how to write a methods section in a research paper

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• How to Write Your Methods

Ensure understanding, reproducibility and replicability

What should you include in your methods section, and how much detail is appropriate?

Why Methods Matter

The methods section was once the most likely part of a paper to be unfairly abbreviated, overly summarized, or even relegated to hard-to-find sections of a publisher’s website. While some journals may responsibly include more detailed elements of methods in supplementary sections, the movement for increased reproducibility and rigor in science has reinstated the importance of the methods section. Methods are now viewed as a key element in establishing the credibility of the research being reported, alongside the open availability of data and results.

A clear methods section impacts editorial evaluation and readers’ understanding, and is also the backbone of transparency and replicability.

For example, the Reproducibility Project: Cancer Biology project set out in 2013 to replicate experiments from 50 high profile cancer papers, but revised their target to 18 papers once they understood how much methodological detail was not contained in the original papers.

What to include in your methods section

What you include in your methods sections depends on what field you are in and what experiments you are performing. However, the general principle in place at the majority of journals is summarized well by the guidelines at PLOS ONE : “The Materials and Methods section should provide enough detail to allow suitably skilled investigators to fully replicate your study. ” The emphases here are deliberate: the methods should enable readers to understand your paper, and replicate your study. However, there is no need to go into the level of detail that a lay-person would require—the focus is on the reader who is also trained in your field, with the suitable skills and knowledge to attempt a replication.

A constant principle of rigorous science

A methods section that enables other researchers to understand and replicate your results is a constant principle of rigorous, transparent, and Open Science. Aim to be thorough, even if a particular journal doesn’t require the same level of detail . Reproducibility is all of our responsibility. You cannot create any problems by exceeding a minimum standard of information. If a journal still has word-limits—either for the overall article or specific sections—and requires some methodological details to be in a supplemental section, that is OK as long as the extra details are searchable and findable .

Imagine replicating your own work, years in the future

As part of PLOS’ presentation on Reproducibility and Open Publishing (part of UCSF’s Reproducibility Series ) we recommend planning the level of detail in your methods section by imagining you are writing for your future self, replicating your own work. When you consider that you might be at a different institution, with different account logins, applications, resources, and access levels—you can help yourself imagine the level of specificity that you yourself would require to redo the exact experiment. Consider:

• Which details would you need to be reminded of? 
• Which cell line, or antibody, or software, or reagent did you use, and does it have a Research Resource ID (RRID) that you can cite?
• Which version of a questionnaire did you use in your survey? 
• Exactly which visual stimulus did you show participants, and is it publicly available? 
• What participants did you decide to exclude? 
• What process did you adjust, during your work? 

Tip: Be sure to capture any changes to your protocols

You yourself would want to know about any adjustments, if you ever replicate the work, so you can surmise that anyone else would want to as well. Even if a necessary adjustment you made was not ideal, transparency is the key to ensuring this is not regarded as an issue in the future. It is far better to transparently convey any non-optimal methods, or methodological constraints, than to conceal them, which could result in reproducibility or ethical issues downstream.

Visual aids for methods help when reading the whole paper

Consider whether a visual representation of your methods could be appropriate or aid understanding your process. A visual reference readers can easily return to, like a flow-diagram, decision-tree, or checklist, can help readers to better understand the complete article, not just the methods section.

Ethical Considerations

In addition to describing what you did, it is just as important to assure readers that you also followed all relevant ethical guidelines when conducting your research. While ethical standards and reporting guidelines are often presented in a separate section of a paper, ensure that your methods and protocols actually follow these guidelines. Read more about ethics .

Existing standards, checklists, guidelines, partners

While the level of detail contained in a methods section should be guided by the universal principles of rigorous science outlined above, various disciplines, fields, and projects have worked hard to design and develop consistent standards, guidelines, and tools to help with reporting all types of experiment. Below, you’ll find some of the key initiatives. Ensure you read the submission guidelines for the specific journal you are submitting to, in order to discover any further journal- or field-specific policies to follow, or initiatives/tools to utilize.

Tip: Keep your paper moving forward by providing the proper paperwork up front

Be sure to check the journal guidelines and provide the necessary documents with your manuscript submission. Collecting the necessary documentation can greatly slow the first round of peer review, or cause delays when you submit your revision.

Randomized Controlled Trials – CONSORT The Consolidated Standards of Reporting Trials (CONSORT) project covers various initiatives intended to prevent the problems of  inadequate reporting of randomized controlled trials. The primary initiative is an evidence-based minimum set of recommendations for reporting randomized trials known as the CONSORT Statement . 

Systematic Reviews and Meta-Analyses – PRISMA The Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ) is an evidence-based minimum set of items focusing  on the reporting of  reviews evaluating randomized trials and other types of research.

Research using Animals – ARRIVE The Animal Research: Reporting of In Vivo Experiments ( ARRIVE ) guidelines encourage maximizing the information reported in research using animals thereby minimizing unnecessary studies. (Original study and proposal , and updated guidelines , in PLOS Biology .) 

Laboratory Protocols Protocols.io has developed a platform specifically for the sharing and updating of laboratory protocols , which are assigned their own DOI and can be linked from methods sections of papers to enhance reproducibility. Contextualize your protocol and improve discovery with an accompanying Lab Protocol article in PLOS ONE .

Consistent reporting of Materials, Design, and Analysis – the MDAR checklist A cross-publisher group of editors and experts have developed, tested, and rolled out a checklist to help establish and harmonize reporting standards in the Life Sciences . The checklist , which is available for use by authors to compile their methods, and editors/reviewers to check methods, establishes a minimum set of requirements in transparent reporting and is adaptable to any discipline within the Life Sciences, by covering a breadth of potentially relevant methodological items and considerations. If you are in the Life Sciences and writing up your methods section, try working through the MDAR checklist and see whether it helps you include all relevant details into your methods, and whether it reminded you of anything you might have missed otherwise.

Summary Writing tips

The main challenge you may find when writing your methods is keeping it readable AND covering all the details needed for reproducibility and replicability. While this is difficult, do not compromise on rigorous standards for credibility!

• Keep in mind future replicability, alongside understanding and readability.
• Follow checklists, and field- and journal-specific guidelines.
• Consider a commitment to rigorous and transparent science a personal responsibility, and not just adhering to journal guidelines.
• Establish whether there are persistent identifiers for any research resources you use that can be specifically cited in your methods section.
• Deposit your laboratory protocols in Protocols.io, establishing a permanent link to them. You can update your protocols later if you improve on them, as can future scientists who follow your protocols.
• Consider visual aids like flow-diagrams, lists, to help with reading other sections of the paper.
• Be specific about all decisions made during the experiments that someone reproducing your work would need to know.

Don’t

• Summarize or abbreviate methods without giving full details in a discoverable supplemental section.
• Presume you will always be able to remember how you performed the experiments, or have access to private or institutional notebooks and resources.
• Attempt to hide constraints or non-optimal decisions you had to make–transparency is the key to ensuring the credibility of your research.
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How to Write a Methods Section for a Psychology Paper

Tips and Examples of an APA Methods Section

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Verywell / Brianna Gilmartin 

The methods section of an APA format psychology paper provides the methods and procedures used in a research study or experiment . This part of an APA paper is critical because it allows other researchers to see exactly how you conducted your research.

Method refers to the procedure that was used in a research study. It included a precise description of how the experiments were performed and why particular procedures were selected. While the APA technically refers to this section as the 'method section,' it is also often known as a 'methods section.'

The methods section ensures the experiment's reproducibility and the assessment of alternative methods that might produce different results. It also allows researchers to replicate the experiment and judge the study's validity.

This article discusses how to write a methods section for a psychology paper, including important elements to include and tips that can help.

What to Include in a Method Section

So what exactly do you need to include when writing your method section? You should provide detailed information on the following:

• Research design
• Participants
• Participant behavior

The method section should provide enough information to allow other researchers to replicate your experiment or study.

Components of a Method Section

The method section should utilize subheadings to divide up different subsections. These subsections typically include participants, materials, design, and procedure.

Participants 

In this part of the method section, you should describe the participants in your experiment, including who they were (and any unique features that set them apart from the general population), how many there were, and how they were selected. If you utilized random selection to choose your participants, it should be noted here.

For example: "We randomly selected 100 children from elementary schools near the University of Arizona."

At the very minimum, this part of your method section must convey:

• Basic demographic characteristics of your participants (such as sex, age, ethnicity, or religion)
• The population from which your participants were drawn
• Any restrictions on your pool of participants
• How many participants were assigned to each condition and how they were assigned to each group (i.e., randomly assignment , another selection method, etc.)
• Why participants took part in your research (i.e., the study was advertised at a college or hospital, they received some type of incentive, etc.)

Information about participants helps other researchers understand how your study was performed, how generalizable the result might be, and allows other researchers to replicate the experiment with other populations to see if they might obtain the same results.

In this part of the method section, you should describe the materials, measures, equipment, or stimuli used in the experiment. This may include:

• Testing instruments
• Technical equipment
• Any psychological assessments that were used
• Any special equipment that was used

For example: "Two stories from Sullivan et al.'s (1994) second-order false belief attribution tasks were used to assess children's understanding of second-order beliefs."

For standard equipment such as computers, televisions, and videos, you can simply name the device and not provide further explanation.

