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The Advanced Placement (AP) curriculum is administered by the College Board and serves as a standardized set of year-long high school classes that are roughly equivalent to one semester of college-level coursework. Although most students enroll in an actual course to prepare for their AP exams, many others will self-study for the exams without enrolling in the actual AP class.

AP classes are generally stand-alone subjects that easily translate to traditional college courses. Typically, they culminate in a standardized exam on which students are graded using a 5-point scale, which colleges and universities will use to determine credit or advanced standing. Starting in fall of 2014, though, this traditional AP course and exam format has begun to adapt in efforts by the College Board to reflect less stringent rote curriculum and a heavier emphasis on critical thinking skills.

The AP Capstone program is at the center of these changes, and its culmination course is AP Research. If you are interested in learning more about the AP Research Course and Assessment, and how they can prepare you for college-level work, read on for CollegeVine’s Ultimate Guide to the AP Research Course and Assessment.

About the Course and Assessment

The AP Research course is the second of two classes required for the AP Capstone™ Diploma . In order to enroll in this course you need to have completed the AP Seminar course during a previous year. Through that course, you will have learned to collect and analyze information with accuracy and precision, developed arguments based on facts, and effectively communicated your conclusions. During the AP Research course, you apply these skills on a larger platform. In the AP Research course, you can expect to learn and apply research methods and practices to address a real-world topic of your choosing, with the end result being the production and defense of a scholarly academic paper. Students who receive a score of 3 or higher on both the AP Seminar and AP Research courses earn an AP Seminar and Research Certificate™. Students who receive a score of 3 or higher on both courses and on four additional AP exams of their choosing receive the AP Capstone Diploma™.    

The AP Research course will guide you through the design, planning, and implementation of a year-long, research-based investigation to address a research question of interest to you. While working with an expert advisor, chosen by you with the help of your teacher, you will explore an academic topic, problem, or issue of your choosing and cultivate the skills and discipline necessary to conduct independent research and produce and defend a scholarly academic paper. Through explicit instruction in research methodology, ethical research practices, and documentation processes, you will develop a portfolio of scholarly work to frame your research paper and subsequent presentation of it.

Although the core content and skills remain standardized for every AP Research course, the implementation of this instruction may vary. Some AP Research courses may have a specific disciplinary focus wherein the course content is rooted in a specific subject, such as AP Research STEM Inquiries or AP Research Performing and Visual Arts. Similarly, other AP Research courses are offered in conjunction with a separate and specific AP class, such as AP Research and AP Biology wherein students are concurrently enrolled in both AP courses and content is presented in a cross-curricular approach. Alternatively, AP Research may be presented in the form of an internship wherein students who are already working with a discipline-specific expert adviser conduct independent studies and research of the student’s choosing while taking the AP Research class. Finally, some AP Research courses are delivered independently as a research methods class. In this style of class, students develop inquiry methods for the purpose of determining which method best fits their chosen topic of inquiry/research question, and each student then uses a selected method to complete his or her investigation.    

Only schools that currently offer the AP Capstone Diploma may offer the AP Research course. Because it is a part of a larger comprehensive, skills-based program, students may not self-study for the AP Research course or final paper. At this time, home-schooled students, home-school organizations, and online providers are not eligible to participate in AP Capstone.

Your performance in the AP Research course is assessed through two performance tasks. The first is the Academic Paper, which accounts for 75% of your total AP score. In this paper, you will present the findings of your yearlong research in 4,000-5,000 words. Although the official submission deadline for this task is April 30, the College Board strongly recommends that this portion of your assessment be completed by April 15 in order to allow enough time for the second of your performance tasks.

The second performance task is your Presentation and Oral Defense, which accounts for the remaining 25% of your total AP score. Using your research topic, your will prepare a 15-20 minute presentation in an appropriate format with appropriate accompanying media. Your defense will include fielding three to four questions from a panel consisting of your AP Research teacher and two additional panel members chosen at the discretion of your teacher.    

In 2016, fewer than 3,000 students submitted an AP Research project, but enrollment is projected to grow rapidly, since 12,000 students took the AP Seminar assessment in 2016 and most will presumably go on to submit an AP Research project in 2017. Scores from the 2016 AP Research projects reveal a high pass rate (score of three or higher) but a difficult rate of mastery. While 67.1% of students taking the assessments scored a three or higher, only 11.6% received the highest score of a five, while nearly 40% received a three. Only 2% of students submitting research projects received the lowest score of one.    

A full course description that can help to guide your planning and understanding of the knowledge required for the AP Research course and assessments can be found in the College Board course description .

Read on for tips for successfully completing the AP Research course.

How Should I Prepare for the AP Research Course?

As you undertake the AP Research course and performance tasks, you will be expected to conduct research, write a scholarly paper, and defend your work in a formal presentation.   Having already completed the AP Seminar course, these skills should be familiar to you. You should use your scores on the AP Seminar performance task to help guide your preparations for the AP Research performance tasks.

Carefully review your scores from AP Seminar. Make sure you understand where points were lost and why. It may be helpful to schedule a meeting with your AP Seminar teacher to review your work. Alternatively, your AP Research teacher may be willing to go over your AP Seminar projects with you. You might also ask a classmate to review your projects together to get a better idea of where points were earned and where points were lost. Use this review as a jumping point for your AP Research studies. You should go into the course with a good idea of where your strengths lie, and where you need to focus on improving.

A sample timeline for the AP Research course is available on page 36 of the course description . One detail worth noting is that the recommended timeline actually begins not in September with the start of the new school year, but instead begins in May with the completion of the AP Seminar course during the previous school year. It is then that you should begin to consider research topics, problems, or ideas. By September of the following school year, it is recommended that you have already finalized a research question and proposal, completed an annotated bibliography, and prepared to begin a preliminary inquiry proposal for peer review.    

What Content Will I Be Held Accountable For During the AP Research Course?

