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How to present a research paper in PPT: best practices

A research paper presentation is frequently used at conferences and other events where you have a chance to share the results of your research and receive feedback from colleagues. Although it may appear as simple as summarizing the findings, successful examples of research paper presentations show that there is a little bit more to it.

In this article, we’ll walk you through the basic outline and steps to create a good research paper presentation. We’ll also explain what to include and what not to include in your presentation of research paper and share some of the most effective tips you can use to take your slides to the next level.

Research paper PowerPoint presentation outline

Creating a PowerPoint presentation for a research paper involves organizing and summarizing your key findings, methodology, and conclusions in a way that encourages your audience to interact with your work and share their interest in it with others. Here’s a basic research paper outline PowerPoint you can follow:

1. Title (1 slide)

Typically, your title slide should contain the following information:

• Title of the research paper
• Affiliation or institution
• Date of presentation

2. Introduction (1-3 slides)

On this slide of your presentation, briefly introduce the research topic and its significance and state the research question or objective.

3. Research questions or hypothesis (1 slide)

This slide should emphasize the objectives of your research or present the hypothesis.

4. Literature review (1 slide)

Your literature review has to provide context for your research by summarizing relevant literature. Additionally, it should highlight gaps or areas where your research contributes.

5. Methodology and data collection (1-2 slides)

This slide of your research paper PowerPoint has to explain the research design, methods, and procedures. It must also Include details about participants, materials, and data collection and emphasize special equipment you have used in your work.

6. Results (3-5 slides)

On this slide, you must present the results of your data analysis and discuss any trends, patterns, or significant findings. Moreover, you should use charts, graphs, and tables to illustrate data and highlight something novel in your results (if applicable).

7. Conclusion (1 slide)

Your conclusion slide has to summarize the main findings and their implications, as well as discuss the broader impact of your research. Usually, a single statement is enough.

8. Recommendations (1 slide)

If applicable, provide recommendations for future research or actions on this slide.

9. References (1-2 slides)

The references slide is where you list all the sources cited in your research paper.

10. Acknowledgments (1 slide)

On this presentation slide, acknowledge any individuals, organizations, or funding sources that contributed to your research.

11. Appendix (1 slide)

If applicable, include any supplementary materials, such as additional data or detailed charts, in your appendix slide.

The above outline is just a general guideline, so make sure to adjust it based on your specific research paper and the time allotted for the presentation.

Steps to creating a memorable research paper presentation

Creating a PowerPoint presentation for a research paper involves several critical steps needed to convey your findings and engage your audience effectively, and these steps are as follows:

Step 1. Understand your audience:

• Identify the audience for your presentation.
• Tailor your content and level of detail to match the audience’s background and knowledge.

Step 2. Define your key messages:

• Clearly articulate the main messages or findings of your research.
• Identify the key points you want your audience to remember.

Step 3. Design your research paper PPT presentation:

• Use a clean and professional design that complements your research topic.
• Choose readable fonts, consistent formatting, and a limited color palette.
• Opt for PowerPoint presentation services if slide design is not your strong side.

Step 4. Put content on slides:

• Follow the outline above to structure your presentation effectively; include key sections and topics.
• Organize your content logically, following the flow of your research paper.

Step 5. Final check:

• Proofread your slides for typos, errors, and inconsistencies.
• Ensure all visuals are clear, high-quality, and properly labeled.

Step 6. Save and share:

• Save your presentation and ensure compatibility with the equipment you’ll be using.
• If necessary, share a copy of your presentation with the audience.

By following these steps, you can create a well-organized and visually appealing research paper presentation PowerPoint that effectively conveys your research findings to the audience.

What to include and what not to include in your presentation

In addition to the must-know PowerPoint presentation recommendations, which we’ll cover later in this article, consider the following do’s and don’ts when you’re putting together your research paper presentation:

• Focus on the topic.
• Be brief and to the point.
• Attract the audience’s attention and highlight interesting details.
• Use only relevant visuals (maps, charts, pictures, graphs, etc.).
• Use numbers and bullet points to structure the content.
• Make clear statements regarding the essence and results of your research.

Don’ts:

• Don’t write down the whole outline of your paper and nothing else.
• Don’t put long, full sentences on your slides; split them into smaller ones.
• Don’t use distracting patterns, colors, pictures, and other visuals on your slides; the simpler, the better.
• Don’t use too complicated graphs or charts; only the ones that are easy to understand.
• Now that we’ve discussed the basics, let’s move on to the top tips for making a powerful presentation of your research paper.