Specialized equipment should be given greater detail, especially if it is complex or created for a niche purpose. In some instances, such as if you created a special material or apparatus for your study, you might need to include an illustration of the item in the appendix of your paper.

In this part of your method section, describe the type of design used in the experiment. Specify the variables as well as the levels of these variables. Identify:

• The independent variables
• Dependent variables
• Control variables
• Any extraneous variables that might influence your results.

Also, explain whether your experiment uses a  within-groups  or between-groups design.

For example: "The experiment used a 3x2 between-subjects design. The independent variables were age and understanding of second-order beliefs."

The next part of your method section should detail the procedures used in your experiment. Your procedures should explain:

• What the participants did
• How data was collected
• The order in which steps occurred

For example: "An examiner interviewed children individually at their school in one session that lasted 20 minutes on average. The examiner explained to each child that he or she would be told two short stories and that some questions would be asked after each story. All sessions were videotaped so the data could later be coded."

Keep this subsection concise yet detailed. Explain what you did and how you did it, but do not overwhelm your readers with too much information.

Tips for How to Write a Methods Section

In addition to following the basic structure of an APA method section, there are also certain things you should remember when writing this section of your paper. Consider the following tips when writing this section:

• Use the past tense : Always write the method section in the past tense.
• Be descriptive : Provide enough detail that another researcher could replicate your experiment, but focus on brevity. Avoid unnecessary detail that is not relevant to the outcome of the experiment.
• Use an academic tone : Use formal language and avoid slang or colloquial expressions. Word choice is also important. Refer to the people in your experiment or study as "participants" rather than "subjects."
• Use APA format : Keep a style guide on hand as you write your method section. The Publication Manual of the American Psychological Association is the official source for APA style.
• Make connections : Read through each section of your paper for agreement with other sections. If you mention procedures in the method section, these elements should be discussed in the results and discussion sections.
• Proofread : Check your paper for grammar, spelling, and punctuation errors.. typos, grammar problems, and spelling errors. Although a spell checker is a handy tool, there are some errors only you can catch.

After writing a draft of your method section, be sure to get a second opinion. You can often become too close to your work to see errors or lack of clarity. Take a rough draft of your method section to your university's writing lab for additional assistance.

A Word From Verywell

The method section is one of the most important components of your APA format paper. The goal of your paper should be to clearly detail what you did in your experiment. Provide enough detail that another researcher could replicate your study if they wanted.

Finally, if you are writing your paper for a class or for a specific publication, be sure to keep in mind any specific instructions provided by your instructor or by the journal editor. Your instructor may have certain requirements that you need to follow while writing your method section.

Frequently Asked Questions

While the subsections can vary, the three components that should be included are sections on the participants, the materials, and the procedures.

• Describe who the participants were in the study and how they were selected.
• Define and describe the materials that were used including any equipment, tests, or assessments
• Describe how the data was collected

To write your methods section in APA format, describe your participants, materials, study design, and procedures. Keep this section succinct, and always write in the past tense. The main heading of this section should be labeled "Method" and it should be centered, bolded, and capitalized. Each subheading within this section should be bolded, left-aligned and in title case.

The purpose of the methods section is to describe what you did in your experiment. It should be brief, but include enough detail that someone could replicate your experiment based on this information. Your methods section should detail what you did to answer your research question. Describe how the study was conducted, the study design that was used and why it was chosen, and how you collected the data and analyzed the results.

Erdemir F. How to write a materials and methods section of a scientific article ? Turk J Urol . 2013;39(Suppl 1):10-5. doi:10.5152/tud.2013.047

Kallet RH. How to write the methods section of a research paper . Respir Care . 2004;49(10):1229-32. PMID: 15447808.

American Psychological Association.  Publication Manual of the American Psychological Association  (7th ed.). Washington DC: The American Psychological Association; 2019.

American Psychological Association. APA Style Journal Article Reporting Standards . Published 2020.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation , or research paper , the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research and your dissertation topic .

It should include:

• The type of research you conducted
• How you collected and analyzed your data
• Any tools or materials you used in the research
• How you mitigated or avoided research biases
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, other interesting articles, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ? How did you prevent bias from affecting your data?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalizable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalized your concepts and measured your variables. Discuss your sampling method or inclusion and exclusion criteria , as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on July 4–8, 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

• Information bias
• Omitted variable bias
• Regression to the mean
• Survivorship bias
• Undercoverage bias
• Sampling bias

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyze?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness store’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

• The Hawthorne effect
• Observer bias
• The placebo effect
• Response bias and Nonresponse bias
• The Pygmalion effect
• Recall bias
• Social desirability bias
• Self-selection bias

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods.

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Next, you should indicate how you processed and analyzed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

• How you prepared the data before analyzing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorizing and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviors, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalized beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalizable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

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

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles

Methodology

• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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How to Write a Methods Section for a Research Paper

A common piece of advice for authors preparing their first journal article for publication is to start with the methods section: just list everything that was done and go from there. While that might seem like a very practical approach to a first draft, if you do this without a clear outline and a story in mind, you can easily end up with journal manuscript sections that are not logically related to each other. 

Since the methods section constitutes the core of your paper, no matter when you write it, you need to use it to guide the reader carefully through your story from beginning to end without leaving questions unanswered. Missing or confusing details in this section will likely lead to early rejection of your manuscript or unnecessary back-and-forth with the reviewers until eventual publication. Here, you will find some useful tips on how to make your methods section the logical foundation of your research paper.

Not just a list of experiments and methods

While your introduction section provides the reader with the necessary background to understand your rationale and research question (and, depending on journal format and your personal preference, might already summarize the results), the methods section explains what exactly you did and how you did it. The point of this section is not to list all the boring details just for the sake of completeness. The purpose of the methods sections is to enable the reader to replicate exactly what you did, verify or corroborate your results, or maybe find that there are factors you did not consider or that are more relevant than expected. 

To make this section as easy to read as possible, you must clearly connect it to the information you provide in the introduction section before and the results section after, it needs to have a clear structure (chronologically or according to topics), and you need to present your results according to the same structure or topics later in the manuscript. There are also official guidelines and journal instructions to follow and ethical issues to avoid to ensure that your manuscript can quickly reach the publication stage.

Table of Contents:

• General Methods Structure: What is Your Story? 
• What Methods Should You Report (and Leave Out)? 
• Details Frequently Missing from the Methods Section

More Journal Guidelines to Consider 

• Accurate and Appropriate Language in the Methods

General Methods Section Structure: What Is Your Story? 

You might have conducted a number of experiments, maybe also a pilot before the main study to determine some specific factors or a follow-up experiment to clarify unclear details later in the process. Throwing all of these into your methods section, however, might not help the reader understand how everything is connected and how useful and appropriate your methodological approach is to investigate your specific research question. You therefore need to first come up with a clear outline and decide what to report and how to present that to the reader.

The first (and very important) decision to make is whether you present your experiments chronologically (e.g., Experiment 1, Experiment 2, Experiment 3… ), and guide the reader through every step of the process, or if you organize everything according to subtopics (e.g., Behavioral measures, Structural imaging markers, Functional imaging markers… ). In both cases, you need to use clear subheaders for the different subsections of your methods, and, very importantly, follow the same structure or focus on the same topics/measures in the results section so that the reader can easily follow along (see the two examples below).

If you are in doubt which way of organizing your experiments is better for your study, just ask yourself the following questions:

• Does the reader need to know the timeline of your study? 
• Is it relevant that one experiment was conducted first, because the outcome of this experiment determined the stimuli or factors that went into the next?
• Did the results of your first experiment leave important questions open that you addressed in an additional experiment (that was maybe not planned initially)?
• Is the answer to all of these questions “no”? Then organizing your methods section according to topics of interest might be the more logical choice.

If you think your timeline, protocol, or setup might be confusing or difficult for the reader to grasp, consider adding a graphic, flow diagram, decision tree, or table as a visual aid.

What Methods Should You Report (and Leave Out)?

The answer to this question is quite simple–you need to report everything that another researcher needs to know to be able to replicate your study. Just imagine yourself reading your methods section in the future and trying to set up the same experiments again without prior knowledge. You would probably need to ask questions such as:

• Where did you conduct your experiments (e.g., in what kind of room, under what lighting or temperature conditions, if those are relevant)? 
• What devices did you use? Are there specific settings to report?
• What specific software (and version of that software) did you use?
• How did you find and select your participants?
• How did you assign participants into groups?  
• Did you exclude participants from the analysis? Why and how?
• Where did your reagents or antibodies come from? Can you provide a Research Resource Identifier (RRID) ?
• Did you make your stimuli yourself or did you get them from somewhere?
• Are the stimuli you used available for other researchers?
• What kind of questionnaires did you use? Have they been validated?
• How did you analyze your data? What level of significance did you use?
• Were there any technical issues and did you have to adjust protocols?

Note that for every experimental detail you provide, you need to tell the reader (briefly) why you used this type of stimulus/this group of participants/these specific amounts of reagents. If there is earlier published research reporting the same methods, cite those studies. If you did pilot experiments to determine those details, describe the procedures and the outcomes of these experiments. If you made assumptions about the suitability of something based on the literature and common practice at your institution, then explain that to the reader.

In a nutshell, established methods need to be cited, and new methods need to be clearly described and briefly justified. However, if the fact that you use a new approach or a method that is not traditionally used for the data or phenomenon you study is one of the main points of your study (and maybe already reflected in the title of your article), then you need to explain your rationale for doing so in the introduction already and discuss it in more detail in the discussion section .