To be successful in the AP Research class, you will begin with learning to investigate relevant topics, compose insightful problem statements, and develop compelling research questions, with consideration of scope, to extend your thinking.   Your teacher will expect you to demonstrate perseverance through setting goals, managing time, and working independently on a long-term project. Specifically, you will prepare for your research project by:

• Identifying, applying, and implementing appropriate methods for research and data collection
• Accessing information using effective strategies
• Evaluating the relevance and credibility of information from sources and data
• Reading a bibliography for the purpose of understanding that it is a source for other research and for determining context, credibility, and scope
• Attributing knowledge and ideas accurately and ethically, using an appropriate citation style
• Evaluating strengths and weaknesses of others’ inquiries and studies

As in the AP Research course, you will continue to investigate real-world issues from multiple perspectives, gathering and analyzing information from various sources in order to develop credible and valid evidence- based arguments. You will accomplish this through instruction in the AP Research Big Ideas, also called the QUEST Framework. These include:

• Question and Explore: Questioning begins with an initial exploration of complex topics or issues. Perspectives and questions emerge that spark one’s curiosity, leading to an investigation that challenges and expands the boundaries of one’s current knowledge.
• Understand and Analyze Arguments: Understanding various perspectives requires contextualizing arguments and evaluating the authors’ claims and lines of reasoning.
• Evaluate Multiple Perspectives: Evaluating an issue involves considering and evaluating multiple perspectives, both individually and in comparison to one another.
• Synthesize Ideas: Synthesizing others’ ideas with one’s own may lead to new understandings and is the foundation of a well-reasoned argument that conveys one’s perspective.
• Team, Transform, and Transmit: Teaming allows one to combine personal strengths and talents with those of others to reach a common goal. Transformation and growth occur upon thoughtful reflection. Transmitting requires the adaptation of one’s message based on audience and context.

In addition, you will use four distinct reasoning processes as you approach your research. The reasoning processes are situating, choosing, defending , and connecting . When you situate ideas, you are aware of their context in your own perspective and the perspective of others, ensuring that biases do not lead to false assumptions. When you make choices about ideas and themes, you recognize that these choices will have both intended and unintentional consequences. As you defend your choices, you explain and justify them using a logical line of reasoning. Finally, when you connect ideas you see intersections within and/or across concepts, disciplines, and cultures.

For a glossary of research terms that you should become familiar with, see page 62 of the course description .

How Will I Know If I’m Doing Well in the AP Research Course?

Because your entire score for the AP Research course is determined by your research paper and presentation, which come at the very end of the course, it can be difficult to gauge your success until that point. Do yourself a favor and do not wait until your final scores come back to determine how successful you have been in the course.

As you undertake the AP Research course, there will be many opportunities for formative assessments throughout the semester. These assessments are used to give both you and your teacher an idea of the direction of instruction needed for you to master the skills required in the AP Research course. You should use these assessments to your advantage and capitalize on the feedback you receive through each. A list of possible activities used for these assessments can be found on page 41 of the course description .

Another way that you and your teacher will track your progress is through your Process and Reflection Portfolio (PREP). The PREP serves to document your development as you investigate your research questions, thereby providing evidence that you have demonstrated a sustained effort during the entire inquiry process. You will review your PREP periodically with your teacher, who will use it as a formative assessment to evaluate your progress.

Throughout the course, you will be assigned prompts and questions to respond to in your PREP. You will use this portfolio to document your research or artistic processes, communication with your expert adviser, and reflections on your thought processes. You should also write freely, journaling about your strengths and weaknesses with regard to implementing such processes and developing your arguments or aesthetic rationales. 

Your final PREP should include:

• Table of contents
• Completed and approved proposal form
• Specific pieces of work selected by the student to represent what he or she considers to be the best showcase for his or her work. (Examples might include: in-class (teacher-directed) free-writing about the inquiry process, resource list, annotated bibliography of any source important to the student’s work, photographs, charts, spreadsheets, and/or links to videos or other relevant visual research/project artifacts, draft versions of selected sections of the academic paper, or notes in preparation for presentation and oral defense.)
• Documentation of permission(s) received from primary sources, if required — for example, permission(s) from an IRB or other agreements with individuals, institutions, or organizations that provide primary and private data such as interviews, surveys, or investigations
• Documentation or log of the student’s interaction with expert adviser(s) and the role the expert adviser(s) played in the student’s learning and inquiry process (e.g., What areas of expertise did the expert adviser have that the student needed to draw from? Did the student get the help he or she needed — and if not, what did he or she do to ensure that the research process was successful? Which avenues of exploration did the expert adviser help the student to discover?)
• Questions asked to and feedback received from peer and adult reviewers both in the initial stages and at key points along the way
• Reflection on whether or not the feedback was accepted or rejected and why
• Attestation signed by the student which states, “I hereby affirm that the work contained in this Process and Reflection Portfolio is my own and that I have read and understand the AP Capstone TM Policy on Plagiarism and Falsification or Fabrication of Information”

It cannot be stressed enough how important it is to maintain strong communications with your teacher as you progress through the AP Research course. Not only is your teacher your best resource for learning new skills and knowledge, but also it is your teacher who will be responsible for grading your final performance tasks and as such, you should always have a strong understanding of how your work is being assessed and the ways in which you can improve it. Remember, your teacher wants you to succeed just as much as you do; work together as a team to optimize your chances.

How Should I Choose a Research Topic?

You will begin to consider research topics before the school year even starts. If your AP Research class is offered in conjunction with another course, such as those rooted in a specific subject or linked to another concurrent AP course, you will have some idea of the direction in which your research should head. Regardless of whether you know the precise subject matter of your topic, you should begin by asking yourself what you want to know, learn, or understand. The AP Research class provides a unique opportunity for you to guide your own learning in a direction that is genuinely interesting to you. You will find your work more engaging, exciting, and worthwhile if you choose a topic that you want to learn more about.

As you begin to consider research topics, you should:

• Develop a list of topics and high-level questions that spark your interest to engage in an individual research project
• Identify potential expert advisers to guide you in the planning and development of your research project (For tips on how to find a mentor, read CollegeVine’s “ How to Choose a Winning Science Fair Project Idea ”)
• Identify potential opportunities (if you are interested) to perform primary research with an expert adviser during the summer, via internships or summer research projects for high school students offered in the community and local higher education institutions
• Discuss research project planning skills and ideas with students who are currently taking or have already taken the AP Research course

You might also find inspiration from reading about past AP Research topics. One list of potential research questions can be found here and another can be found here . Keep in mind that these lists make great starting points and do a good job of getting you thinking about important subjects, but your research topic should ultimately be something that you develop independently as the result of careful introspection, discussions with your teacher and peers, and your own preliminary research.

Finally, keep in mind that if you pursue a research project that involves human subjects, your proposal will need to be reviewed and approved by an institutional review board (IRB) before experimentation begins. Talk with your teacher to decide if this is the right path for you before you get too involved in a project that may not be feasible.

Once you have decided on a research topic, complete an Inquiry Proposal Form. This will be distributed by your teacher and can also be found on page 55 of the course description .

How Do I Conduct My Research?