8 tips on how to make research paper presentation that achieves its goals

You’ve probably been to a presentation where the presenter reads word for word from their PowerPoint outline. Or where the presentation is cluttered, chaotic, or contains too much data. The simple tips below will help you summarize a 10 to 15-page paper for a 15 to 20-minute talk and succeed, so read on!

Tip #1: Less is more

You want to provide enough information to make your audience want to know more. Including details but not too many and avoiding technical jargon, formulas, and long sentences are always good ways to achieve this.

Tip #2: Be professional

Avoid using too many colors, font changes, distracting backgrounds, animations, etc. Bullet points with a few words to highlight the important information are preferable to lengthy paragraphs. Additionally, include slide numbers on all PowerPoint slides except for the title slide, and make sure it is followed by a table of contents, offering a brief overview of the entire research paper.

Tip #3: Strive for balance

PowerPoint slides have limited space, so use it carefully. Typically, one to two points per slide or 5 lines for 5 words in a sentence are enough to present your ideas.

Tip #4: Use proper fonts and text size

The font you use should be easy to read and consistent throughout the slides. You can go with Arial, Times New Roman, Calibri, or a combination of these three. An ideal text size is 32 points, while a heading size is 44.

Tip #5: Concentrate on the visual side

A PowerPoint presentation is one of the best tools for presenting information visually. Use graphs instead of tables and topic-relevant illustrations instead of walls of text. Keep your visuals as clean and professional as the content of your presentation.

Tip #6: Practice your delivery

Always go through your presentation when you’re done to ensure a smooth and confident delivery and time yourself to stay within the allotted limit.

Tip #7: Get ready for questions

Anticipate potential questions from your audience and prepare thoughtful responses. Also, be ready to engage in discussions about your research.

Tip #8: Don’t be afraid to utilize professional help

If the mere thought of designing a presentation overwhelms you or you’re pressed for time, consider leveraging professional PowerPoint redesign services . A dedicated design team can transform your content or old presentation into effective slides, ensuring your message is communicated clearly and captivates your audience. This way, you can focus on refining your delivery and preparing for the presentation.

Lastly, remember that even experienced presenters get nervous before delivering research paper PowerPoint presentations in front of the audience. You cannot know everything; some things can be beyond your control, which is completely fine. You are at the event not only to share what you know but also to learn from others. So, no matter what, dress appropriately, look straight into the audience’s eyes, try to speak and move naturally, present your information enthusiastically, and have fun!

If you need help with slide design, get in touch with our dedicated design team and let qualified professionals turn your research findings into a visually appealing, polished presentation that leaves a lasting impression on your audience. Our experienced designers specialize in creating engaging layouts, incorporating compelling graphics, and ensuring a cohesive visual narrative that complements content on any subject.

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How to Make a Successful Research Presentation

Turning a research paper into a visual presentation is difficult; there are pitfalls, and navigating the path to a brief, informative presentation takes time and practice. As a TA for  GEO/WRI 201: Methods in Data Analysis & Scientific Writing this past fall, I saw how this process works from an instructor’s standpoint. I’ve presented my own research before, but helping others present theirs taught me a bit more about the process. Here are some tips I learned that may help you with your next research presentation:

More is more

In general, your presentation will always benefit from more practice, more feedback, and more revision. By practicing in front of friends, you can get comfortable with presenting your work while receiving feedback. It is hard to know how to revise your presentation if you never practice. If you are presenting to a general audience, getting feedback from someone outside of your discipline is crucial. Terms and ideas that seem intuitive to you may be completely foreign to someone else, and your well-crafted presentation could fall flat.

Less is more

Limit the scope of your presentation, the number of slides, and the text on each slide. In my experience, text works well for organizing slides, orienting the audience to key terms, and annotating important figures–not for explaining complex ideas. Having fewer slides is usually better as well. In general, about one slide per minute of presentation is an appropriate budget. Too many slides is usually a sign that your topic is too broad.

Limit the scope of your presentation

Don’t present your paper. Presentations are usually around 10 min long. You will not have time to explain all of the research you did in a semester (or a year!) in such a short span of time. Instead, focus on the highlight(s). Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

You will not have time to explain all of the research you did. Instead, focus on the highlights. Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

Craft a compelling research narrative

After identifying the focused research question, walk your audience through your research as if it were a story. Presentations with strong narrative arcs are clear, captivating, and compelling.

• Introduction (exposition — rising action)

Orient the audience and draw them in by demonstrating the relevance and importance of your research story with strong global motive. Provide them with the necessary vocabulary and background knowledge to understand the plot of your story. Introduce the key studies (characters) relevant in your story and build tension and conflict with scholarly and data motive. By the end of your introduction, your audience should clearly understand your research question and be dying to know how you resolve the tension built through motive.

