ slightly depending on the topic or guidelines set by a particular instructor or publication.

APA style is commonly used in the fields of psychology, sociology, anthropology, economics, and education.

If you’re writing an abstract in APA style, there are certain conventions to follow. Your readers and people in your industry will expect you to adhere to particular elements of layout, content, and structure.

Follow our advice in this article, and you will be confident that your APA abstract complies with the expected standards and will encourage people to read your full paper.

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How to Write an Abstract APA Format

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

An APA abstract is a brief, comprehensive summary of the contents of an article, research paper, dissertation, or report.

It is written in accordance with the guidelines of the American Psychological Association (APA), which is a widely used format in social and behavioral sciences. 

An APA abstract summarizes, usually in one paragraph of between 150–250 words, the major aspects of a research paper or dissertation in a prescribed sequence that includes:

• The rationale: the overall purpose of the study, providing a clear context for the research undertaken.
• Information regarding the method and participants: including materials/instruments, design, procedure, and data analysis.
• Main findings or trends: effectively highlighting the key outcomes of the hypotheses.
• Interpretations and conclusion(s): solidify the implications of the research.
• Keywords related to the study: assist the paper’s discoverability in academic databases.

The abstract should stand alone, be “self-contained,” and make sense to the reader in isolation from the main article.

The purpose of the abstract is to give the reader a quick overview of the essential information before reading the entire article. The abstract is placed on its own page, directly after the title page and before the main body of the paper.

Although the abstract will appear as the very first part of your paper, it’s good practice to write your abstract after you’ve drafted your full paper, so that you know what you’re summarizing.

Note : This page reflects the latest version of the APA Publication Manual (i.e., APA 7), released in October 2019.

Structure of the Abstract

[NOTE: DO NOT separate the components of the abstract – it should be written as a single paragraph. This section is separated to illustrate the abstract’s structure.]

1) The Rationale

One or two sentences describing the overall purpose of the study and the research problem(s) you investigated. You are basically justifying why this study was conducted.

• What is the importance of the research?
• Why would a reader be interested in the larger work?
• For example, are you filling a gap in previous research or applying new methods to take a fresh look at existing ideas or data?
• Women who are diagnosed with breast cancer can experience an array of psychosocial difficulties; however, social support, particularly from a spouse, has been shown to have a protective function during this time. This study examined the ways in which a woman’s daily mood, pain, and fatigue, and her spouse’s marital satisfaction predict the woman’s report of partner support in the context of breast cancer.
• The current nursing shortage, high hospital nurse job dissatisfaction, and reports of uneven quality of hospital care are not uniquely American phenomena.
• Students with special educational needs and disabilities (SEND) are more likely to exhibit behavioral difficulties than their typically developing peers. The aim of this study was to identify specific risk factors that influence variability in behavior difficulties among individuals with SEND.

2) The Method

Information regarding the participants (number, and population). One or two sentences outlining the method, explaining what was done and how. The method is described in the present tense.

• Pretest data from a larger intervention study and multilevel modeling were used to examine the effects of women’s daily mood, pain, and fatigue and average levels of mood, pain, and fatigue on women’s report of social support received from her partner, as well as how the effects of mood interacted with partners’ marital satisfaction.
• This paper presents reports from 43,000 nurses from more than 700 hospitals in the United States, Canada, England, Scotland, and Germany in 1998–1999.
• The study sample comprised 4,228 students with SEND, aged 5–15, drawn from 305 primary and secondary schools across England. Explanatory variables were measured at the individual and school levels at baseline, along with a teacher-reported measure of behavior difficulties (assessed at baseline and the 18-month follow-up).

3) The Results

One or two sentences indicating the main findings or trends found as a result of your analysis. The results are described in the present or past tense.