By the time you begin your AP Research course, you will have already learned many of the basics about research methods during your AP Seminar course. You should be comfortable collecting and analyzing information with accuracy and precision, developing arguments based on facts, and effectively communicating your point of view. These will be essential skills as you move forward in your AP Research project.

As you undertake your work, remember the skills you’ve already learned about research:

• Use strategies to aid your comprehension as you tackle difficult texts.
• Identify the author’s main idea and the methods that he or she uses to support it.
• Think about biases and whether other perspectives are acknowledged.
• Assess the strength of research, products, and arguments.
• Look for patterns and trends as you strive to make connections between multiple arguments.
• Think about what other issues, questions, or topics could be explored further.

You should be certain to keep track of all sources used in your research and cite them appropriately. The College Board has a strict policy against plagiarism. You can read more about its specifics on page 60 of the course description .

How Do I Write My Paper?

Before you begin writing your final paper, make sure to thoroughly read the Task Overview handout which will be distributed by your teacher. If you would like to see it beforehand, it can be found on page 56 of the course description . You should also review the outline of required paper sections on page 49 of the course description .

Your paper must contain the following sections:

› Introduction

› Method, Process, or Approach

› Results, Product, or Findings

› Discussion, Analysis, and/or Evaluation

› Conclusion and Future Directions

› Bibliography

Before you begin writing, organize your ideas and findings into an outline using the sections listed above. Be sure to consider how you can connect and analyze the evidence in order to develop an argument and support a conclusion. Also think about if there are any alternate conclusions that could be supported by your evidence and how you can acknowledge and account for your own biases and assumptions. 

Begin your paper by introducing and contextualizing your research question or problem. Make sure to include your initial assumptions and/or hypothesis. Next, include a literature review of previous work in the field and various perspectives on your topic. Use the literature review to highlight the gap in the current field of knowledge to be addressed by your research project. Then, explain and justify your methodology, present your findings, evidence, or data, and interpret the significance of these findings. Discuss implications for further research or limitations of your existing project. Finally, reflect on the project, how it could impact its field, and any possible next steps. Your paper should conclude with a comprehensive bibliography including all of the sources used in your process.

Make sure to proofread and edit your paper yourself, have it proofread and edited by a friend, and then proofread and edit it again before you complete your final draft.

How Do I Prepare For My Oral Defense?

Once your paper is finished, you may be tempted to sit back and rest on your laurels. Although you’ve no doubt expended a tremendous about of energy in producing a final product you can be proud of, don’t forget that the work is not over yet. Your oral defense accounts for 25% of your total score so it should be taken seriously.

Your oral defense is a 15-20 minute presentation that uses appropriate media to present your findings to an oral defense panel. You may choose any appropriate format for your presentation, as long as the presentation reflects the depth of your research. If your academic paper was accompanied by an additional piece of scholarly work (e.g., performance, exhibit, product), you should arrange with your teacher for him or her, along with the panelists, to view the scholarly work prior to your presentation.

As you plan your presentation, consider how you can best appeal to your audience. Consider different mediums for your presentation, and how those mediums might affect your credibility as a presenter. You want to be engaging to your audience while still being taken seriously.

Following your presentation, you will field three or four questions from your panelists. These will include one question pertaining to your research or inquiry process, one question focused on your depth of understanding, and one question about your reflection throughout the inquiry process as evidenced in your PREP. The fourth question and any follow-up questions are at the discretion of the panel. A list of sample oral defense questions begins on page 52 of the course description . For a complete outline of the oral defense, see page 49 of the course description . 

How Will My Work Be Assessed?

Because this assessment is only available to students enrolled in the AP Capstone program, your teacher will register you for the assessment when you enroll in the course. You should confirm with your teacher that you are registered for the assessment no later than March 1. 

You will submit your final paper and complete your oral presentation no later than April 30, at which point your teacher will submit your work and scores through an AP Digital Portfolio. Your presentation will be scored by your teacher alone. Your paper will be scored by your teacher and validated by the College Board.

You may find the scoring rubric from the 2016 performance tasks available here . You may find a collection authentic student research papers and scoring explanations available here .

Preparing for any AP assessment can be a stressful process. Having a specific plan of attack and a firm grasp of how your work is assessed will help you to feel prepared and score well. Use CollegeVine’s Ultimate Guide to the AP Research Course and Assessment to help shape your understanding of the course and how to complete your performance tasks effectively. When submission day arrives, you should feel better prepared and informed about the work you have produced.

For more about information about APs, check out these CollegeVine posts:

• Can AP Tests Actually Save You Thousands of Dollars?

• Should I Take AP/IB/Honors Classes?

• How to Choose Which AP Courses and Exams to Take

• What If My School Doesn’t Offer AP or IB Courses?

• Are All APs Created Equal in Admissions?

Want access to expert college guidance — for free? When you create your free CollegeVine account, you will find out your real admissions chances, build a best-fit school list, learn how to improve your profile, and get your questions answered by experts and peers—all for free. Sign up for your CollegeVine account today to get a boost on your college journey.

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Advanced Placement (AP)

AP Research is a class introduced by the College Board as a part of its new AP Capstone program. But what does it really involve? How can you do well?

In this article, I'll provide an overview of AP Research and give you some more information about whether you should take it and how you can be successful in the class.

What Is AP Research?

AP Research is the second course that students take in the AP Capstone program . It comes after AP Seminar.

If you take AP Seminar and AP Research, you will earn an AP Research and Seminar Certificate, and if you take both classes in addition to four other AP courses and exams, you will earn an AP Capstone Diploma. This program is very new (the College Board rolled out the full version in the fall of 2014), but you will most likely benefit from it in the college application process when schools see the types of advanced assignments you've completed in these research-focused classes.

In AP Research, students are encouraged to explore a topic or problem that interests them and design, plan, and conduct a year-long research project centered around it. The class represents the culmination of skills that students learn in AP Seminar, which include effectively analyzing sources, formulating coherent arguments backed up by evidence, and examining issues from differing points of view. Smaller research projects in AP Seminar will prepare you for the large-scale research project you will undertake in AP Research.

What Exactly Will You Do in AP Research?

AP Research consists entirely of a year-long research project. The end product is a 4000-5000 word academic paper and a 15-20 minute presentation with an oral defense. You will also be expected to compile the materials you used in your research into a portfolio. This piece of work is similar to a thesis project, so it’s good preparation for college academics. Topics for the research project are usually relatively open, but arguments for and against solutions to major problems in society tend to be the main focus. For example, you might investigate whether the government should invest more resources in finding and supporting sustainable energy sources.