• Methods (rising action)

The methods section should transition smoothly and logically from the introduction. Beware of presenting your methods in a boring, arc-killing, ‘this is what I did.’ Focus on the details that set your story apart from the stories other people have already told. Keep the audience interested by clearly motivating your decisions based on your original research question or the tension built in your introduction.

• Results (climax)

Less is usually more here. Only present results which are clearly related to the focused research question you are presenting. Make sure you explain the results clearly so that your audience understands what your research found. This is the peak of tension in your narrative arc, so don’t undercut it by quickly clicking through to your discussion.

• Discussion (falling action)

By now your audience should be dying for a satisfying resolution. Here is where you contextualize your results and begin resolving the tension between past research. Be thorough. If you have too many conflicts left unresolved, or you don’t have enough time to present all of the resolutions, you probably need to further narrow the scope of your presentation.

• Conclusion (denouement)

Return back to your initial research question and motive, resolving any final conflicts and tying up loose ends. Leave the audience with a clear resolution of your focus research question, and use unresolved tension to set up potential sequels (i.e. further research).

Use your medium to enhance the narrative

Visual presentations should be dominated by clear, intentional graphics. Subtle animation in key moments (usually during the results or discussion) can add drama to the narrative arc and make conflict resolutions more satisfying. You are narrating a story written in images, videos, cartoons, and graphs. While your paper is mostly text, with graphics to highlight crucial points, your slides should be the opposite. Adapting to the new medium may require you to create or acquire far more graphics than you included in your paper, but it is necessary to create an engaging presentation.

The most important thing you can do for your presentation is to practice and revise. Bother your friends, your roommates, TAs–anybody who will sit down and listen to your work. Beyond that, think about presentations you have found compelling and try to incorporate some of those elements into your own. Remember you want your work to be comprehensible; you aren’t creating experts in 10 minutes. Above all, try to stay passionate about what you did and why. You put the time in, so show your audience that it’s worth it.

For more insight into research presentations, check out these past PCUR posts written by Emma and Ellie .

— Alec Getraer, Natural Sciences Correspondent

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How to Make a PowerPoint Presentation of Your Research Paper

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Table of Contents

A research paper presentation is often used at conferences and in other settings where you have an opportunity to share your research, and get feedback from your colleagues. Although it may seem as simple as summarizing your research and sharing your knowledge, successful research paper PowerPoint presentation examples show us that there’s a little bit more than that involved.

In this article, we’ll highlight how to make a PowerPoint presentation from a research paper, and what to include (as well as what NOT to include). We’ll also touch on how to present a research paper at a conference.

Purpose of a Research Paper Presentation

The purpose of presenting your paper at a conference or forum is different from the purpose of conducting your research and writing up your paper. In this setting, you want to highlight your work instead of including every detail of your research. Likewise, a presentation is an excellent opportunity to get direct feedback from your colleagues in the field. But, perhaps the main reason for presenting your research is to spark interest in your work, and entice the audience to read your research paper.

So, yes, your presentation should summarize your work, but it needs to do so in a way that encourages your audience to seek out your work, and share their interest in your work with others. It’s not enough just to present your research dryly, to get information out there. More important is to encourage engagement with you, your research, and your work.

Tips for Creating Your Research Paper Presentation

In addition to basic PowerPoint presentation recommendations, which we’ll cover later in this article, think about the following when you’re putting together your research paper presentation:

• Know your audience : First and foremost, who are you presenting to? Students? Experts in your field? Potential funders? Non-experts? The truth is that your audience will probably have a bit of a mix of all of the above. So, make sure you keep that in mind as you prepare your presentation.

Know more about: Discover the Target Audience .

• Your audience is human : In other words, they may be tired, they might be wondering why they’re there, and they will, at some point, be tuning out. So, take steps to help them stay interested in your presentation. You can do that by utilizing effective visuals, summarize your conclusions early, and keep your research easy to understand.
• Running outline : It’s not IF your audience will drift off, or get lost…it’s WHEN. Keep a running outline, either within the presentation or via a handout. Use visual and verbal clues to highlight where you are in the presentation.
• Where does your research fit in? You should know of work related to your research, but you don’t have to cite every example. In addition, keep references in your presentation to the end, or in the handout. Your audience is there to hear about your work.
• Plan B : Anticipate possible questions for your presentation, and prepare slides that answer those specific questions in more detail, but have them at the END of your presentation. You can then jump to them, IF needed.

What Makes a PowerPoint Presentation Effective?