Note that you also need to explain your statistical analyses at the end of your methods section. You present the results of these analyses later, in the results section of your paper, but you need to show the reader in the methods section already that your approach is either well-established or valid, even if it is new or unusual. 

When it comes to the question of what details you should leave out, the answer is equally simple ‒ everything that you would not need to replicate your study in the future. If the educational background of your participants is listed in your institutional database but is not relevant to your study outcome, then don’t include that. Other things you should not include in the methods section:

• Background information that you already presented in the introduction section.
• In-depth comparisons of different methods ‒ these belong in the discussion section.
• Results, unless you summarize outcomes of pilot experiments that helped you determine factors for your main experiment.

Also, make sure your subheadings are as clear as possible, suit the structure you chose for your methods section, and are in line with the target journal guidelines. If you studied a disease intervention in human participants, then your methods section could look similar to this:

Since the main point of interest here are your patient-centered outcome variables, you would center your results section on these as well and choose your headers accordingly (e.g., Patient characteristics, Baseline evaluation, Outcome variable 1, Outcome variable 2, Drop-out rate ). 

If, instead, you did a series of visual experiments investigating the perception of faces including a pilot experiment to create the stimuli for your actual study, you would need to structure your methods section in a very different way, maybe like this:

Since here the analysis and outcome of the pilot experiment are already described in the methods section (as the basis for the main experimental setup and procedure), you do not have to mention it again in the results section. Instead, you could choose the two main experiments to structure your results section ( Discrimination and classification, Familiarization and adaptation ), or divide the results into all your test measures and/or potential interactions you described in the methods section (e.g., Discrimination performance, Classification performance, Adaptation aftereffects, Correlation analysis ).

Details Commonly Missing from the Methods Section

Manufacturer information.

For laboratory or technical equipment, you need to provide the model, name of the manufacturer, and company’s location. The usual format for these details is the product name (company name, city, state) for US-based manufacturers and the product name (company name, city/town, country) for companies outside the US.

Sample size and power estimation

Power and sample size estimations are measures for how many patients or participants are needed in a study in order to detect statistical significance and draw meaningful conclusions from the results. Outside of the medical field, studies are sometimes still conducted with a “the more the better” approach in mind, but since many journals now ask for those details, it is better to not skip this important step.

Ethical guidelines and approval

In addition to describing what you did, you also need to assure the editor and reviewers that your methods and protocols followed all relevant ethical standards and guidelines. This includes applying for approval at your local or national ethics committee, providing the name or location of that committee as well as the approval reference number you received, and, if you studied human participants, a statement that participants were informed about all relevant experimental details in advance and signed consent forms before the start of the study. For animal studies, you usually need to provide a statement that all procedures included in your research were in line with the Declaration of Helsinki. Make sure you check the target journal guidelines carefully, as these statements sometimes need to be placed at the end of the main article text rather than in the method section.

Structure & word limitations

While many journals simply follow the usual style guidelines (e.g., APA for the social sciences and psychology, AMA for medical research) and let you choose the headers of your method section according to your preferred structure and focus, some have precise guidelines and strict limitations, for example, on manuscript length and the maximum number of subsections or header levels. Make sure you read the instructions of your target journal carefully and restructure your method section if necessary before submission. If the journal does not give you enough space to include all the details that you deem necessary, then you can usually submit additional details as “supplemental” files and refer to those in the main text where necessary.

Standardized checklists

In addition to ethical guidelines and approval, journals also often ask you to submit one of the official standardized checklists for different study types to ensure all essential details are included in your manuscript. For example, there are checklists for randomized clinical trials, CONSORT (Consolidated Standards of Reporting Trials) , cohort, case-control, cross‐sectional studies, STROBE (STrengthening the Reporting of OBservational studies in Epidemiology ), diagnostic accuracy, STARD (STAndards for the Reporting of Diagnostic accuracy studies) , systematic reviews and meta‐analyses PRISMA (Preferred Reporting Items for Systematic reviews and Meta‐Analyses) , and Case reports, CARE (CAse REport) .

Make sure you check if the manuscript uses a single- or double-blind review procedure , and delete all information that might allow a reviewer to guess where the authors are located from the manuscript text if necessary. This means that your method section cannot list the name and location of your institution, the names of researchers who conducted specific tests, or the name of your institutional ethics committee.  

Accurate and Appropriate Language in the Methods Section

Like all sections of your research paper, your method section needs to be written in an academic tone . That means it should be formal, vague expressions and colloquial language need to be avoided, and you need to correctly cite all your sources. If you describe human participants in your method section then you should be especially careful about your choice of words. For example, “participants” sounds more respectful than “subjects,” and patient-first language, that is, “patients with cancer,” is considered more appropriate than “cancer patients” by many journals.

Passive voice is often considered the standard for research papers, but it is completely fine to mix passive and active voice, even in the method section, to make your text as clear and concise as possible. Use the simple past tense to describe what you did, and the present tense when you refer to diagrams or tables. Have a look at this article if you need more general input on which verb tenses to use in a research paper . 

Lastly, make sure you label all the standard tests and questionnaires you use correctly (look up the original publication when in doubt) and spell genes and proteins according to the common databases for the species you studied, such as the HUGO Gene Nomenclature Committee database for human studies .  

Visit Wordvice AI’s AI Text Editor to receive a free grammar check and English editing services (including manuscript editing , paper editing , and dissertation editing ) before submitting your manuscript to journal editors.

• USC Libraries
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Organizing Your Social Sciences Research Paper

• 6. The Methodology
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
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• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
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• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
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• Further Readings
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• Bibliography

The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

• Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
• In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
• The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
• The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
• In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I.  Groups of Research Methods

There are two main groups of research methods in the social sciences:

• The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
• The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II.  Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

• Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
• Tools and methods used to identify and collect information, and how you identified relevant variables,
• The ways in which you processed the data and the procedures you used to analyze that data, and
• The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

• Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
• Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
• Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
• Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
• Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
• Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
• Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
• Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

NOTE :   Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.

ANOTHER NOTE : If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE :   If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III.  Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.

Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

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How To Write A Research Paper

Research Paper Methods Section

How To Write The Methods Section of a Research Paper Step-by-Step

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Published on: Mar 6, 2024

Last updated on: Mar 5, 2024

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The method and material section stands as the cornerstone of any research paper. Crafting this section with precision is important, especially when aiming for a target journal. 

If you're navigating the intricacies of research paper writing and pondering on how to ace the methodology, fear not – we've got you covered. Our guide will walk you through the essentials, ensuring your methodology shines in the eyes of your target journal. 

Let's jump into the basics of the method section!

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What is the Methods Section of a Research Paper?

The methods section of a research paper provides a detailed description of the procedures, techniques, and methods employed to conduct the study ( American Psychological Association, 2020 ). It outlines the steps taken to collect, analyze, and interpret data, allowing other researchers to replicate the study and assess the validity of the results. 

This section includes information on the study design, participants, materials or apparatus used, data collection procedures, and statistical analyses. Typically, the methodology section is placed after the introduction and before the results section in a research paper.

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Importance of Methods Section

The methods section of a research paper holds significant importance. Here is why: 

• Replicability: The methods section ensures the replicability of the study by providing a clear and comprehensive account of the procedures used.
• Transparency: It enhances transparency, allowing other researchers to understand and evaluate the validity of the study's findings.
• Credibility: A well-documented methods section enhances the credibility of the research, instilling confidence in the study's design and execution.
• Guidance for Future Research: It serves as a guide for future research, offering insights into methodologies that can be applied or modified in similar studies.
• Ethical Considerations: The section highlights ethical considerations, promoting responsible and accountable research practices.

Structure of Methods Section of a Research Paper

There are some important parts of the method section of a research paper that you will need to include, whether you have done an experimental study or a descriptive study. 

Provided structured approach below ensures clarity and replicability of the research methodology:

Formatting of the Methods Section 

Make the main " Methods " heading centered, bold, and capitalized. For subtopics under "Methods," like participant details or data collection, use left-aligned, bold, and title cases. 

Feel free to include even sub-headings for more specifics. This formatting helps readers easily follow your study steps.

Next, we will address the most common query, i.e., how to write the methodology section of a research paper. Let’s explain the steps for writing the methodology section of a research paper:

Step 1: Start with Study Design

The initial step in the method section of a research paper is to provide a clear description of the study type. This involves outlining the overall plan and structure of the research. 

Different types of studies, such as cohort, case-control, and cross-sectional, may be employed based on the research objectives.

For instance:

Starting with the study design sets the stage for understanding the methodology. It provides readers with a foundation for subsequent sections in the methods portion of the research paper.

Step 2: Describe Participants

In the methods section, the second step involves providing a detailed account of the participants involved in the study. Start by describing the characteristics of both human and non-human subjects, using clear and descriptive language.

Address specific demographic characteristics relevant to your study, such as age, sex, ethnic or racial group, gender identity, education level, and socioeconomic status. Clearly outlining these essential details ensures transparency, replicability, and a comprehensive understanding of the study's sample.

Sampling Procedures:

• Clearly outline how participants were selected, specifying any inclusion and exclusion criteria applied.
• Appropriately identify the sampling procedure used, such as random sampling, convenience sampling, or stratified sampling.
• If applicable, note the percentage of invited participants who actually participated.
• Specify if participants were self-selected or chosen by their institutions (e.g., schools submitting student data).