In your academic paper, you will be expected to:

Introduce and contextualize your research question and your initial thoughts and hypotheses about it. In the case of my example, the research question might be "Should the government devote more resources to sustainable energy projects?" You would reflect on the question briefly here and share your initial uninformed opinions before diving into any research.

Review previous ideas and works on the subject and their arguments and perspectives. This is where you would address arguments for and against the adoption of policies to promote the use of sustainable energy. This section lays the groundwork for your arguments in later sections of the paper.

Explain your research method and why you approached the question this way. Here, you would discuss how you went about compiling sources for your research and how you collected the information. This lends credibility to your argument in the next section.

Present your findings and interpret their significance in connection to your research question. In this section, you would lay out your argument based on the evidence you discovered through your research. In the example, your argument might be that we should devote more resources to sustainable energy projects because the long term consequences of continuing to use non-renewable energy sources will be extremely dire. You could support this argument with research that you touched on in previous sections.

Discuss the implications and limitations of your findings and reflect on the process. This is where you would talk about any qualifiers related to your argument in the previous section. If you can't be absolutely sure of a conclusion that you drew or there is some speculation involved, you would go over those potential limitations. You would also talk about what your findings mean in a larger context.

Talk about potential next steps on the issue in view of these findings. Basically, this is the "so what?" section. This is where you would present your ideas for what practical steps the world might take based on your research. In the example, this could be something like providing better tax incentives for businesses that use renewable energy sources or rearranging money in the government's budget in a specific way so that more of it goes towards clean energy projects.

Provide a complete bibliography. This is pretty self-explanatory. You'll need to cite all your sources correctly and make sure that they're trustworthy.

After you turn in your paper, you will also deliver a 15-20 minute presentation to a panel of teachers in whatever format works best for your research. You'll be asked to give a defense of your findings after your presentation. Your AP Research teacher and two panel members chosen by your teacher will ask you three or four questions about your work, and you'll have to answer them based on your research. 

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Should You Take AP Research?

First of all, you can only take AP Research if you take AP Seminar beforehand. Make sure you plan out your classes carefully if you want to end up in this class!

If you are looking to earn the AP Research and Seminar Certificate or the AP Capstone Diploma, you will need to take this class. Keep in mind that for the Capstone Diploma you’ll also need to take four more AP classes and exams.

Some colleges will offer you credit for taking these classes or will allow you to place out of introductory courses that are required for other students. This can make things a little easier on you during your freshman year.

You’ll also be better prepared for college academics if you take AP Research. You will already be familiar with the process of collecting research and using it to formulate an opinion on a topic. When you're assigned your first research paper, you’ll know exactly where to start.

Aside from those benefits, AP Research can be a fun way to explore a topic that genuinely interests you. You'll have a ton of freedom when it comes to choosing your topic, so you can explore almost any idea that you find compelling. AP Research is a good choice for students who are looking for a way to enrich their high school experience with independent research and enroll in competitive college programs.

How Can You Do Well in AP Research?

In AP Research, the most important rule for doing well is to avoid falling behind! ;Since your entire grade rests on one long-term project, you will need to make sure that you are diligent about staying on task throughout the year. It’s tempting to procrastinate when it seems like you have such a long time to complete the project, but you won’t get the most out of your research if you don’t spread out your work. You want to avoid turning in a sub-par project that you don’t believe in because you ran out of time.

I would also suggest that you finish doing all of your research before you start writing any part of your paper. It's hard to write a cohesive argument when you're adding to it piece by piece as you go along. It's best to compile all the information you need first, figure out your argument based on the evidence, and then start structuring your paper around it. This might seem obvious, but sometimes with these types of projects it's tempting to start working on the part that you actually have to turn in before you've fully explored all the background information.

In a related point, you should be flexible and accept that you may need to reframe your research question. You never know what dead ends you might hit or how you might need to change your project as you learn more about your topic. The best way to plan for these scenarios is to start your research early. The highest-quality projects will be those that adapt to new findings over time. You will have to defend your work, so you should be sure that you believe in the point of view that you’re selling and that it's backed up by solid evidence.

Finally, you should choose a research question that fascinates you. Working on a research project for a whole year can get tedious, and you don't want to be completely sick of your topic after a couple of months. Talk to your teacher about your interests so that you can work together to find a viable research question that will hold your focus.

AP Research is the second class in the AP Capstone program after AP Seminar. It's similar to an independent study class and consists entirely of one year-long research project on a topic of your choice. You will write a research paper summarizing your findings and then give an oral presentation followed by a defense of your argument.

AP Research can be a useful class for students who want to be well-prepared for college-level assignments. It's a great way to hone your skills in effectively conducting research and formulating arguments based on evidence. It also might be a nice break from your other classes because of the level of freedom it provides to students. It's an opportunity to learn more about nearly any topic or question that intrigues you!

What's Next?

Not sure if you can take AP Research at your school? Consult this article for a list of all the high schools that offer the AP Capstone program.

You should also take at this article for a detailed guide to which AP classes you should take in general.

If you're on the fence about AP classes in general, check out this article for more information on how AP classes and exams might benefit you.

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Samantha is a blog content writer for PrepScholar. Her goal is to help students adopt a less stressful view of standardized testing and other academic challenges through her articles. Samantha is also passionate about art and graduated with honors from Dartmouth College as a Studio Art major in 2014. In high school, she earned a 2400 on the SAT, 5's on all seven of her AP tests, and was named a National Merit Scholar.

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How to Write an APA Methods Section | With Examples

Published on February 5, 2021 by Pritha Bhandari . Revised on June 22, 2023.

The methods section of an APA style paper is where you report in detail how you performed your study. Research papers in the social and natural sciences often follow APA style. This article focuses on reporting quantitative research methods .

In your APA methods section, you should report enough information to understand and replicate your study, including detailed information on the sample , measures, and procedures used.

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Table of contents

Structuring an apa methods section.

Participants

Example of an APA methods section

Other interesting articles, frequently asked questions about writing an apa methods section.

The main heading of “Methods” should be centered, boldfaced, and capitalized. Subheadings within this section are left-aligned, boldfaced, and in title case. You can also add lower level headings within these subsections, as long as they follow APA heading styles .

To structure your methods section, you can use the subheadings of “Participants,” “Materials,” and “Procedures.” These headings are not mandatory—aim to organize your methods section using subheadings that make sense for your specific study.