• Results show that on days in which women reported higher levels of negative or positive mood, as well as on days they reported more pain and fatigue, they reported receiving more support. Women who, on average, reported higher levels of positive mood tended to report receiving more support than those who, on average, reported lower positive mood. However, average levels of negative mood were not associated with support. Higher average levels of fatigue but not pain were associated with higher support. Finally, women whose husbands reported higher levels of marital satisfaction reported receiving more partner support, but husbands’ marital satisfaction did not moderate the effect of women’s mood on support.
• Nurses in countries with distinctly different healthcare systems report similar shortcomings in their work environments and the quality of hospital care. While the competence of and relation between nurses and physicians appear satisfactory, core problems in work design and workforce management threaten the provision of care.
• Hierarchical linear modeling of data revealed that differences between schools accounted for between 13% (secondary) and 15.4% (primary) of the total variance in the development of students’ behavior difficulties, with the remainder attributable to individual differences. Statistically significant risk markers for these problems across both phases of education were being male, eligibility for free school meals, being identified as a bully, and lower academic achievement. Additional risk markers specific to each phase of education at the individual and school levels are also acknowledged.

4) The Conclusion / Implications

A brief summary of your conclusions and implications of the results, described in the present tense. Explain the results and why the study is important to the reader.

• For example, what changes should be implemented as a result of the findings of the work?
• How does this work add to the body of knowledge on the topic?

Implications of these findings are discussed relative to assisting couples during this difficult time in their lives.

• Resolving these issues, which are amenable to managerial intervention, is essential to preserving patient safety and care of consistently high quality.
• Behavior difficulties are affected by risks across multiple ecological levels. Addressing any one of these potential influences is therefore likely to contribute to the reduction in the problems displayed.

The above examples of abstracts are from the following papers:

Aiken, L. H., Clarke, S. P., Sloane, D. M., Sochalski, J. A., Busse, R., Clarke, H., … & Shamian, J. (2001). Nurses’ reports on hospital care in five countries . Health affairs, 20(3) , 43-53.

Boeding, S. E., Pukay-Martin, N. D., Baucom, D. H., Porter, L. S., Kirby, J. S., Gremore, T. M., & Keefe, F. J. (2014). Couples and breast cancer: Women’s mood and partners’ marital satisfaction predicting support perception . Journal of Family Psychology, 28(5) , 675.

Oldfield, J., Humphrey, N., & Hebron, J. (2017). Risk factors in the development of behavior difficulties among students with special educational needs and disabilities: A multilevel analysis . British journal of educational psychology, 87(2) , 146-169.

5) Keywords

APA style suggests including a list of keywords at the end of the abstract. This is particularly common in academic articles and helps other researchers find your work in databases.

Keywords in an abstract should be selected to help other researchers find your work when searching an online database. These keywords should effectively represent the main topics of your study. Here are some tips for choosing keywords:

Core Concepts: Identify the most important ideas or concepts in your paper. These often include your main research topic, the methods you’ve used, or the theories you’re discussing.

Specificity: Your keywords should be specific to your research. For example, suppose your paper is about the effects of climate change on bird migration patterns in a specific region. In that case, your keywords might include “climate change,” “bird migration,” and the region’s name.

Consistency with Paper: Make sure your keywords are consistent with the terms you’ve used in your paper. For example, if you use the term “adolescent” rather than “teen” in your paper, choose “adolescent” as your keyword, not “teen.”

Jargon and Acronyms: Avoid using too much-specialized jargon or acronyms in your keywords, as these might not be understood or used by all researchers in your field.

Synonyms: Consider including synonyms of your keywords to capture as many relevant searches as possible. For example, if your paper discusses “post-traumatic stress disorder,” you might include “PTSD” as a keyword.

Remember, keywords are a tool for others to find your work, so think about what terms other researchers might use when searching for papers on your topic.

The Abstract SHOULD NOT contain:

Lengthy background or contextual information: The abstract should focus on your research and findings, not general topic background.

Undefined jargon, abbreviations,  or acronyms: The abstract should be accessible to a wide audience, so avoid highly specialized terms without defining them.

Citations: Abstracts typically do not include citations, as they summarize original research.

Incomplete sentences or bulleted lists: The abstract should be a single, coherent paragraph written in complete sentences.