You’ve probably attended a presentation where the presenter reads off of their PowerPoint outline, word for word. Or where the presentation is busy, disorganized, or includes too much information. Here are some simple tips for creating an effective PowerPoint Presentation.

• Less is more: You want to give enough information to make your audience want to read your paper. So include details, but not too many, and avoid too many formulas and technical jargon.
• Clean and professional : Avoid excessive colors, distracting backgrounds, font changes, animations, and too many words. Instead of whole paragraphs, bullet points with just a few words to summarize and highlight are best.
• Know your real-estate : Each slide has a limited amount of space. Use it wisely. Typically one, no more than two points per slide. Balance each slide visually. Utilize illustrations when needed; not extraneously.
• Keep things visual : Remember, a PowerPoint presentation is a powerful tool to present things visually. Use visual graphs over tables and scientific illustrations over long text. Keep your visuals clean and professional, just like any text you include in your presentation.

Know more about our Scientific Illustrations Services .

Another key to an effective presentation is to practice, practice, and then practice some more. When you’re done with your PowerPoint, go through it with friends and colleagues to see if you need to add (or delete excessive) information. Double and triple check for typos and errors. Know the presentation inside and out, so when you’re in front of your audience, you’ll feel confident and comfortable.

How to Present a Research Paper

If your PowerPoint presentation is solid, and you’ve practiced your presentation, that’s half the battle. Follow the basic advice to keep your audience engaged and interested by making eye contact, encouraging questions, and presenting your information with enthusiasm.

We encourage you to read our articles on how to present a scientific journal article and tips on giving good scientific presentations .

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Writing an Abstract

Oral presentation, compiling a powerpoint.

Abstract : a short statement that describes a longer work.

• Indicate the subject.
• Describe the purpose of the investigation.
• Briefly discuss the method used.
• Make a statement about the result.

Oral presentations usually introduce a discussion of a topic or research paper. A good oral presentation is focused, concise, and interesting in order to trigger a discussion.

• Be well prepared; write a detailed outline.
• Introduce the subject.
• Talk about the sources and the method.
• Indicate if there are conflicting views about the subject (conflicting views trigger discussion).
• Make a statement about your new results (if this is your research paper).
• Use visual aids or handouts if appropriate.

An effective PowerPoint presentation is just an aid to the presentation, not the presentation itself .

• Be brief and concise.
• Focus on the subject.
• Attract attention; indicate interesting details.
• If possible, use relevant visual illustrations (pictures, maps, charts graphs, etc.).
• Use bullet points or numbers to structure the text.
• Make clear statements about the essence/results of the topic/research.
• Don't write down the whole outline of your paper and nothing else.
• Don't write long full sentences on the slides.
• Don't use distracting colors, patterns, pictures, decorations on the slides.
• Don't use too complicated charts, graphs; only those that are relatively easy to understand.
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How to Write a Research Paper | A Beginner's Guide

A research paper is a piece of academic writing that provides analysis, interpretation, and argument based on in-depth independent research.

Research papers are similar to academic essays , but they are usually longer and more detailed assignments, designed to assess not only your writing skills but also your skills in scholarly research. Writing a research paper requires you to demonstrate a strong knowledge of your topic, engage with a variety of sources, and make an original contribution to the debate.

This step-by-step guide takes you through the entire writing process, from understanding your assignment to proofreading your final draft.

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Table of contents

Understand the assignment, choose a research paper topic, conduct preliminary research, develop a thesis statement, create a research paper outline, write a first draft of the research paper, write the introduction, write a compelling body of text, write the conclusion, the second draft, the revision process, research paper checklist, free lecture slides.

Completing a research paper successfully means accomplishing the specific tasks set out for you. Before you start, make sure you thoroughly understanding the assignment task sheet:

• Read it carefully, looking for anything confusing you might need to clarify with your professor.
• Identify the assignment goal, deadline, length specifications, formatting, and submission method.
• Make a bulleted list of the key points, then go back and cross completed items off as you’re writing.

Carefully consider your timeframe and word limit: be realistic, and plan enough time to research, write, and edit.

Prevent plagiarism. Run a free check.

There are many ways to generate an idea for a research paper, from brainstorming with pen and paper to talking it through with a fellow student or professor.

You can try free writing, which involves taking a broad topic and writing continuously for two or three minutes to identify absolutely anything relevant that could be interesting.

You can also gain inspiration from other research. The discussion or recommendations sections of research papers often include ideas for other specific topics that require further examination.

Once you have a broad subject area, narrow it down to choose a topic that interests you, m eets the criteria of your assignment, and i s possible to research. Aim for ideas that are both original and specific:

• A paper following the chronology of World War II would not be original or specific enough.
• A paper on the experience of Danish citizens living close to the German border during World War II would be specific and could be original enough.