Sample Size and Power:

• Detail the intended sample size estimation per condition and the statistical power aimed for in the study.
• Provide information on any analyses conducted to determine the sample size and power.
• Emphasize the importance of statistical power for detecting effects if present.
• State whether the final sample size differed from the originally intended sample.
• Base your interpretations of study outcomes solely on the final sample, reinforcing the importance of transparency in reporting.

Step 3: State Materials or Apparatus

In the third step, thoroughly describe the materials or apparatus used in your research. In addition, gives detailed information on the tools and techniques employed to measure relevant outcome variables.

Primary and Secondary Measures:

• Clearly define both primary and secondary outcome measures aligned with research questions.
• Specify all instruments used, citing hardware models, software versions, or references to manuals/articles.
• Report settings of specialized apparatus, such as screen resolution.

Reliability and Validity:

• For each instrument, detail measures of reliability and validity.
• Include an explanation of how consistently (reliability) and precisely (validity) the method measures the targeted variables.
• Provide examples or reference materials to illustrate the reliability and validity of tests, questionnaires, or interviews.

Covariates and Quality Assurance:

• Describe any covariates considered and their relevance to explaining or predicting outcomes.
• Review methods used to assure measurement quality, such as researcher training, multiple assessors, translation procedures, and pilot studies.
• For subjectively coded data, report interrater reliability scores to gauge consistency among raters.

Step 4 Write the Procedure

Next is the procedure section of the research paper, which thoroughly details the procedures applied for administering the study, processing data, and planning data analyses.

Data Collection Methods and Research Design

• Summarize data collection methods (e.g., surveys, tests) and the overall research design.
• Provide detailed procedures for administering surveys, tests, or any other data collection instruments.
• Clarify the research design framework, specifying whether it's experimental, quasi-experimental, descriptive, correlational, and/or longitudinal.
• For multi-group studies, report assignment methods, group instructions, interventions, and session details.

Data Analysis 

• Clearly state the planned data analysis methods for each research question or hypothesis.
• Specify descriptive statistics, inferential statistical tests, and any other analysis techniques.
• Include software or tools used for data analysis (e.g., SPSS, R).
• Provide a brief rationale for choosing each analysis method.

Step 5: Mention Ethical Approvals

In the fifth step of the methods section, explicitly address the ethical considerations of your research, ensuring transparency and adherence to ethical standards. Here are some key ethical considerations: 

• IRB Approval:

Clearly state that the research received approval from the Institutional Review Board (IRB) or an equivalent ethical review body.

• Informed Consent:

Specify the process of obtaining informed consent, including the provision of information sheets to participants.

• Confidentiality:

Describe measures taken to maintain confidentiality, such as assigning unique identification numbers and securing data.

• Participant Rights:

Emphasize participants' right to withdraw from the study at any point without consequences.

• Debriefing:

Mention if debriefing procedures were implemented to address any participant concerns post-study.

Methods Section of Research Paper Examples

Exploring sample methodology sections is crucial when composing your first research paper, as it enhances your understanding of the structure. We provide PDF examples of methodology sections that you can review to gain inspiration for your own research paper.

Methods Section of A Qualitative Research Paper

Methods Section of Research Paper Template

Methods Section of Research Proposal Example

Methods Section of Research Paper APA

How To Write A Method For An Experiment

Journal Guidelines to Consider

When writing the methods section, be mindful of the specific guidelines set by your target journal. These guidelines can vary, impacting the structure, word limitations, and even the presentation of your methodology. 

Here's a detailed explanation, along with an example:

Structure & Word Limitations

If a journal follows APA guidelines, it might allow flexibility in structuring the method section. However, some journals may impose strict limitations on the manuscript's length and the number of subsections. 

For instance, a journal might specify a maximum of 3000 words for the entire paper and limit the method section to 500 words. In such cases, ensure you adhere to these constraints, potentially submitting supplemental files for additional details.

Standardized Checklists

Journals often request authors to use standardized checklists for various study types to ensure completeness. 

For a randomized clinical trial, the CONSORT(Consolidated Standards of Reporting Trials) checklist might be required. If your research involves observational studies, the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist may be applicable. 

For diagnostic accuracy studies, adherence to the STARD (Standards for the Reporting of Diagnostic Accuracy Studies) checklist is common. These checklists serve as a systematic way to include essential details in your manuscript, aligning with the journal's preferred reporting standards.

Blind Review Procedures

Some journals implement single- or double-blind review procedures. If a double-blind review is in place, authors need to remove any information that might reveal their identity or institutional affiliations. 

For instance, the method section cannot explicitly mention the institution's name, researchers' identities, or the institutional ethics committee. This ensures an unbiased evaluation of the research without reviewers being influenced by the authors' affiliations.

The Dos And Don’ts Of Writing The Methods Section

While it's important to be thorough, certain elements are better suited for other sections of the paper. Here are some Do’s and Don’ts of writing the methods section:

Dos of Writing the Methods Section

Here are what to include in the methods section: 

• Clarity and Precision: Clearly and concisely describe the procedures used in your study. Ensure that another researcher can replicate your work based on your explanation.
• Chronological Order: Present the methods in a logical and chronological sequence. This helps readers follow the flow of your research.
• Detail and Specificity: Provide sufficient detail to allow for replication. Specify equipment, materials, and procedures used, including any modifications.
• Consistency with Study Design: Align your methods with the overall design of your study. Clearly state whether it's experimental, observational, or another design.
• Inclusion of Participants: Detail participant characteristics, including demographics and any inclusion/exclusion criteria. Clearly state the sample size.
• Operational Definitions: Define and operationalize key variables. Clearly explain how each variable was measured or manipulated.
• Transparency in Data Collection: Describe the data collection process, including the timing, location, and any relevant protocols followed during the study.
• Statistical Information: Outline the statistical methods used for analysis. Specify the software, tests employed and significance levels.
• Ethical Considerations: Discuss ethical approvals obtained, informed consent procedures, and measures taken to ensure participant confidentiality. Address any potential conflicts of interest.

Don'ts of Writing the Methods Section

• Extraneous Details: Unlike the discussion section avoid including unnecessary details or information that does not contribute directly to understanding the research methods.
• Results Discussion: Refrain from discussing or interpreting the results in the methods section. Focus solely on describing the methods employed.
• Ambiguity and Vagueness: Steer clear of vague or ambiguous language. Be precise and specific in your descriptions.
• Overemphasis on Background: While some background information is relevant, avoid turning the methods section into an extensive literature review . Keep the focus on the research methods.
• Personal Opinions: Do not include personal opinions or anecdotes. Stick to factual and objective descriptions.
• Excessive Jargon: Minimize the use of technical jargon that may be confusing to readers who are not experts in your field. If necessary, provide clear explanations.
• Inadequate Explanation of Modifications: If you deviate from standard procedures, clearly explain the modifications and justify why they were made.
• Inconsistency with Design: Ensure that your methods align with the study design. Avoid inconsistencies that could create confusion for readers.

In conclusion , learning the art of writing the methods section is pivotal for any research paper. Following a step-by-step approach, from defining the study design to detailed data collection and analysis, ensures clarity and replicability. 

Remember, precision matters. If you find yourself grappling with the intricacies of your methodology, don't hesitate to reach out to CollegeEssay.org.  

Our professional writing service is ready to assist you in crafting a robust and well-structured methods section. 

Connect with our research paper writing service for expert guidance and conquer the challenges of research paper writing.

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How to write a materials and methods section of a scientific article?

In contrast to past centuries, scientific researchers have been currently conducted systematically in all countries as part of an education strategy. As a consequence, scientists have published thousands of reports. Writing an effective article is generally a significant problem for researchers. All parts of an article, specifically the abstract, material and methods, results, discussion and references sections should contain certain features that should always be considered before sending a manuscript to a journal for publication. It is generally known that the material and methods section is a relatively easy section of an article to write. Therefore, it is often a good idea to begin by writing the materials and methods section, which is also a crucial part of an article. Because “reproducible results” are very important in science, a detailed account of the study should be given in this section. If the authors provide sufficient detail, other scientists can repeat their experiments to verify their findings. It is generally recommended that the materials and methods should be written in the past tense, either in active or passive voice. In this section, ethical approval, study dates, number of subjects, groups, evaluation criteria, exclusion criteria and statistical methods should be described sequentially. It should be noted that a well-written materials and methods section markedly enhances the chances of an article being published.

How to Write a Materials and Methods Section of a Scientific Article?