New information not covered in the paper: The abstract should only summarize the paper’s content.

Subjective comments or value judgments: Stick to objective descriptions of your research.

Excessive details on methods or procedures: Keep descriptions of methods brief and focused on main steps.

Speculative or inconclusive statements: The abstract should state the research’s clear findings, not hypotheses or possible interpretations.

• Any illustration, figure, table, or references to them . All visual aids, data, or extensive details should be included in the main body of your paper, not in the abstract. 
• Elliptical or incomplete sentences should be avoided in an abstract . The use of ellipses (…), which could indicate incomplete thoughts or omitted text, is not appropriate in an abstract.

APA Style for Abstracts

An APA abstract must be formatted as follows:

Include the running head aligned to the left at the top of the page (professional papers only) and page number. Note, student papers do not require a running head. On the first line, center the heading “Abstract” and bold (do not underlined or italicize). Do not indent the single abstract paragraph (which begins one line below the section title). Double-space the text. Use Times New Roman font in 12 pt. Set one-inch (or 2.54 cm) margins. If you include a “keywords” section at the end of the abstract, indent the first line and italicize the word “Keywords” while leaving the keywords themselves without any formatting.

Example APA Abstract Page

Download this example as a PDF

Further Information

• APA 7th Edition Abstract and Keywords Guide
• Example APA Abstract
• How to Write a Good Abstract for a Scientific Paper or Conference Presentation
• How to Write a Lab Report
• Writing an APA paper

How long should an APA abstract be?

An APA abstract should typically be between 150 to 250 words long. However, the exact length may vary depending on specific publication or assignment guidelines. It is crucial that it succinctly summarizes the essential elements of the work, including purpose, methods, findings, and conclusions.

Where does the abstract go in an APA paper?

In an APA formatted paper, the abstract is placed on its own page, directly after the title page and before the main body of the paper. It’s typically the second page of the document. It starts with the word “Abstract” (centered and not in bold) at the top of the page, followed by the text of the abstract itself.

What are the 4 C’s of abstract writing?

The 4 C’s of abstract writing are an approach to help you create a well-structured and informative abstract. They are:

Conciseness: An abstract should briefly summarize the key points of your study. Stick to the word limit (typically between 150-250 words for an APA abstract) and avoid unnecessary details.

Clarity: Your abstract should be easy to understand. Avoid jargon and complex sentences. Clearly explain the purpose, methods, results, and conclusions of your study.

Completeness: Even though it’s brief, the abstract should provide a complete overview of your study, including the purpose, methods, key findings, and your interpretation of the results.

Cohesion: The abstract should flow logically from one point to the next, maintaining a coherent narrative about your study. It’s not just a list of disjointed elements; it’s a brief story of your research from start to finish.

What is the abstract of a psychology paper?

An abstract in a psychology paper serves as a snapshot of the paper, allowing readers to quickly understand the purpose, methodology, results, and implications of the research without reading the entire paper. It is generally between 150-250 words long.

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An abstract summarizes, usually in one paragraph of 300 words or less, the major aspects of the entire paper in a prescribed sequence that includes: 1) the overall purpose of the study and the research problem(s) you investigated; 2) the basic design of the study; 3) major findings or trends found as a result of your analysis; and, 4) a brief summary of your interpretations and conclusions.

Writing an Abstract. The Writing Center. Clarion University, 2009; Writing an Abstract for Your Research Paper. The Writing Center, University of Wisconsin, Madison; Koltay, Tibor. Abstracts and Abstracting: A Genre and Set of Skills for the Twenty-first Century . Oxford, UK: Chandos Publishing, 2010;

Importance of a Good Abstract

Sometimes your professor will ask you to include an abstract, or general summary of your work, with your research paper. The abstract allows you to elaborate upon each major aspect of the paper and helps readers decide whether they want to read the rest of the paper. Therefore, enough key information [e.g., summary results, observations, trends, etc.] must be included to make the abstract useful to someone who may want to examine your work.