Note any discussions that seem important to the topic, and try to find an issue that you can focus your paper around. Use a variety of sources , including journals, books, and reliable websites, to ensure you do not miss anything glaring.

Do not only verify the ideas you have in mind, but look for sources that contradict your point of view.

• Is there anything people seem to overlook in the sources you research?
• Are there any heated debates you can address?
• Do you have a unique take on your topic?
• Have there been some recent developments that build on the extant research?

In this stage, you might find it helpful to formulate some research questions to help guide you. To write research questions, try to finish the following sentence: “I want to know how/what/why…”

A thesis statement is a statement of your central argument — it establishes the purpose and position of your paper. If you started with a research question, the thesis statement should answer it. It should also show what evidence and reasoning you’ll use to support that answer.

The thesis statement should be concise, contentious, and coherent. That means it should briefly summarize your argument in a sentence or two, make a claim that requires further evidence or analysis, and make a coherent point that relates to every part of the paper.

You will probably revise and refine the thesis statement as you do more research, but it can serve as a guide throughout the writing process. Every paragraph should aim to support and develop this central claim.

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A research paper outline is essentially a list of the key topics, arguments, and evidence you want to include, divided into sections with headings so that you know roughly what the paper will look like before you start writing.

A structure outline can help make the writing process much more efficient, so it’s worth dedicating some time to create one.

Your first draft won’t be perfect — you can polish later on. Your priorities at this stage are as follows:

• Maintaining forward momentum — write now, perfect later.
• Paying attention to clear organization and logical ordering of paragraphs and sentences, which will help when you come to the second draft.
• Expressing your ideas as clearly as possible, so you know what you were trying to say when you come back to the text.

You do not need to start by writing the introduction. Begin where it feels most natural for you — some prefer to finish the most difficult sections first, while others choose to start with the easiest part. If you created an outline, use it as a map while you work.

Do not delete large sections of text. If you begin to dislike something you have written or find it doesn’t quite fit, move it to a different document, but don’t lose it completely — you never know if it might come in useful later.

Paragraph structure

Paragraphs are the basic building blocks of research papers. Each one should focus on a single claim or idea that helps to establish the overall argument or purpose of the paper.

Example paragraph

George Orwell’s 1946 essay “Politics and the English Language” has had an enduring impact on thought about the relationship between politics and language. This impact is particularly obvious in light of the various critical review articles that have recently referenced the essay. For example, consider Mark Falcoff’s 2009 article in The National Review Online, “The Perversion of Language; or, Orwell Revisited,” in which he analyzes several common words (“activist,” “civil-rights leader,” “diversity,” and more). Falcoff’s close analysis of the ambiguity built into political language intentionally mirrors Orwell’s own point-by-point analysis of the political language of his day. Even 63 years after its publication, Orwell’s essay is emulated by contemporary thinkers.

Citing sources

It’s also important to keep track of citations at this stage to avoid accidental plagiarism . Each time you use a source, make sure to take note of where the information came from.

You can use our free citation generators to automatically create citations and save your reference list as you go.

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The research paper introduction should address three questions: What, why, and how? After finishing the introduction, the reader should know what the paper is about, why it is worth reading, and how you’ll build your arguments.

What? Be specific about the topic of the paper, introduce the background, and define key terms or concepts.

Why? This is the most important, but also the most difficult, part of the introduction. Try to provide brief answers to the following questions: What new material or insight are you offering? What important issues does your essay help define or answer?

How? To let the reader know what to expect from the rest of the paper, the introduction should include a “map” of what will be discussed, briefly presenting the key elements of the paper in chronological order.

The major struggle faced by most writers is how to organize the information presented in the paper, which is one reason an outline is so useful. However, remember that the outline is only a guide and, when writing, you can be flexible with the order in which the information and arguments are presented.

One way to stay on track is to use your thesis statement and topic sentences . Check:

• topic sentences against the thesis statement;
• topic sentences against each other, for similarities and logical ordering;
• and each sentence against the topic sentence of that paragraph.

Be aware of paragraphs that seem to cover the same things. If two paragraphs discuss something similar, they must approach that topic in different ways. Aim to create smooth transitions between sentences, paragraphs, and sections.

The research paper conclusion is designed to help your reader out of the paper’s argument, giving them a sense of finality.

Trace the course of the paper, emphasizing how it all comes together to prove your thesis statement. Give the paper a sense of finality by making sure the reader understands how you’ve settled the issues raised in the introduction.