Up to the 18 th Century scientific researches were performed on a voluntary basis by certain scientists. However from the second half of the 19 th century, scientific development has gained momentum with the contributions of numerous scientists including Edison, Fleming, and Koch. In parallel with these developments, apparently each scientific field, and even their branches made, and still making magnificent progressions from the end of the 18 th century. Secondary to these developments, scientific researches have been implemented systematically by universities, and various institutions in every part of the world as an integral component of national strategies. Naturally, the number of researchers who performed scientific investigations or sponsored by various institutions increased considerably. Also, as is known very well, all over the world scientists, and researchers move from one place to another to disseminate scientific knowledge. All of these scientific efforts, and activities reflect on clinical practice, and hundreds of thousands, and millions scientific articles which we can currently gain access into all of them online. As indicated by the investigator Gerard Piel, “Without publication, science is dead” which explains the importance of publication. In other words, if you don’t share your investigation and knowledge, they don’t mean anything by themselves. Although sharing the knowledge is essential for writing a scientific paper, nowadays writing a scientific article is mostly learnt as a master-apprentice relationship, and therefore certain standards have not been established. This phenomenon creates serious stress especially for young investigators in their early stage of writing scientific papers. Indeed investigators receiving their residency training confront this reality finally during writing of their dissertations. Though sharing knowledge is known as a fundamental principle in writing a scientific paper, it creates difficulties in the whole world. Relevant to this issue, in the whole world investigations have been performed, and books have been written on the subject of how to write a scientific paper. Accordingly, in our country mostly local meetings, and courses have been organized. These organizations, and investigations should be performed. Indeed, nowadays, in the first assessments, the rejection rate of the journals by internationally acknowledged scientific indexes as “Science Citation İndex (SCI)” and “Science Citation İndex Expanded (SCI-extended” which have certain scientific standards, increases to 62 percent. As a matter of fact only 25% of Class A journals have been included in the lists of SCI, and SCI-extended.

As we all know very well, scientific articles consist of sections of summary, introduction, material, and methods, discussion, and references. Among them, conventionally Materials and Methods section has been reported as the most easily written or will be written section. Although it is known as the most easily written section, nearly 30% of the reasons for rejection are related to this section per se. Therefore due care, and attention should be given to the writing of this section. In the writing process of the ‘Material and Methods’ section, all achievements performed throughout the study period should be dealt with in consideration of certain criteria in a specific sequence. Since as a globally anticipated viewpoint, ‘Materials and Methods’ section can be written quite easily, it has been indicated that if difficulties are encountered in writing a manuscript, then one should start writing from this section. In writing this section, study design describing the type of the article, study subjects to be investigated, methods, and procedures of measurements should be provided under four main headings. [ 1 , 2 ] Accordingly, in brief, we can emphasize the importance of providing clear-cut, adequate, and detailed information in the ‘Materials and Methods’ section to the scientists who will read this scientific article. Meeting these criteria carries great importance with respect to the evaluation of reliability of the investigation by the readers, and reviewers, and also informing them about procedural method, design, data collection, and assessment methods of the investigation, Priorly, as is the case in all scientific investigations, one should be reminded about the importance, and indispensability of compliance with certain standard writing rules. Accordingly, rules of grammar should be obeyed, and if possible passive voice of simple past tense should be used. Related to these rules, use of verbs ‘investigated’, ‘evaluated’ or ‘performed’ will be appropriate. Recently, expressions showing the ownership of the investigation as ‘we performed’, ‘we evaluated’, ‘we implemented’ have taken priority. Since the important point is communication of the message contained in the scientific study, the message should be clearly comprehensible. While ensuring clarity of the message, use of flourishing, and irrelevant sentences should be avoided. [ 1 , 3 ] According to another approach, since our article will be read by professionals of other disciplines, it is important to comply with certain rules of writing. To that end, standard units of measurements, and international abbreviations should be used. Abbreviations should be explained within parentheses at their first mention in the manuscript. For instance let’s analyze the following sentence” The patients were evaluated with detailed medical history, physical examination, complete urinalysis, PSA, and urinary system ultrasound” The abbreviation PSA is very well known by the urologist. However we shouldn’t forget that this article will be read by the professionals in other medical disciplines. Similarly this sentence should not be written as: “The patients were evaluated with detailed medical history, physical examination, complete urinalysis PSA (prostate-specific antigen), and urinary system ultrasound.” Indeed the abbreviation should follow the explanation of this abbreviation. Then the appropriate expression of the sentence should be. “The patients were evaluated with detailed medical history, physical examination, complete urinalysis, prostate-specific antigen (PSA), and urinary system ultrasound.”

In addition to the abovementioned information, in the beginning paragraphs of ‘Materials and Methods’ section of a clinical study the answers to the following questions should be absolutely provided:

• The beginning, and termination dates of the study period.
• Number of subjects/patients/experimental animals etc. enrolled in the study,
• Has the approval of the ethics committee been obtained?
• Study design (prospective, retrospective or other). [ 1 , 2 , 4 – 7 ]

Still additional features of the study design (cross-sectional) should be indicated. Apart from this, other types of study designs (randomized, double-blind, placebo-controlled or double-blind, parallel control etc.) should be revealed.

The heading of the section “Materials and Methods” can be changed to “Patients and the Method” in accordance with writing rules of the journal in question. Indication of starting, and termination dates of a clinical study will facilitate scientific interpretation of the article. Accordingly, outcomes obtained during development phase of a newly implemented method might be considered differently from those acquired during conventional use of this method. Besides, incidence of the diseases, and number of affected people might vary under the impact of social fluctuations, and environmental factors. Therefore with this justification study period should be specified. Number of cases included in the study should be absolutely indicated in the ‘Materials and Methods’ section. It will be appropriate to determine study population after consultation to a statistician-and if required-following “power analysis” Accordingly, the need for a control group will be indicated based on the study design. Nowadays, as a requirement of patient rights, obtainment of approval from ethics committee should be indicated with its registration number. In addition, acquirement of informed consent forms from patients should be indicated. Ethics Committee approval should be obtained in prospective studies performed with study drugs. Otherwise in case of occurrence of adverse effects, it should be acknowledged that in compliance with Article #90 of the Turkish Criminal Law, a 3-year prison sentence is given to the guilty parties. [ 8 ] Since issues related to the Ethics Committee are the subject of another manuscript, they won’t be handled herein.

The following paragraph exemplifies clearly the aforementioned arguments: “After approval of the local ethics committee (BADK-22), informed consent forms from the patients were obtained, and a total of 176 cases with lower urinary tract symptoms (LUTS) were retrospectively evaluated between January 2011, and December 2012.” In a prospectively designed study, methods used to communicate with the cases including face-to-face interviews, phone calls and/or e-mail should be indicated. [ 1 , 2 ] Each paragraph or subheading in the ‘Materials and Methods’ section should be in accordance with the related ones in the ‘Results’ section. In other words, the sequence of paragraphs, and subheadings in the ‘Results’ section should be the same in the ‘Materials and Methods’ section.

As a next step, names of the groups, and distribution of the cases in these groups should be indicated. For instance: the statement “Cases were divided into 3 groups based on their LUTS scores as. Groups 1 (0–9; n=91), 2 (10–18; n=66), and 3 (≥19; n=20)” clearly delineates the scope of the study at baseline.. In the ‘Materials and Methods’ section the number of study subjects should be absolutely documented. Herein, after assignment of names to groups, in the rest of the manuscript, these names should be used. For example instead of saying: “Mean ages of the cases with LUTS scores between 0–9, 10–18, and ≥19 were determined to be 63.2±2.1, 62.8±4.5, and 65.7±3.9 years, respectively” it will be more comprehensible to use the expression: “Mean ages of the Groups 1, 2, and 3 were specified as 63.2±2.1, 62.8±4.5, and 65.7±3.9 years.” (p=0.478). Expressions indicated in the ‘Materials and Methods’ section should not be repeated in the “Results” section. Thus, errors of repetition will be precluded. Following the abovementioned information, the evaluation method of the cases enrolled in the study should be indicated. Hence, results of medical history, physical examination, and if performed laboratory or radiological evaluations-in that order-should be indicated. The application of survey study-if any-should be investigated, and documented. Therefore, the following sentences encompass all the information stated above: “The cases were evaluated with detailed medical history, physical examination, measurements of serum follicle stimulating hormone (FSH), luteinizing hormone (LH), testosterone (T) levels, complete urinalysis, urinary flow rate, direct urinary system roentgenograms, urinary system ultrasound, and if required cyctoscopy. Lower urinary system complaints, and erectile dysfunction were evaluated using International Prostate Symptom Score (IPSS), and International Erectile Function Scale (IIEF), respectively.” Apparently, questionnaire forms were used in the above-cited study. However, methods used for the evaluation of questionnaire forms, and significance of the results obtained, and if possible, the first performer of this survey should be written with accompanying references. In relation to the abovementioned questionnaires the following statements constitute standard expressions for the ‘Materials and Methods’ section: “International Prostate Symptom Score (IPPS) was used in the determination of the severity of prostatic symptoms. IPSS used to determine the severity of the disease, evaluate treatment response, and ascertain the symptomatic progression, is the most optimal scoring system recommended by European Association of Urology (EAU) which classifies the severity of the disease based on IPSS scores as mild (0–7), moderate (8–19), and severe symptomatic (20–35) disease. In the evaluation of sexual function International Erectile Function Scale (IIEF) was used. IIEF is one of the most prevalently used form for the patients who consulted for the complaints of sexual dysfunction Based on IIEF scores, the severity of the disease was classified as severe (1–10), moderate (11–16), mild to moderate (17–21), mild (22–25), and no ED (26–30).”