Note that not all of these topics will necessarily be relevant for your study. For example, if you didn’t need to consider outlier removal or ways of assigning participants to different conditions, you don’t have to report these steps.

The APA also provides specific reporting guidelines for different types of research design. These tell you exactly what you need to report for longitudinal designs , replication studies, experimental designs , and so on. If your study uses a combination design, consult APA guidelines for mixed methods studies.

Detailed descriptions of procedures that don’t fit into your main text can be placed in supplemental materials (for example, the exact instructions and tasks given to participants, the full analytical strategy including software code, or additional figures and tables).

Prevent plagiarism. Run a free check.

Begin the methods section by reporting sample characteristics, sampling procedures, and the sample size.

Participant or subject characteristics

When discussing people who participate in research, descriptive terms like “participants,” “subjects” and “respondents” can be used. For non-human animal research, “subjects” is more appropriate.

Specify all relevant demographic characteristics of your participants. This may include their age, sex, ethnic or racial group, gender identity, education level, and socioeconomic status. Depending on your study topic, other characteristics like educational or immigration status or language preference may also be relevant.

Be sure to report these characteristics as precisely as possible. This helps the reader understand how far your results may be generalized to other people.

The APA guidelines emphasize writing about participants using bias-free language , so it’s necessary to use inclusive and appropriate terms.

Sampling procedures

Outline how the participants were selected and all inclusion and exclusion criteria applied. Appropriately identify the sampling procedure used. For example, you should only label a sample as random  if you had access to every member of the relevant population.

Of all the people invited to participate in your study, note the percentage that actually did (if you have this data). Additionally, report whether participants were self-selected, either by themselves or by their institutions (e.g., schools may submit student data for research purposes).

Identify any compensation (e.g., course credits or money) that was provided to participants, and mention any institutional review board approvals and ethical standards followed.

Sample size and power

Detail the sample size (per condition) and statistical power that you hoped to achieve, as well as any analyses you performed to determine these numbers.

It’s important to show that your study had enough statistical power to find effects if there were any to be found.

Additionally, state whether your final sample differed from the intended sample. Your interpretations of the study outcomes should be based only on your final sample rather than your intended sample.

Write up the tools and techniques that you used to measure relevant variables. Be as thorough as possible for a complete picture of your techniques.

Primary and secondary measures

Define the primary and secondary outcome measures that will help you answer your primary and secondary research questions.

Specify all instruments used in gathering these measurements and the construct that they measure. These instruments may include hardware, software, or tests, scales, and inventories.

• To cite hardware, indicate the model number and manufacturer.
• To cite common software (e.g., Qualtrics), state the full name along with the version number or the website URL .
• To cite tests, scales or inventories, reference its manual or the article it was published in. It’s also helpful to state the number of items and provide one or two example items.

Make sure to report the settings of (e.g., screen resolution) any specialized apparatus used.

For each instrument used, report measures of the following:

• Reliability : how consistently the method measures something, in terms of internal consistency or test-retest reliability.
• Validity : how precisely the method measures something, in terms of construct validity  or criterion validity .

Giving an example item or two for tests, questionnaires , and interviews is also helpful.

Describe any covariates—these are any additional variables that may explain or predict the outcomes.

Quality of measurements

Review all methods you used to assure the quality of your measurements.

These may include:

• training researchers to collect data reliably,
• using multiple people to assess (e.g., observe or code) the data,
• translation and back-translation of research materials,
• using pilot studies to test your materials on unrelated samples.

For data that’s subjectively coded (for example, classifying open-ended responses), report interrater reliability scores. This tells the reader how similarly each response was rated by multiple raters.

Report all of the procedures applied for administering the study, processing the data, and for planned data analyses.

Data collection methods and research design

Data collection methods refers to the general mode of the instruments: surveys, interviews, observations, focus groups, neuroimaging, cognitive tests, and so on. Summarize exactly how you collected the necessary data.

Describe all procedures you applied in administering surveys, tests, physical recordings, or imaging devices, with enough detail so that someone else can replicate your techniques. If your procedures are very complicated and require long descriptions (e.g., in neuroimaging studies), place these details in supplementary materials.

To report research design, note your overall framework for data collection and analysis. State whether you used an experimental, quasi-experimental, descriptive (observational), correlational, and/or longitudinal design. Also note whether a between-subjects or a within-subjects design was used.

For multi-group studies, report the following design and procedural details as well:

• how participants were assigned to different conditions (e.g., randomization),
• instructions given to the participants in each group,
• interventions for each group,
• the setting and length of each session(s).

Describe whether any masking was used to hide the condition assignment (e.g., placebo or medication condition) from participants or research administrators. Using masking in a multi-group study ensures internal validity by reducing research bias . Explain how this masking was applied and whether its effectiveness was assessed.

Participants were randomly assigned to a control or experimental condition. The survey was administered using Qualtrics (https://www.qualtrics.com). To begin, all participants were given the AAI and a demographics questionnaire to complete, followed by an unrelated filler task. In the control condition , participants completed a short general knowledge test immediately after the filler task. In the experimental condition, participants were asked to visualize themselves taking the test for 3 minutes before they actually did. For more details on the exact instructions and tasks given, see supplementary materials.

Data diagnostics

Outline all steps taken to scrutinize or process the data after collection.

This includes the following:

• Procedures for identifying and removing outliers
• Data transformations to normalize distributions
• Compensation strategies for overcoming missing values

To ensure high validity, you should provide enough detail for your reader to understand how and why you processed or transformed your raw data in these specific ways.

Analytic strategies

The methods section is also where you describe your statistical analysis procedures, but not their outcomes. Their outcomes are reported in the results section.

These procedures should be stated for all primary, secondary, and exploratory hypotheses. While primary and secondary hypotheses are based on a theoretical framework or past studies, exploratory hypotheses are guided by the data you’ve just collected.

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This annotated example reports methods for a descriptive correlational survey on the relationship between religiosity and trust in science in the US. Hover over each part for explanation of what is included.

The sample included 879 adults aged between 18 and 28. More than half of the participants were women (56%), and all participants had completed at least 12 years of education. Ethics approval was obtained from the university board before recruitment began. Participants were recruited online through Amazon Mechanical Turk (MTurk; www.mturk.com). We selected for a geographically diverse sample within the Midwest of the US through an initial screening survey. Participants were paid USD $5 upon completion of the study.

A sample size of at least 783 was deemed necessary for detecting a correlation coefficient of ±.1, with a power level of 80% and a significance level of .05, using a sample size calculator (www.sample-size.net/correlation-sample-size/).