You might also discuss the more general consequences of the argument, outline what the paper offers to future students of the topic, and suggest any questions the paper’s argument raises but cannot or does not try to answer.

You should not :

• Offer new arguments or essential information
• Take up any more space than necessary
• Begin with stock phrases that signal you are ending the paper (e.g. “In conclusion”)

There are four main considerations when it comes to the second draft.

• Check how your vision of the paper lines up with the first draft and, more importantly, that your paper still answers the assignment.
• Identify any assumptions that might require (more substantial) justification, keeping your reader’s perspective foremost in mind. Remove these points if you cannot substantiate them further.
• Be open to rearranging your ideas. Check whether any sections feel out of place and whether your ideas could be better organized.
• If you find that old ideas do not fit as well as you anticipated, you should cut them out or condense them. You might also find that new and well-suited ideas occurred to you during the writing of the first draft — now is the time to make them part of the paper.

The goal during the revision and proofreading process is to ensure you have completed all the necessary tasks and that the paper is as well-articulated as possible. You can speed up the proofreading process by using the AI proofreader .

Global concerns

• Confirm that your paper completes every task specified in your assignment sheet.
• Check for logical organization and flow of paragraphs.
• Check paragraphs against the introduction and thesis statement.

Fine-grained details

Check the content of each paragraph, making sure that:

• each sentence helps support the topic sentence.
• no unnecessary or irrelevant information is present.
• all technical terms your audience might not know are identified.

Next, think about sentence structure , grammatical errors, and formatting . Check that you have correctly used transition words and phrases to show the connections between your ideas. Look for typos, cut unnecessary words, and check for consistency in aspects such as heading formatting and spellings .

Finally, you need to make sure your paper is correctly formatted according to the rules of the citation style you are using. For example, you might need to include an MLA heading  or create an APA title page .

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Checklist: Research paper

I have followed all instructions in the assignment sheet.

My introduction presents my topic in an engaging way and provides necessary background information.

My introduction presents a clear, focused research problem and/or thesis statement .

My paper is logically organized using paragraphs and (if relevant) section headings .

Each paragraph is clearly focused on one central idea, expressed in a clear topic sentence .

Each paragraph is relevant to my research problem or thesis statement.

I have used appropriate transitions  to clarify the connections between sections, paragraphs, and sentences.

My conclusion provides a concise answer to the research question or emphasizes how the thesis has been supported.

My conclusion shows how my research has contributed to knowledge or understanding of my topic.

My conclusion does not present any new points or information essential to my argument.

I have provided an in-text citation every time I refer to ideas or information from a source.

I have included a reference list at the end of my paper, consistently formatted according to a specific citation style .

I have thoroughly revised my paper and addressed any feedback from my professor or supervisor.

I have followed all formatting guidelines (page numbers, headers, spacing, etc.).

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How to write a research paper?

The presentation addresses different issues relating to writing research papers.

Related Papers

Journal of Universal College of Medical Sciences

bishal joshi

INTRODUCTION A research paper is a part of academic writing where there is a gathering of information from different sources. It is multistep process. Selection of title is the most important part of research writing. The title which is interesting should be chosen for the research purpose. All the related information is gathered and the title for research is synthesized. After thorough understanding and developing the title, the preliminary outline is made which maintains the logical path for its exploration. After preliminary research, proper research work is started with collection of previous resources which is then organized and important points are noted. Then research paper is written by referring to outlines, notes, articles, journals and books. The research paper should be well structured containing core parts like introduction, material and methods, results and disscussion and important additional parts like title, abstract, references.

Kevin O'Donnell

Sher Singh Bhakar

Organisation of Book: The book is organized into two parts. Part one starts with thinking critically about research, explains what is (and isn’t) research, explains how to properly use research in your writing to make your points, introduces a series of writing exercises designed to help students to think about and write effective research papers. Instead of explaining how to write a single “research paper,” The Process of Research Writing part of the book breaks down the research process into many smaller and easier-to manage parts like what is a research paper, starting steps for writing research papers, writing conceptual understanding and review of literature, referencing including various styles of referencing, writing research methodology and results including interpretations, writing implications and limitations of research and what goes into conclusions. Part two contains sample research articles to demonstrate the application of techniques and methods of writing good resear...