Whether the institutions of the authors working for should be written in the ‘Materials and Methods’ section can be a subject of debate, generally viewpoints favour provision of this information. However, in compliance with their writing rules, some journals do not favour open-label studies where name of the study site is indicated, and this principle is communicated to the author during editorial evaluation Besides, in the ‘Materials and Methods’ section, the brand of the study object, and its country of origin should be indicated. (ie. if radiological methods are used, then the brand of radiological equipment, and its manufacturing country should be specified. In a study entitled ‘The Impact of Computed Tomography in the Prediction of Post-Radical Nephrectomy Stage in Renal Tumours’ since the main topic of the study is computed tomography, the specifications of the equipment used should be explicitely indicated. On the other hand, the details of the medical method which can effect the outcomes of the study should be also recorded. Accordingly, the methods applied for percutaneous nephrolithotomy, ureterorenoscopy, varicocelectomy, transurethral prostatectomy, radical prostatectomy (perineal, open, laparoscopic or robotic should be absolutely indicated. Then inclusion, and exclusion criteria, and if used control group, and its characteristics should be documented. Thus the following paragraph about exclusion criteria will be appropriate: Patients with a history of neurogenic bladder, prostatic or abdominal operation, and transrectal ultrasound guided prostate biopsy (within the previous 6 months), those aged <40 or >70 years, individuals with a peak urine flow rate below 10 ml/sec, and residual urine more than 150 cc were not included in the study.” [ 1 – 3 , 9 ]

Some diseases mentioned in the “Materials and Methods” section require special monitorization procedures. In these cases the procedure of monitorization should be documented for the sake of the validity of the study in question. Accordingly, in conditions such as “nephrectomy, prostatectomy, orchidectomy, pyeloplasty, varicocelectomy, drug therapies, penile prosthesis, and urethral stricture” clinical follow-up protocols should be provided.

The abovementioned rules, and recommendations are most frequently valid for a clinical study, and some points indicated in experimental studies should be also considered. Types, weights, gender, and number of the animals used in animal studies should be absolutely specified. Besides condition of evaluation of experimental animals should be noted. Then as is the case with clinical studies, approval of the ethics committee should be obtained, and documented. Accordingly, the beginning paragraphs of the ‘Materials and Methods’ can be expressed as follows:

“In the study, 40 Wistar-Albino 6-month-old rats each weighing 350–400 g were used. After approval of the ethics committee (HADYEK-41) the study was performed within the frame of rules specified by the National Institute for animal experiments. The rats were divided into 3 groups. Hence, Group 1 (n=7) was accepted as the control group. The rats subjected to partial ureteral obstruction with or without oral carvedilol therapy at daily doses of 2 mg/kg maintained for 7 days constituted Groups 3 (n=8), and 2 (n=8), respectively. Each group of 4 rats was housed in standard cages with an area of 40×60 cm. The animals were fed with standard 8 mm food pellets, and fresh daily tap water. The rats were kept in the cages under 12 hours of light, and 12 hours of dark. Ambient temperature, and humidity were set at 22±2°C, and 50±10%, respectively.”

Herein, the method, and agent of anesthesia used (local or general anesthesia) in surgical procedures, and then the experimental method applied should be clearly indicated. For example the following sentences explain our abovementioned arguments; “All surgical procedures were performed under xylazine-ketamine anesthesia. In all groups, ureters were approached through midline abdominal incision. In Group 1, ureters were manipulated without causing obstruction. Results of biochemical, and pathological evaluations performed in Group 1 were considered as baseline values.”

“Through a midline abdominal incision partial ureteral obstruction was achieved by embedding two-thirds of the distal part of the left ureter into psoas muscle using 4/0 silk sutures as described formerly by Wen et al. [ 10 ] ( Figure 1 ). [ 11 ] All rats were subjected to left nephrectomies at the end of the experimental study.” As formulated by the above paragraph, if the method used is not widely utilized, then the first researcher who describes the method should be indicated with relevant references. One or more than one figures with a good resolution, and easily comprehensible legends should be also included in the explanation of the experimental model. For very prevalently used experimental models as torsion models cited in the “Materials and Methods” section, there is no need to include figures in the manuscript.

Partial ureteral obstruction model [ 11 ]

Appropriate signs, and marks placed on the figure will facilitate comprehension of the legends ( Figure 2 ).

Ureteral segments (black arrows) seen in a rat partial ureteral obstruction model [ 11 ]

The signs used will also improve intelligibility of the target. The figures should be indicated within parentheses in their first mention in the “Materials and Methods” section. Headings and as a prevalent convention legends of the figures should be indicated at the end of the manuscript.

If a different method is used in the study, this should be explained in detail. For instance, in a study where the effect of smoking on testes was investigated, the method, and the applicator used to expose rats to cigarette smoke should be indicated in the ‘Methods’ section following classical description. Relevant to the study in question, the following paragraph explaining the study method should be written: “A glass chamber with dimensions of 75 × 50 × 50 cm was prepared, and divided into 4 compartments with wire fences. The rats in the 2., and 4. cages were placed in these compartments. Each compartment contained 4 rats. Cigarette smoke was produced using one cigarette per hour, and smoke coming from the tip, and the filter of the lighted cigarette was pumped into the gas chamber with a pneumatic motor. The rats were exposed to smoke of 6 cigarettes for 6 hours. The compartments of the rats were changed every day so as to achieve balanced exposure of the rats to cigarette smoke.” [ 12 ]

Meanwhile, chemical names, doses, and routes of administration of the substances used in experimental studies should be indicated. If the substance used is a solution or an antibody, then manufacturing firm, and its country should be indicated in parenthesis. This approach can be exemplified as “Animals used in experiments were randomized into 4 groups of 8 animals. Each group was housed in 2 cages each containing 4 animals. The first group did not undergo any additional procedure (Group 1). The second group was exposed to cigarette smoke (Group 2). The third (Group 3), and the fourth (Group 4) groups received daily intraperitoneal injectable doses of 10 mg/kg resveratrol (Sigma-Aldrich, St. Louis, MO, USA). The Group 4 was also exposed to cigarette smoke. [ 12 ]

After all of these procedures, method, and analytical procedure of histopathological examination used should be described-if possible-by a pathologist Similarly, biochemical method used should be referenced, and written by the department of clinical chemistry. It can be inferred that each division should describe its own method. In other words, histopathological, microbiological, and pharmacological method should be described in detail by respective divisions.

If we summarize all the information stated above, understandably sharing of the scientific knowledge is essential.. Since reproducibility of a study demonstrates the robustness of a study, with the detailed approaches indicated above, reproducibility of our study is provided, and the relevant questions of “How?”, and “How much?” are answered. Besides, since ‘Materials, and Methods’, and ‘Results’ sections will constitute a meaningful whole, explanations of all information related to the data mentioned in the ‘Results’ section should be provided. As an important point not to be forgotten, evaluation or measurement method used for each parameter indicated in the ‘Results’ section should be expounded in the “Materials and Methods” section. For example if you used an expression in the” Results” section like “median body mass index (BMI) of the patients was 27.42 kg/m 2 ”, then you should beforehand indicate that comparative evaluation of BMIs will be done in the “Materials and Methods” section. In addition, the description, and significance of the values expressed in the “Results” section should be indicated in the “Materials and Methods” section. In other words, it should be stated that the patients were evaluated based on their BMIs as normal (18–24.9 kg/m 2 ), overweight (25 kg/m 2 –40 kg/m 2 ), and morbid obesity (>40 kg/m 2 ). If you encounter difficulties in writing “Materials and Methods” section, also a valid approach for other sections, firstly simple headings can be written, then you can go into details. In brief, for every parameter, the reader should get clear-cut answers to the questions such as “How did they evaluate this parameter, and which criteria were used?”. [ 1 , 3 , 13 – 15 ]

The last paragraph of the ‘Materials, and Methods’ section should naturally involve statistical evaluations. This section should be written by statisticians. Accordingly, the preferred statistical method, and the justifications for this preference should be indicated. In conventional statistical evaluations, provision of details is not required. In information indicated above, the statement “For statistical analysis, ANOVA test, chi-square test, T test, Kruskal-Wallis test have been used.” is not required very much. Instead, more appropriate expression will be a statement indicating that recommendations of a knowledgeable, and an experienced statistician were taken into consideration or advanced statistical information was reflected on the statistical evaluations as follows: “Chi-square tests were used in intergroup comparisons of categorical variables, and categorical variables were expressed as numbers, and percentages. In comparisons between LUTS, and ED as for age, independent two samples t-test was used. In the evaluation of the factors effective on erectile dysfunction multivariate logistic regresssion test was used. P values lower than 0.05 were considered as statistically significant The calculations were performed using a statistical package program (PASW v18, SPSS Inc, Chicago, IL).” Herein, the type of statistical package used for statistical methods should be emphasized.

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How to Write the Methods Section of a Research Manuscript

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The methods section of a manuscript is one of the most important parts of a research paper because it provides information on the validity of the study and credibility of the results. Inadequate description of the methods has been reported as one of the main reasons for manuscript rejection. The methods section must include sufficient detail so that others could repeat the study and reproduce the results. The structure of the methods section should flow logically and chronologically. There are multiple components of methods sections, including study design, materials used, study procedures, and data analysis. Each element must be adequately described and thoroughly detailed to provide an understanding of how the results were obtained and how to interpret the findings. Studies that involved humans or animals must include an ethics statement of approval from the appropriate governing body. The methods section should explain how subjects were identified and should state inclusion and exclusion criteria. All materials used to complete the study should be described in detail, including equipment, drugs, gases, chemicals, treatments, interventions, or other items. Study procedures should outline all steps taken to obtain the results and clearly state the outcome measures. Subheadings might be helpful for organizing the methods section into subsections when there is a considerable amount of information to report. A well-written methods section will guide the reader through the research process and provide adequate information to evaluate study validity and reproduce the work. The purpose of this paper is to provide guidance for writing the methods section of a manuscript.