The primary outcome measures were the levels of religiosity and trust in science. Religiosity refers to involvement and belief in religious traditions, while trust in science represents confidence in scientists and scientific research outcomes. The secondary outcome measures were gender and parental education levels of participants and whether these characteristics predicted religiosity levels.

Religiosity

Religiosity was measured using the Centrality of Religiosity scale (Huber, 2003). The Likert scale is made up of 15 questions with five subscales of ideology, experience, intellect, public practice, and private practice. An example item is “How often do you experience situations in which you have the feeling that God or something divine intervenes in your life?” Participants were asked to indicate frequency of occurrence by selecting a response ranging from 1 (very often) to 5 (never). The internal consistency of the instrument is .83 (Huber & Huber, 2012).

Trust in Science

Trust in science was assessed using the General Trust in Science index (McCright, Dentzman, Charters & Dietz, 2013). Four Likert scale items were assessed on a scale from 1 (completely distrust) to 5 (completely trust). An example question asks “How much do you distrust or trust scientists to create knowledge that is unbiased and accurate?” Internal consistency was .8.

Potential participants were invited to participate in the survey online using Qualtrics (www.qualtrics.com). The survey consisted of multiple choice questions regarding demographic characteristics, the Centrality of Religiosity scale, an unrelated filler anagram task, and finally the General Trust in Science index. The filler task was included to avoid priming or demand characteristics, and an attention check was embedded within the religiosity scale. For full instructions and details of tasks, see supplementary materials.

For this correlational study , we assessed our primary hypothesis of a relationship between religiosity and trust in science using Pearson moment correlation coefficient. The statistical significance of the correlation coefficient was assessed using a t test. To test our secondary hypothesis of parental education levels and gender as predictors of religiosity, multiple linear regression analysis was used.

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
• Social desirability bias

In your APA methods section , you should report detailed information on the participants, materials, and procedures used.

• Describe all relevant participant or subject characteristics, the sampling procedures used and the sample size and power .
• Define all primary and secondary measures and discuss the quality of measurements.
• Specify the data collection methods, the research design and data analysis strategy, including any steps taken to transform the data and statistical analyses.

You should report methods using the past tense , even if you haven’t completed your study at the time of writing. That’s because the methods section is intended to describe completed actions or research.

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.

Cite this Scribbr article

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AP ® Research Handbook

Chapter 2 literature review.

List resources for conducting literature review. Show example of literature review with inline citations. Show ways to keep track of sources for bibliography.

• contains example literature reviews from political science, philosophy, and chemistry.

Consider using a reference management system like Mendeley to organize your sources as you conduct your literature review. In fact, Mendeley has a Literature Search function, so you can manage sources and conduct literature reviews at the same time. See the Bibliography Management Section for more information on managing sources.

Databases for Literature Reviews

• Browse by subjects in the humanities and sciences. This can be your starting point if you have not developed a research topic.
• Open-access journal articles in fields such as mathematics, statistics, economics, physics, quantitative biology, quantiative finance, and electrical engineering
• arXiv to BibTex : Outputs automated citations in BibTeX and other formats by typing the arXiv number of the article. For instance, just type in 1905.03758 into the search engine if the article is labeled arXiv: 1905.03758.
• Alternatively, use Mendeley Web Importer to import article into Mendeley Desktop for automated citation outputs.
• Download Mendeley Desktop and register for a free account. Mendeley Desktop syncs with your online Mendeley account, but the literature search is currently only available in the desktop version.
• Mendeley is primarly a reference managements software, so you can organize your citations as you conduct your literature review.
• Search engine with the world’s largest collectin of open-access research papers.
• For batch searches of metadata and full texts, you may consider requesting a free API key to use the Core API .
• Search for content, authors, collections, and journals in the advanced search , where you have the option to search by discipline or key word.
• Search for articles in clincial sciences, biochemistry, public health, physical chemistry, and materials engineering.
• Search open-access journals and dissertations. Note that dissertations can vary in quality, since they have not gone through peer review.
• AP Research students should have access to a free EBSCO account from the AP Capstone program.
• Many of the social science articles are free access.
• Search for articles related to to education research.
• The search engine includes the open to search for full-text articles.
• Index of major computer science publications.
• Option to search for open-access articles.
• Search for journal articles, working papers, and conference papers in economics and business.
• You can sign up for a free MyJSTOR account to access up to six articles a month for free.
• This may be helpful for accessing articles that are not open access.

Tips for Accessing Paywalled Articles

• Search for the author’s website. Many researchers have draft manuscripts on their websites or research profiles on sites such as ResearchGate .
• Consult your school’s research librarian for other ways to access the article.
• Send the author an e-mail to request for a digital copy of the article. You should provide context in the e-mail request by including a brief description of your AP Research project and its relevance and connection to the author’s article.

AP Psychology: Understanding Research Methods

In AP Psychology, a deep understanding of research methods is essential for interpreting psychological studies and conducting empirical research. Here's a comprehensive guide to the key research methods studied in AP Psychology:

1. Experimental Research:

   - Objective: Establish cause-and-effect relationships between variables.

   - Design: Random assignment of participants to conditions, manipulation of an independent variable, and measurement of dependent variables.

2. Correlational Research:

   - Objective: Examine relationships between variables without manipulating them.

   - Design: Measure variables to determine the degree and direction of correlation. No manipulation of variables occurs.

3. Descriptive Research:

   - Objective: Observe and describe behavior without manipulating variables.

   - Design: Includes naturalistic observation, case studies, and surveys to gather information about behavior.

4. Longitudinal Studies:

   - Objective: Examine changes in behavior or traits over an extended period.

   - Design: Data collected from the same participants over time to observe developmental changes.

5. Cross-Sectional Studies:

   - Objective: Compare individuals of different ages to assess differences.

   - Design: Data collected from participants of different age groups at a single point in time.

6. Quasi-Experimental Designs:

   - Objective: Investigate cause-and-effect relationships without random assignment.

   - Design: Participants are not randomly assigned to conditions due to ethical or practical reasons.

7. Surveys and Questionnaires:

   - Objective: Gather self-report data on opinions, attitudes, or behaviors.

   - Design: Participants respond to a set of questions, providing quantitative or qualitative data.

8. Naturalistic Observation:

   - Objective: Observe and record behavior in its natural setting.

   - Design: Researchers avoid interfering with the environment, allowing for a more authentic representation of behavior.

9. Case Studies:

   - Objective: In-depth analysis of an individual or small group.