How do you know when you have enough information in your abstract? A simple rule-of-thumb is to imagine that you are another researcher doing a similar study. Then ask yourself: if your abstract was the only part of the paper you could access, would you be happy with the amount of information presented there? Does it tell the whole story about your study? If the answer is "no" then the abstract likely needs to be revised.

Farkas, David K. “A Scheme for Understanding and Writing Summaries.” Technical Communication 67 (August 2020): 45-60;  How to Write a Research Abstract. Office of Undergraduate Research. University of Kentucky; Staiger, David L. “What Today’s Students Need to Know about Writing Abstracts.” International Journal of Business Communication January 3 (1966): 29-33; Swales, John M. and Christine B. Feak. Abstracts and the Writing of Abstracts . Ann Arbor, MI: University of Michigan Press, 2009.

Structure and Writing Style

I.  Types of Abstracts

To begin, you need to determine which type of abstract you should include with your paper. There are four general types.

Critical Abstract A critical abstract provides, in addition to describing main findings and information, a judgment or comment about the study’s validity, reliability, or completeness. The researcher evaluates the paper and often compares it with other works on the same subject. Critical abstracts are generally 400-500 words in length due to the additional interpretive commentary. These types of abstracts are used infrequently.

Descriptive Abstract A descriptive abstract indicates the type of information found in the work. It makes no judgments about the work, nor does it provide results or conclusions of the research. It does incorporate key words found in the text and may include the purpose, methods, and scope of the research. Essentially, the descriptive abstract only describes the work being summarized. Some researchers consider it an outline of the work, rather than a summary. Descriptive abstracts are usually very short, 100 words or less. Informative Abstract The majority of abstracts are informative. While they still do not critique or evaluate a work, they do more than describe it. A good informative abstract acts as a surrogate for the work itself. That is, the researcher presents and explains all the main arguments and the important results and evidence in the paper. An informative abstract includes the information that can be found in a descriptive abstract [purpose, methods, scope] but it also includes the results and conclusions of the research and the recommendations of the author. The length varies according to discipline, but an informative abstract is usually no more than 300 words in length.

Highlight Abstract A highlight abstract is specifically written to attract the reader’s attention to the study. No pretense is made of there being either a balanced or complete picture of the paper and, in fact, incomplete and leading remarks may be used to spark the reader’s interest. In that a highlight abstract cannot stand independent of its associated article, it is not a true abstract and, therefore, rarely used in academic writing.

II.  Writing Style

Use the active voice when possible , but note that much of your abstract may require passive sentence constructions. Regardless, write your abstract using concise, but complete, sentences. Get to the point quickly and always use the past tense because you are reporting on a study that has been completed.

Abstracts should be formatted as a single paragraph in a block format and with no paragraph indentations. In most cases, the abstract page immediately follows the title page. Do not number the page. Rules set forth in writing manual vary but, in general, you should center the word "Abstract" at the top of the page with double spacing between the heading and the abstract. The final sentences of an abstract concisely summarize your study’s conclusions, implications, or applications to practice and, if appropriate, can be followed by a statement about the need for additional research revealed from the findings.

Composing Your Abstract

Although it is the first section of your paper, the abstract should be written last since it will summarize the contents of your entire paper. A good strategy to begin composing your abstract is to take whole sentences or key phrases from each section of the paper and put them in a sequence that summarizes the contents. Then revise or add connecting phrases or words to make the narrative flow clearly and smoothly. Note that statistical findings should be reported parenthetically [i.e., written in parentheses].

Before handing in your final paper, check to make sure that the information in the abstract completely agrees with what you have written in the paper. Think of the abstract as a sequential set of complete sentences describing the most crucial information using the fewest necessary words. The abstract SHOULD NOT contain:

• A catchy introductory phrase, provocative quote, or other device to grab the reader's attention,
• Lengthy background or contextual information,
• Redundant phrases, unnecessary adverbs and adjectives, and repetitive information;
• Acronyms or abbreviations,
• References to other literature [say something like, "current research shows that..." or "studies have indicated..."],
• Using ellipticals [i.e., ending with "..."] or incomplete sentences,
• Jargon or terms that may be confusing to the reader,
• Citations to other works, and
• Any sort of image, illustration, figure, or table, or references to them.