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• Introduction

Dissemination of research findings occurs through abstracts, posters, presentations, and manuscripts. 1 , - , 3 Writing the manuscript is considered the last step of the research process because it provides a detailed account of the research from start to finish. 4 , 5 The main components of a research paper include an abstract, the introduction, methods, results, discussion, and conclusions. 3 , 4 Each section of the manuscript is important and has a specific role in describing the research story. However, the methods are one of the most critical sections of a manuscript because the details are used to evaluate and determine the validity of the study and credibility of the results. 6 Validity in research refers to reliability of the measured results: the extent to which the study accurately measured what it intended (internal validity) and how the results can be applied to the general population beyond the study (external validity). 6 , 7

The methods section describes what was done to answer the research question. 8 This section specifies how the research was done, the rationale for the procedures, what materials were used, and how the results were analyzed, all in a clear, concise, and organized manner. 6 The description of the research should provide enough detail so others could repeat the study and reproduce the results. 6 , 9 , 10 Much of the methods section should be written before the study is initiated. Indeed, for funded research, a detailed methods section is written as part of the grant application. There are several aspects of the methods sections, and the essential elements will vary, depending on the type of study. Submission requirements differ among journals; therefore, it is important to consult the instructions for authors for the specific journal to ensure that all necessary elements are included. 11 The purpose of this paper is to describe the different components of the methods section and provide guidance for writing the methods section of a research paper.

• General Considerations

An inadequate description of methods has been identified as one of the top reasons for manuscript rejection. 12 It has been suggested that including too much information is better than having insufficient detail because irrelevant content can later be omitted. 12 The methods section of a research paper is analogous to a recipe. 10 , 13 A recipe is composed of multiple elements, including the list and quantity of ingredients, equipment and tools needed and applicable settings, and the detailed instructions for how to create the recipe. Similar to a recipe, there are different elements of methods to describe in a manuscript. In general, common components of the methods section include a description of the study design, materials used, study procedures, measurements or calculations, and the statistical tests used to analyze the results. Materials used to conduct research are comparable with the ingredients, tools, and equipment for a recipe. Materials represent what was studied, including subjects, equipment or devices, and treatments or interventions. 6 , 14 The steps to create a recipe are akin to study procedures such as the process for data collection, measurements, calculations, and statistical analysis. A summary of the different elements of the methods section is included in Table 1 . The individual components for each element may vary, depending on the nature of the study.

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Methods Section Elements

Although similarities exist between a recipe and the methods section of a research paper, the methods section should not be formatted to read like a recipe. 13 Use past tense for writing the methods section because the study has been completed and describes what was already done. 6 , 9 , 10 , 13 , 14 The methods section should be structured for logical and chronological flow. 6 , 14 , 15 Use of subheadings can be helpful for organizing the different components for the methods section when there is a substantial amount of detail to describe. 6 , 13 However, subheadings may not be required by some journals. An excessive use of subheadings can be distracting to the reader by interrupting the flow of the manuscript. There should not be a subheading for every paragraph. This is particularly distracting when each subheading is followed by a short 1- or 2-sentence paragraph. Paragraphs with fewer than 3 sentences should be avoided; combine the information with another paragraph unless the journal to which the paper will be submitted requires specific subheadings. Subheadings can be useful as an outline when writing the methods section but then might be omitted in the final manuscript.

A common error in manuscript writing is reporting results in the methods section and vice versa. A frequently occurring example is including the number of subjects who participated in the research in the methods section when it was unknown how many met inclusion criteria before study initiation and subject screening. The methods section should only include information available during the planning phase, before study initiation. 10 , 16 There are instances in which study procedures may have changed after the study commencement. This information would be reported in the methods section but the outcomes stated in the results section. The results section should reflect the data obtained from study procedures because this information would be unknown before the study was completed.

• Study Design

The methods section often begins with an overall description of the study design and key attributes, including the type of study, setting, time frame, and procedures. 14 , 15 This provides an overview and context for how the study was conducted with further details and specifics described in subsequent subsections. Study design has been described as a road map for the methods section to provide information for how to understand the approach and interpret the results. 14

Common study designs include observational, bench evaluation, systematic review, randomized controlled trial, survey, and others. Guidelines for writing the manuscript include the Consolidated Standards of Reporting Trials (CONSORT) checklist for randomized controlled trials and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews. 17 , 18 Registration is another consideration for clinical trials and systematic reviews. The International Committee of Medical Journal Editors (ICMJE) 16 requires registration of clinical trials on a public trials registry. Many journals, including R espiratory C are , follow the recommendations for publication set forth by this group. A randomized controlled trial should also include the blinding mechanism and different treatment groups as applicable. 17 Although registration of a systematic review is often not a prerequisite for publication, registering the protocol supports transparency, decreases potential bias, and can help prevent duplication of reviews. 18 An observational study should report if the design was retrospective, prospective, a secondary or post hoc analysis, or other category of observational design. 7

The setting where the study occurred, if it included data from a single-center or multiple centers, and the time frame in which it took place must be included because these factors have implications for clinical practice, generalizability, and validity. 7 Potential study settings might include an ICU (or specific ICU type), medical surgical ward, emergency department, out-patient clinic, home-care environment, or simulation laboratory. The time frame is an essential element for context because practices and trends change over time. A prime example of this is prone positioning for treatment of hypoxemic respiratory failure as use substantially increased during the COVID-19 pandemic. 19

• Ethics Statement

The United States Department of Health and Human Services defines a human subject “as a living individual about whom an investigator (whether professional or student) conducting research obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the biospecimens; or obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens.” 20 The methods section must include a statement regarding approval from an institutional review board (IRB) or ethics committee for research that included human subjects. 16 Quality improvement studies and certain types of surveys are often not considered human subject research and therefore may not require IRB oversight but the decision is made by the IRB. Quality improvement projects, depending on requirements of the institution or organization, can be performed without IRB approval in some cases; however, IRB approval is needed before publication or presentation outside of the institution, and human subject determination is made by the IRB, not the investigators.

Animal studies also require ethics approval to be reported in the methods section. Research that involves animals is subject to approval from the local Institutional Animal Care and Use Committee and must be conducted in accordance with national guidelines, for example, the National Institute of Health Public Health Service Policy on Humane Care and Use of Laboratory Animals. 21 For journals that do not have a specific requirement for where to include the ethics statement within the methods section, many authors typically incorporate it in the initial general description of the study or with the detailed description of the subjects. Some studies have included it at the beginning of the methods section.

Characteristics of the study population should be described. This includes basic demographics (eg, adults or children, age, sex) and general health status such as if the individuals were healthy volunteers or had a specific diagnosis or condition. This information is also needed for control groups. 4 Inclusion criteria for how subjects were identified and selected should be detailed as well as reasons for exclusion. For example, an evaluation of a disease management program included adults ages ≥ 65 years and with COPD who were admitted to 1 of 5 hospitals during a specified time frame. 22 Patients were excluded if they left against medical advice, died during admission, transferred to a hospital outside of the health system, entered hospice care, refused home care, or were unable to participate in education. 22 In this example, subject characteristics (adults with COPD), selection and identification (hospital admission during defined time frame), and exclusion criteria are clearly stated.

When referring to human subjects in research, the terms subject and patient are often used interchangeably, but there is a difference. 23 A patient receives care to improve health, and care is individualized in each particular case. When a patient participates in research, he or she becomes a subject. In research, care is designed to create information and is the same for all subjects based on the study protocol. The individual conducting the research is not always involved in the patient care provided, thus also making the distinction between subject and patient. A common error is to use the word subjects exclusively when writing the manuscript. However, individuals are patients before enrollment. When referring to the broader population of individuals who might benefit from the research findings, the word patients is likely more correct. Participant and volunteer are other terms that can be used in place of subject. Individuals who participated in survey research are typically referred to as respondents . 24

In addition to humans, research subjects may also involve animals or organisms such as cells. When animals are studied, the methods should describe the species, weight, age, and sex of the animals. 6 Ring et al 25 used ex vivo porcine lungs to evaluate the effect of breathing pattern and nebulization on exhaled viral content during mechanical ventilation. The authors reported that the lungs were sourced from a retail processing facility and were from 6-month-old Yorkshire hybrid pigs that weighed 118 kg. In addition, it was noted that approval to conduct the study was granted by the local Institutional Animal Care and Use Committee. This publication demonstrates an appropriate description of animal subjects, including an ethics statement.

• Equipment and Other Materials

Identify all equipment and other materials used in the study, including devices, related accessories, drugs, or chemicals. At first mention of any device, provide the specific name of the item, model number if applicable, and manufacturer information. Many scientific journals do not usually allow use of trademark or registration symbols. 10 The ICMJE recommends that manufacturer name and location be included in parentheses. 16 For example, a study that evaluated the safety and feasibility of breathing high-dose nitric oxide in healthy volunteers used a Sievers 280i nitric oxide analyzer (GE Analytical Instruments, Boulder, Colorado) to measure nitric oxide gas concentration. 26 Subsequent mentions of equipment should be noted by generic name versus trade name when possible. It is important that the methods section does not project any bias that an author may have for a specific device or manufacturer.