Tips for Writing a Research Paper

Rachel M. Laisne

Writing a good research paper involves proper preparation. A small amount of time preparing in advance with your materials will provide you with the organization needed to puttl a good research paper together.

josé António C. Santos , Tourism & Management Studies

Journal of Emerging Technologies and Innovative Research, ISSN-2349-5162

Siva Prathap , M Kamraju , Mohd Akhter Ali

Research papers are highly recognized in scholar fraternity. In addition to that the published research work provides a big weight-age to career growth and helps to get admission in reputed universities. Research Scholars doing Ph.D it has become mandatory to publish their research papers at international level academic Journals or scholarly (Peer Reviewed) journals. And many of the students who wish to apply for further studies in technical courses will discover that relevant published research papers help during admission process.So, in this paper we will highlight and enlist the proven steps to write the research paper for peer-reviewed journals.

guidelines and notes

Jeff Immanuel

Hossein Saiedian

Shendry Rosero

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Top 10 Research Presentation Templates with Examples and Samples

Simran Shekhawat

Research organizes all your thoughts, suggestions, findings and innovations in one area that postulates to determining the future applicability. A crucial part of strategic planning is research. It aids organizations in goal setting, decision-making, and resource allocation. Research allows us to uncover and discover many segments of society by establishing facts and generating data that effectively determine future outcomes and progress.

Here's an ultimate guide to conduct market research! Click to know more!

Research primarily comprises gathering and analysing information about consumer behaviour, industry dynamics, economic conditions, and other elements that affect how markets and businesses behave in the context of understanding market trends. Understanding market trends requires market research, which is likely to be successful. Research can reveal prospective market dangers and difficulties, enabling organizations to create backup plans and decide on market entry or expansion with more excellent knowledge. By understanding market trends, businesses can create marketing and advertising efforts that resonate with their target audience. 

Learn about product market research templates. Click here .

Additionally, it aids in determining the best customer-reach methods. Businesses can better satisfy market demands by customizing their products or services by studying consumer behaviours, preferences, and feedback. Assessing Market Size and Potential research can shed light on a market's size, potential for expansion, and competitive environment. Businesses aiming to expand or enter new markets need to know this information.

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Use this premium PPT template to captivate your audience. Download this well-created template to raise your presenting threshold. Establish your milestones with workflows designed to ease the overburdening of tasks. State clear-cut objectives to specify your aim and deliver a timeline. Use these 58-page PowerPoint slides to launch your product success and deliver a presentation that awakes the audience with your research performance and goals.

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Uncover impacts about the stock markets and analyze company-related specific and general equity design using this ready-made template. Understanding the technicality of maintenance and presentation of stocks and equity research, we at SlideTeam have designed an equity research PowerPoint slide to ease your presentation load. This presentation aims to analyze the target company's financial performance, ratios, and financial model to welcome investment in the company. Provide an extensive company summary, income statement, balance sheet, vertical and horizontal analysis, organization shareholding structure, SWOT analysis, and share price performance throughout history through this template.

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Template 3: IT Services Research and Development Template

Showcase the power of your company's services, expertise achievement and future goals using this PPT template. This PPT slide provides you with a summary, key statistics, targets, and overview of your IT service Company. Allow this template to lay out values mission, categorize solutions, and enlist a range of services provided along with expenditure incurred on Research development. The deck also includes a business model canvas that depicts the company's historical development, global reach, management team, organizational structure, employee breakdown, and ownership structure.

Template 4: Research Proposal Steps PowerPoint Template

If you are looking to learn how to draft a research proposal, this slide is the ultimate fit for a newbie to comprehend about - 'what', 'where', and 'how' of research. Download this slide to learn about the format and structure of the research proposal. Use this template to illustrate the goal of the research proposal. Furthermore, our PPT sample file aids in instructing students on how to write a research proposal. Furthermore, you may quickly persuade the audience about the proposal's limitations, objectives, and research gap.

Template 5: Research Proposal for Thesis Template

Provide a clear idea and concise summary of your research with the help of this premium template. A well-written thesis statement frequently paves the way for discussion and debate. It can be the foundation for academic dialogue, enabling others to interact with and challenge your ideas—essential for developing knowledge across all disciplines. Your thesis statement will determine the depth of your study and conclusion while enabling you to attract your targeted audience.

Template 6: Market Research PowerPoint Template

To understand the trends and techniques of market structure, companies need to be aware of the trends and to enable that, and market research is one such profitable asset to invest in to allow numerous investments from companies across. Use this template to highlight the key drivers of growth that define the ultimate indicators of market trends. Use this PPT slide to solve marketing issues and make company decisions, incorporating polished business analysis PPT visuals. Get this template to connect business operations with your company's strategic goals.

Template 7: Establish Research Objective Template

For an effective and meaningful research, clarity is essential. Deploy this template to facilitate that research objectives should specify the precise goals and targets of the study to assist in limiting its scope. To ensure the study's readability and comprehensibility, SlideTeam has crafted a flowchart template design to help you elucidate the study's objective, providing a basis for measuring and evaluating the success of well-defined research. Define and design your research with the help of this four-stage design pattern.