   - Design: Intensive examination of a person's history, behavior, and experiences.

10. Independent and Dependent Variables:

    - Objective: Identify the manipulated and measured aspects in an experiment.

    - Design: The independent variable is manipulated, and the dependent variable is measured to observe the effect.

11. Random Assignment:

    - Objective: Minimize pre-existing differences among participants in different experimental conditions.

    - Design: Participants are randomly assigned to experimental and control groups.

12. Sampling Methods:

    - Objective: Ensure the selected sample is representative of the population.

    - Design: Techniques like random sampling, stratified sampling, or convenience sampling are used.

13. Ethical Considerations:

    - Objective: Ensure the well-being of participants and the integrity of research.

    - Design: Adherence to ethical guidelines, including informed consent, debriefing, and protection from harm.

14. Reliability and Validity:

    - Objective: Assess the consistency and accuracy of measurements.

    - Design: Researchers employ techniques to ensure that data collection methods are reliable and valid.

15. Statistical Analysis:

    - Objective: Draw meaningful conclusions from data.

    - Design: Utilize statistical tests like t-tests, ANOVA, or correlation coefficients to analyze and interpret results.

16. Replication:

    - Objective: Confirm the reliability of study findings.

    - Design: Repeated studies with similar methodologies to ensure the consistency of results.

By mastering these research methods, AP Psychology students can critically evaluate psychological studies, design their own experiments, and contribute to the scientific understanding of behavior and mental processes. Understanding the strengths and limitations of each method is crucial for becoming a proficient consumer and producer of psychological research.

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Tax changes small business owners should be aware of as the tax deadline looms

FILE - A cash register is seen on the front counter at the Alpha Shoe Repair Corp., Feb. 3, 2023, in New York. As Tax Day, April 15, approaches, there are plenty of things small business owners should keep in mind when filing taxes this year. (AP Photo/Mary Altaffer, File)

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As Tax Day approaches, there are plenty of things small business owners should keep in mind when filing taxes this year.

April 15 is still the annual tax deadline for many small businesses although, unlike individuals, small businesses can have varying deadlines depending on the type of company, the state the taxes are filed in, and other factors. Quarterly estimated tax payments are generally required throughout the year. And certain types of small businesses had to file by March 15.

Since business tax filing is complex, most experts recommend small business owners work with a professional tax adviser rather than trying to file on their own or even with tax-filing software.

“Taxes should not be scary, especially when you have a certified tax professional or someone who is your trusted adviser,” said Amber Kellogg, vice president of affiliate origination and management at business consultancy Occams Advisory. “I always say you don’t go to the dentist to get your oil changed, and you certainly shouldn’t do (taxes) yourself unless you’re an expert.”

But even if small business owners aren’t filing taxes themselves, it’s still important to stay informed about any tax changes during the year. Here are things small business owners should consider as the April 15 deadline looms.

Consider an extension

Because of some pending tax legislation in Congress this year, Mitch Gerstein, senior tax adviser at accounting firm Isdaner & Co., said it might be a good idea to file for an extension. When you file an extension you still pay estimated taxes, but final paperwork isn’t due until September.

This gives your tax provider adequate time to file a return. And it’s cheaper to file an extension than an amended return, which costs more in administrative fees.

One reason Gerstein recommends an extension this year: a bonus depreciation write-off used by many small businesses is set to decrease for 2023. The bonus depreciation allowance was designed to spur capital purchases and it let businesses write off 100% of certain new and used assets in 2022. But beginning in 2023, that will decrease to 80% for used assets, dropping another 20% each year thereafter. However, a tax bill pending in Congress could restore the write-off to 100%. It’s rare that there is such a significant tax bill pending in Congress when taxes are due, Gerstein said.

Optimize your retirement plan

The Secure Act 2.0 passed by Congress in late 2022 gives small businesses some tax advantages if they offer a retirement plan. There’s a tax credit for small businesses starting new employee plans. The credit is up to 100% of the startup costs for adopting and maintaining a new 401(k) plan, capped at $5,000. There’s also a tax credit based on employer contribution, up to $1,000 annually per employee, over the plan’s first five years.

Changes in research and development write-offs

Scott Orn, chief operating officer of Kruze Consulting, works with startups backed by venture capital. Orn said the number one concern his clients are calling about is “Section 174,” a part of the tax code that involves writing off research and development costs.

In the past, companies were able to deduct 100% of research and development expenses from their taxable income. That was helpful because often that deduction meant the company was operating at a loss and wouldn’t have to pay taxes.

But starting in 2022 due to new legislation, companies have had to “capitalize” the expense – or spread it out over several years. That means they must now write off the expenses over five years for U.S.-based R&D, or 15 years for foreign R&D expenses.

Large and small companies alike are affected by the change, but small businesses are hurt the most, Orn said.

“(Small businesses) are the ones who are swinging into profit where they thought they were like safely losing money and not ever going to pay taxes for a while,” Orn said. “And that’s why it’s such a big surprise for them. It’s hurting people, it’s like it’s a lot of money these companies don’t have.”

Avoid underpayment penalties

Yet another reason for small business owners to use a tax professional is the fact that underpaying will cost more this year. In the past, underpayment penalties hovered at around 3%, but this year they’re more than double at 8% . That’s because the penalties are based on the federal short term interest rate plus three points, said Danny Castro, Florida Market Tax Leader at BDO USA, part of BDO Global, a global accounting network.

“The cost of underpayment is as high as it’s been in a long time,” he said.

One credit to skip: the ERC

At one time, the pandemic-era Employee Retention Credit seemed like a boon for small businesses. Designed to help small businesses keep employees during pandemic-era shutdowns, the generous credit let businesses file amended tax returns to claim the credit.

But that led to a cottage industry of scammers trying to entice small businesses to help them file for the credit – for a fee – even if they didn’t qualify. The IRS has launched several initiatives to claw back some money improperly given to businesses. To date, the IRS said 500 taxpayers have given back $225 million via a voluntary disclosure program, which ended on March 22, that let small businesses who thought they received the credit in error give back the money and keep 20%. And 1,800 businesses have withdrawn unprocessed claims totaling $251 million.

Get organized, stay organized

The best thing small businesses can do to help their tax advisers file their taxes is stay organized. A shoe box full of receipts isn’t helpful when trying to file timely taxes. Owners should log receipts in an orderly database they can turn over to their adviser. And stay on top of quarterly estimated payments.