Use of figures can be an effective means of providing a visual description of the equipment setup, especially when there are many components involved. This can also help reduce the amount of text and improve understanding of how the equipment was assembled. Figures can be either a photograph of the equipment or a graphic illustration (line drawing), but all components should be clearly labeled. An illustration of the setup used to deliver high-dose nitric oxide in the aforementioned study is provided in Figure 1 . 26 Use of a photograph to depict the experimental setup for measuring peak expiratory flow during mechanical insufflation-exsufflation is demonstrated in Figure 2 . 27 Photographs should be of good quality and include all relevant items. In both examples, all components are clearly identified and labeled.

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Graphic illustration of an experimental setup. From Reference 26.

Photograph of an experimental setup. From Reference 27.

In addition to naming the specific equipment used in the study, settings should also be included in the methods section because these details are highly relevant for duplication of the study. For example, the evaluation of peak expiratory flow during mechanical insufflation-exsufflation provided the pressure settings used during therapy. 27 Not only is this important for repeating the study, but it is also essential for assessing the validity of the results. If the settings were not typical of those used in the study population or in clinical practice, then this would introduce limitations to interpreting and understanding the results.

Equipment preparation is another consideration for the methods section. Describe the calibration process and the frequency for equipment that requires calibration. The flow meter used to measure peak expiratory flow during mechanical insufflation-exsufflation was calibrated and validated annually by the manufacturer. 27 If manufacturer standards for calibration are not followed, then the accuracy of the results may be affected. It should be noted that calibration and validation represent two different processes. 4 Both should be described as applicable.

In addition to equipment, identify all drugs, chemicals, gases, or other materials used specifically for the study. The details for drugs and gases should include the concentration, dose, frequency, and route of administration. Gases should also note the flow used. Chemicals should be noted with the name and concentration as applicable. Use the generic name for drugs. If the trade name for a drug is relevant to the study, then follow the same process for identifying equipment brands and manufacturer information and use the generic name after initial identification. Preparation information may be needed in some cases. For example, detailed preparation information was provided for the bacteriophage used in the animal study conducted by Ring et al. 25 The process for how the bacteriophage was prepared was described in detail as well as the amounts used for the study.

• Study Procedures

The methods section should explicitly detail all procedures, treatments, or interventions used in the study. This portion of the methods section describes how study procedures were performed, the chronological order of procedures, measurements or calculations made, and the specific data elements collected. A rationale may be needed for some procedures, depending on the audience. 6 Outcome measures are often included in the subsection for study procedures, but some authors report them in the overall description for study design.

A comprehensive explanation of the procedures is vital for providing adequate details for reproducibility and validity regardless of the study design. A retrospective cohort study investigated outcomes of children treated with continuous albuterol that contains benzalkonium chloride and preservative-free solutions. 28 Collected data were clearly stated and included subject demographics, diagnosis, mortality risk score, albuterol dose and duration, use of adjunctive therapies, and respiratory support. The methods section for this paper also reported the source of the data extraction (electronic medical records, database, manual chart review) and the process for how therapies were initiated, escalated, and de-escalated (intensivist discretion). 28 The basis for the use of therapies in this study is an important consideration for generalizability because practices vary among institutions and some care might involve the use of protocols.

Diagrams and flow charts can be helpful for illustrating processes or workflow. An evaluation of sputum volume obtained with different cough augmentation techniques outlined the protocol in an illustrated timeline for the sequence of interventions and data collection ( Fig. 3 ). 29 The timeline provides clear information for the procedures that were done, when they were done, and the data elements collected. Data were collected at baseline, at the end of the intervention, and then 1 h after the intervention, followed by a minimum 4-h washout period before the second intervention and data collection. 29 Details with regard to who performed the interventions (5 experienced respiratory clinicians), how they were administered (cough augmentation technique and settings), and subject information (positioning) were comprehensively described.

Illustrated timeline of study protocol that depicts chronological order. From Reference 29.

Measurements obtained during study procedures should be identified along with a description of how they were obtained and the devices used. For example, the same study measured ventilator parameters before, during, and after interventions by using a Fluxmed GrH monitor (MBMED, Buenos Aires, Argentina). 29 Procedures for measurements or techniques with established references do not have to be described in detail and can be omitted if the procedure could be repeated without the specific details. 6 , 12 , 14 This is a common practice for measurements obtained during spirometry. In those instances, provide the reference for the previous work without providing all of the additional details. In a study that aimed to correlate baseline spirometry with airway hyper-responsiveness in methacholine challenge, the reported testing was performed according to published guidelines. 30 The guideline was referenced without providing all the specifics. On the contrary, studies that used novel methods would need to be further described. 6

The outcome measures that address the research question should be clearly stated. Outcome measures are the dependent or response variables assessed to evaluate the impact of the research that is established before beginning the study. 6 , 8 Outcome measures may include both primary and secondary outcomes. The primary outcome is the main measure of the research question, and secondary outcomes provide additional information for interpreting results. The retrospective evaluation of different albuterol solutions used ICU and hospital length of stay as primary outcomes and duration of continuous albuterol, use and duration of adjunctive therapies, and need for mechanical ventilation as secondary outcomes. 28 The primary outcome was sputum volume for the trial that assessed cough augmentation techniques, and secondary outcomes were respiratory mechanics and hemodynamics. 29

• Statistical Analysis

The statistical analysis component is typically included as the last part of the methods section. This subsection describes how the collected data were analyzed through identification of the statistical tests that were used and the P value threshold for statistical significance. A clinical trial that evaluated the effect of endotracheal tube scraping during mechanical ventilation reported that categorical variables were analyzed with the chi-square or Fisher exact test, and continuous variables were presented as mean ± SD or median (interquartile range) based on distribution and analyzed with t test or Mann-Whitney test. 31 P < .05 was considered significant. The statistical analysis section of this paper distinctly identified the tests used to analyze specific data points and provided an explanation for when mean or median was reported.

The statistical analysis should also describe how the power analysis was conducted to determine the appropriate sample size. Justification for the approach should be provided when needed. For example, the study that evaluated the effect of endotracheal tube scraping calculated sample size for each treatment group based on previous institutional data for the mean duration of mechanical ventilation and determined that each group needed 136 subjects with an alpha of 0.05 and power of 0.80. 31 Citing references for the rationale and justification for the selected statistical tests is also an approach to support the choice of test. The previously noted evaluation of methacholine reactivity used a reference to support the use of partition analysis. 30 The software package and version used for data analysis should also be specified in the data analysis portion of the methods section. 16

• A Methods Model

Several publications were used throughout this paper to demonstrate the different elements of the methods section of a research paper. A summary of each of those elements and the individual components comprised within each subsection adapted from the endotracheal tube scraping clinical trial are included in Table 2 . 31 It is important to note how some items were further described in the text, such as the technique for airway suctioning, the definition of a successful spontaneous breathing trial, an explanation for extubation outcome, the elements of the ventilator-associated event prevention bundle, and how ventilator-associated events were defined. These specifics provide additional information to help determine validity and generalizability, and highlight the importance of including enough detail to duplicate the study.

Summary of Methods Elements and Details from a Published Paper

The methods section is an important part of a manuscript because it provides information on the validity of the study. One of the main reasons for manuscript rejection is an inadequate description of the methods. Enough detail must be provided so others could repeat the study and reproduce the results, similar to following a recipe. The methods section should be structured for logical and chronological flow, and be written in past tense. There are multiple components of the methods section that must be adequately described and thoroughly detailed to provide an understanding of how the results were obtained to interpret the findings. Subheadings can be helpful for organizing the methods section into subsections when there is a considerable amount of information to report, but subheadings should be used judiciously. A well-written methods section will guide the reader through the research process and provide adequate information to evaluate study validity and credibility of the results as well as reproduce the work.

• Correspondence: Denise Willis MSc RRT RRT-NPS AE-C FAARC, Respiratory Care Services, Arkansas Children’s Hospital, 1 Children’s Way, Slot 303, Little Rock, AR 72202. E-mail: WillisLD{at}archildrens.org

Ms Willis is a Section Editor for R espiratory C are .

Ms Willis presented a version of this paper at the symposium Research in Respiratory Care at AARC Congress 2022 held November 8, 2022, in New Orleans, Louisiana.

• Copyright © 2023 by Daedalus Enterprises
• Stenson JF ,
• Lendner M ,
• Annesley TM
• Ghasemi A ,
• Bahadoran Z ,
• Zadeh-Vakili A ,
• Montazeri SA ,
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• Canário-Almeida F ,
• Almeida Fonseca J ,
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• 16. ↵ International Committee of Medical Journal Editors . Recommendations for the conduct, reporting, editing, and publication of scholarly work in medical journals . Updated May 2022 . https://www.icmje.org/recommendations/ . Accessed July 6, 2023
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• 20. ↵ United States Department of Health and Human Services . 45 Code of Federal Regulations Part 46 . https://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/index.html . Accessed July 6, 2023
• 21. ↵ National Institutes of Health Office of Laboratory and Animal Welfare . Public Health Service Policy on Humane Care and Use of Laboratory Animals . https://olawnihgov/policies-laws/phs-policyhtm 2015 . Accessed July 6, 2023.
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• Open access
• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

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

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

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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).

Author information

These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

Authors and Affiliations

VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium

Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

AB InBev SA/NV, Brouwerijplein 1, B-3000, Leuven, Belgium

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S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

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Correspondence to Kevin J. Verstrepen .

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

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Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

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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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