Abstract. Writing Center. University of Kansas; Abstract. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Abstracts. The Writing Center. University of North Carolina; Borko, Harold and Seymour Chatman. "Criteria for Acceptable Abstracts: A Survey of Abstracters' Instructions." American Documentation 14 (April 1963): 149-160; Abstracts. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Hartley, James and Lucy Betts. "Common Weaknesses in Traditional Abstracts in the Social Sciences." Journal of the American Society for Information Science and Technology 60 (October 2009): 2010-2018; Koltay, Tibor. Abstracts and Abstracting: A Genre and Set of Skills for the Twenty-first Century. Oxford, UK: Chandos Publishing, 2010; Procter, Margaret. The Abstract. University College Writing Centre. University of Toronto; Riordan, Laura. “Mastering the Art of Abstracts.” The Journal of the American Osteopathic Association 115 (January 2015 ): 41-47; Writing Report Abstracts. The Writing Lab and The OWL. Purdue University; Writing Abstracts. Writing Tutorial Services, Center for Innovative Teaching and Learning. Indiana University; Koltay, Tibor. Abstracts and Abstracting: A Genre and Set of Skills for the Twenty-First Century . Oxford, UK: 2010; Writing an Abstract for Your Research Paper. The Writing Center, University of Wisconsin, Madison.

Writing Tip

Never Cite Just the Abstract!

Citing to just a journal article's abstract does not confirm for the reader that you have conducted a thorough or reliable review of the literature. If the full-text is not available, go to the USC Libraries main page and enter the title of the article [NOT the title of the journal]. If the Libraries have a subscription to the journal, the article should appear with a link to the full-text or to the journal publisher page where you can get the article. If the article does not appear, try searching Google Scholar using the link on the USC Libraries main page. If you still can't find the article after doing this, contact a librarian or you can request it from our free i nterlibrary loan and document delivery service .

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How to Write an Abstract for a Research Paper

Writing Informative Abstracts

Informative abstracts state in one paragraph the essence of a whole paper about a study or a research project. That one paragraph must mention all the main points or parts of the paper: a description of the study or project, its methods, the results, and the conclusions. Here is an example of the abstract accompanying a seven-page essay that appeared in 2002 in  The Journal of Clinical Psychology :

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The relationship between boredom proneness and health-symptom reporting was examined. Undergraduate students (N = 200) completed the Boredom Proneness Scale and the Hopkins Symptom Checklist. A multiple analysis of covariance indicated that individuals with high boredom-proneness total scores reported significantly higher ratings on all five sub-scales of the Hopkins Symptom Checklist (Obsessive–Compulsive, Somatization, Anxiety, Interpersonal Sensitivity, and Depression). The results suggest that boredom proneness may be an important element to consider when assessing symptom reporting. Implications for determining the effects of boredom proneness on psychological- and physical-health symptoms, as well as the application in clinical settings, are discussed. —Jennifer Sommers and Stephen J. Vodanovich, (adsbygoogle = window.adsbygoogle || []).push({}); “Boredom Proneness”

The first sentence states the nature of the study being reported. The next summarizes the method used to investigate the problem, and the following one gives the results: students who, according to specific tests, are more likely to be bored are also more likely to have certain medical or psychological symptoms. The last two sentences indicate that the paper discusses those results and examines the conclusion and its implications.

Writing Descriptive Abstracts

Descriptive abstracts are usually much briefer than informative abstracts and provide much less information. Rather than summarizing the entire paper, a descriptive abstract functions more as a teaser, providing a quick overview that invites the reader to read the whole. Descriptive abstracts usually do not give or discuss results or set out the conclusion or its implications. A descriptive abstract of the boredom-proneness essay might simply include the first sentence from the informative abstract plus a final sentence of its own:

The relationship between boredom proneness and health-symptom reporting was examined. The findings and their application in clinical settings are discussed.