“(Small business owners) need to be able to keep accurate records throughout the year and not have to go back in April and go, gosh, what what was this receipt for,” said Occams Advisory’s Amber Kellogg, “Keeping those, accurate records is very, very important.”

This story has been corrected to show that BDO USA is part of BDO Global, not BBO Global.

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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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Metrics details

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

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

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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

Philippe Malcorps & Luk Daenen

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Contributions

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

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Correspondence to Kevin J. Verstrepen .

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

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

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Academic Paper: Literature Review

7 min read • march 13, 2023

Dylan Black

Introduction

Your Literature Review is the first formal part of your paper. It is the section that is the most detached from your actual research project because you most likely will not bring up your actual research question/topic until the end when you situate your gap in the body of knowledge relating to your topic of inquiry.

The point of your literature review is to explain what is already known about your specific topic of inquiry and establish that the gap you have chosen is truly a gap in the body of knowledge. Coming out of your introduction, your reader will be aware of the broader context of your work and the general idea of what it is you'll be talking about. The literature review takes that context and blows it up into what will eventually become approximately the first 1200-1500 words of your paper (though word count varies across discipline and paper format).

How to Write a Lit Review

The goals of your lit review.

The first goal of your literature review is to, as the name implies, conduct an extensive review of the literature through little-r research. This involves more than just listing the sources and their content because you will be explaining connections between them whether they be agreements in data, disagreements in conclusions, etc.

The biggest mistake students make in their Lit Review - and we'll discuss this in more detail in a later section - is that they just list what sources say and do little to analyze what is actually known about a certain subject. This leads to a weak review of the literature and a murky, undeveloped gap. By connecting your sources, you are able to incorporate multiple perspectives, establish credibility, and at the end of the day, simply have a better reading lit review that truly explains the body of knowledge surrounding your topic.

The second goal of your literature review is to establish your gap and explain how:

it hasn't been covered by researchers before (this is justified through the fact that you just explained what is already known!)

why it is relevant to your discipline/topic

This part of your lit review is crucial to your research paper. This part of your paper connects your literature review - all of the known stuff - to what you are doing. This goal basically addresses the question, "ok so we just looked at aaaalllll of these sources, now what are you going to do", answering it with something along the lines of "well, we see that these sources have established [something] about my discipline, but no one has addressed [your gap], so I plan on researching it."

Using phrases like "This paper provides new insight on..." and "However, [researchers, be specific!] have overlooked [your research gap]" can make this especially clear, which is important because your gap is what lays the foundation of everything to come!

Clearing the Gap , Image From GIPHY

Varying Perspectives

An especially important part of your literature review is establishing and explaining, as the CollegeBoard puts it, "relevant scholarly works of varying perspectives". This means that your lit review cannot just be a bunch of summaries of random papers strewn across a page at random, despite the content being somewhat similar.

When writing, and more specifically when planning your lit review, understanding the perspectives of each of your papers, and being able to connect them in a way that they join hands , or naturally lead into one another in a logical chain of succession is invaluable.

To use myself as an example, in my literature review I focused on three sections: "Marxism and Labor", a section about the economic foundations of my topic, "Applications of Marxism to Film", in which I described how the previous section connects to film in a broader sense, and finally "Marxist Film Theory and Moon", which took the previous two sections and explicitly connected them to my film of interest. In building my Lit Review throughout these 3 sections, not only was I able to split up topics in a meaningful way, but each section built on itself, connecting to points made by previously mentioned sources.

Connecting Your Sources To Your Topic of Inquiry

When writing your literature review, every source you mention at some point or another will have to connect to your overall topic of inquiry, otherwise what's the point? Why would they be used? Thus, when you include a source, not only do you want to explain the content and what the source actually says in terms of the literature but you want to be explicit in explaining the relevancy and credibility of the source in connection to the content of your paper.

This will help you better connect your sources to each other. This is important because the quality of your literature review hinges on how it presents the known information to your readers and helps them understand why your project A) is connected to these sources and B) is a new addition to them.

When you're explicitly building on sources to connect them to the topic of your paper, you more effectively accomplish goal A because your reader doesn't have to do much guesswork. Then, when you move onto your gap, the fact that all of your sources are established as relevant will help you effectively justify your gap.

A sample structure of a literature review. Image from the University of Hull

What Not To Do: The Source Spaghetti

We've mentioned this a few times throughout this guide, but it needs repeating. There is one thing, and one thing only, that you should never do in your literature review, and that's something called "the source spaghetti". Basically, this is a method of writing a literature review where instead of planning or connecting sources to each other, you essentially list out your sources with a brief summary, quickly moving from source to source with very little rhyme or reason making your lit review muddled and unfocused, like reading a bowl of spaghetti.

While this certainly gets some information on the page, it is not an effective way of writing a literature review for the simple reason that you won't be able to have the level of detail that a good Lit Review requires.

Image from GIPHY

Rubric Points and Logistics

Now that we've gone over some tips and tricks, let's take a look directly at the rubric and see what the CollegeBoard explicitly states are the expectations for a lit review. For reference, the full rubric can be found here , and is the source for all the following rubric screenshots.

Seen above is the row of the AP Research scoring guidelines that best correspond to your literature review. Let's move from a one to a five and see what differentiates each point.

A literature review that earns a one and a two is marked by a single perspective, that is to say, it does not cover a wide variety of sources and does not effectively describe the body of knowledge surrounding a topic of inquiry. Papers that earn a one for the literature review typically use non-scholarly works as opposed to scholarly works in their research. For example, news sources do not count as scholarly, whereas research papers that have been peer-reviewed (that bit is important) do.

Moving into the three-point category, we see that a paper with this score does have varying perspectives and scholarly works, but lacks in the connections department. A paper with this score may have done some solid little-r research, but did not structure their lit review in such a way that the sources were able to be in discussion with one another. This is why a paper that uses a spaghetti sources way of organization will not be successful. Remember that connections between your sources are just as crucial as the content of the sources themselves.

The four and five categories meld into one. In fact, the text in the rubric is exactly the same. What separates a four and a five is a degree of sophistication in your writing. Where a 4 may hit all the points and do it well, a five does that and goes above and beyond. It's a very fine line and frankly, it's pretty subjective on the part of the reader. The easiest way to figure out what separates these two is to read sample fours and sample fives to see the difference for yourself.

Final Thoughts

Congratulations! If you've made it this far you have all the skills to get started on your literature review. Your next steps from here are to get started on your little-r research, start collecting sources, and get connecting. You got this!

Key Terms to Review ( 3 )

Non-scholarly works

Peer Review

Research papers

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