Writing Proposal Abstracts

Proposal abstracts contain the same basic information as informative abstracts, but their purpose is very different. You prepare proposal abstracts to persuade someone to let you write on a topic, pursue a project, conduct an experiment, or present a paper at a scholarly conference. This kind of abstract is not written to introduce a longer piece but rather to stand alone, and often the abstract is written before the paper itself. Titles and other aspects of the proposal deliberately reflect the theme of the proposed work, and you may use the future tense, rather than the past, to describe work not yet completed. Here is a possible proposal for doing research on boredom:

Undergraduate students will complete the Boredom Proneness Scale and the Hopkins Symptom Checklist. A multiple analysis of covariance will be performed to determine the relationship between boredom-proneness total scores and ratings on the five sub-scales of the Hopkins Symptom Checklist (Obsessive–Compulsive, Somatization, Anxiety, Interpersonal Sensitivity, and Depression).

Key Features of a Research Paper Abstract

• A summary of basic information . An informative abstract includes enough information to substitute for the report itself, a descriptive abstract offers only enough information to let the audience decide whether to read further, and a proposal abstract gives an overview of the planned work.
• Objective description . Abstracts present information on the contents of a report or a proposed study; they do not present arguments about or personal perspectives on those contents. The informative abstract on boredom proneness, for example, offers only a tentative conclusion: “The results suggest that boredom proneness may be an important element to consider.”
• Brevity . Although the length of abstracts may vary, journals and organizations often restrict them to 120–200 words—meaning you must carefully select and edit your words.

A Brief Guide to Writing Abstracts

Consider the rhetorical situation.

• Purpose : Are you giving a brief but thorough overview of a completed study? Only enough information to create interest? Or a proposal for a planned study or presentation?
• Audience : For whom are you writing this abstract? What information about your project will your readers need?
• Stance : Whatever your stance in the longer work, your abstract must be objective.
• Media/Design : How will you set your abstract off from the rest of the text? If you are publishing it online, will you devote a single page to it? What format does your audience require?

Generating Ideas and Text

Write the paper first, the abstract last. You can then use the finished work as the guide for the abstract, which should follow the same basic structure. Exception: You may need to write a proposal abstract months before the work it describes will be complete.

Copy and paste key statements. If you’ve already written the work, highlight your thesis, objective, or purpose; basic information on your methods; your results; and your conclusion. Copy and paste those sentences into a new document to create a rough version of your abstract.

Pare down the information to key ideas. Summarize the report, editing out any nonessential words and details. In your first sentence, introduce the overall scope of your study. Also include any other information that seems crucial to understanding your paper. Avoid phrases that add unnecessary words, such as “It is concluded that.” In general, you probably won’t want to use “I”; an abstract should cover ideas, not say what you think or will do.

Conform to any requirements. In general, an informative abstract should be at most 10 percent as long as the original and no longer than the maximum length allowed. Descriptive abstracts should be shorter still, and proposal abstracts should conform to the requirements of the organization calling for the proposal.

By now your writing is almost complete; you’ve come a long way, but you’re not finished yet! Now it’s time to revise the research paper.

Back to  How To Write A Research Paper .

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What is an Abstract?

An abstract is a short summary of an article, essay or research findings. A well-written abstract will provide the reader with a brief overview of the entire article, including the article's purpose, methodology and conclusion. An abstract should give the reader enough detail to determine if the information in the article meets their research needs...and it should make them want to read more!

While an abstract is usually anywhere between 150 - 300 words, it is important to always establish with your teacher the desired length of the abstract you are submitting.

This excellent guide from the University of Melbourne is a great snapshot of how to write an abstract.

Here are a few links to some useful abstract examples:

University of New South Wales

University of Wollongong

Michigan State University

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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.

Corresponding author

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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Received : 30 October 2023

Accepted : 21 February 2024

Published : 26 March 2024

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

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