Template 8:  A Company Research Venn Chart Presentation

Establish relationships between the sets and groups of data while comparing and contrasting the company's research analysis. This template is helpful as it helps to understand the abstract, objectives, limitations, methodologies, research gap, etc., of the research effectively while focusing on postulating future recommendations and suggestions.

Template 9: Sample Research Paper Outline in a One-Pager Summary Presentation

How effortless it is to study a research paper without turning several pages? Grab this PPT template to research any topic and jot down your findings in a simple and concise format. Most importantly, a significant amount of their precious time can now be dedicated to critical tasks, aiding them in accelerating the research process. This incredibly well-curated one-pager template includes information about the introduction, problem, literature review, suggestions, and conclusions.

Template 10: Big Data Analytics Market Research Template

Deploy this template to introduce your company's extensive data analysis to understand the industry landscape, identify objectives, and make informed business decisions. Use this template slide to determine the current market size and growth rate. Consider the variables influencing this expansion, such as the rising volume of data produced and the demand for data-driven insights. Give information about the big data analysis market's prospects for the future. Over the coming few years, forecast growth trajectories, rising technologies, and market dynamics. Recognize the intended client base's demographics. Summarize your research and include suggestions for companies wishing to enter or grow in the big data analysis market.

PS: Provide an extensive statistical analysis for your research with this template. Check out now!

Refine your Research with SlideTeam.

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FAQs on Research Presentation

What is a research presentation.

Research Presentation is a visual representation of an individual or a team's observational findings or invocation in a particular subject.

What are the steps in research presentation?

To effectively convey your research findings to your audience, various phases are involved in creating a research presentation. Whether you're giving a presentation at a conference or a business meeting,

• Define your audience - Identify your audience's interests and level of knowledge. Make sure to adjust your presentation to fit their wants and needs.
• Outline What You Present - Create a clear structure with an introduction, three main ideas, and a conclusion. Choose the most essential points you want your audience to remember.
• Research and Data Collection - Gather and arrange the pertinent information, facts, and proof. Make sure your sources are reliable and current.
• Develop Visuals - To improve understanding, create visual aids like slides, charts, graphs, and photographs. Keep visuals straightforward, clutter-free, and with a distinct visual hierarchy.
• Get Your Audience Active - Take advantage of storytelling, anecdotes, or pertinent instances to draw in your audience. If appropriate, encourage audience participation and questions during the lecture.
• Present your argument - Start with a compelling introduction. Follow your outline while ensuring a logical and obvious flow.
• Keep an open line of communication, communicate clearly, and change your tone and pace. Improve your communication by making gestures and using body language. Respond to comments and questions as they come up or after the presentation.
• Recap and Draw a Conclusion - Summarize the core ideas and principal conclusions. Reiterate the importance of your study and its consequences.

How do you research a topic for a presentation?

To begin with, the idea of research presentation, choosing topics that align with your expertise and knowledge is the first and foremost. After understanding the topic, collect core factual and empirical data for proper understanding. After gauging information, it creates a place for every subtopic that must be introduced.

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Home Free PowerPoint Templates Free Research Paper PowerPoint Template

Download Free Research Paper PowerPoint Template

Demonstrate your research findings in a visually appealing presentation using our Free Research Paper PowerPoint Template. A research paper is a piece of academic writing that provides deep insights, analysis, interpretation, arguments, and results based on independent research. There are various types of research papers, and they can be related to any domain, such as biological sciences, social sciences, literature, psychology, and technology. We have crafted this interactive research paper PowerPoint Template to present the overview of your research outcomes in a powerful visual way. The color scheme, infographic icons, and data charts used in the template simplify the complex concepts and make them understandable to the audience. Users can change the placeholder images and display their relevant images.

Our Free Research Paper PowerPoint Template is crafted using 100% editable PPT elements. Moreover, users can change these features according to their preferences. The template consists of a title slide to display the topic of the presentation. Next, there are creative slides for showing the following attributes of the research paper:

• Introduction: In this slide, experts can briefly introduce their exploration.
• Research Questions/ Hypothesis: Using graphical icons and editable textboxes, professionals can list out their hypotheses in this slide.
• Literature Review/ Theory: Students can utilize four rectangular boxes to write down various theories related to their studies.
• Methods & Data Collection: Using bullet points, individuals can use this slide to discuss the primary and secondary data facts.
• Data Presentation/ Findings: There are five slides in which we have provided data charts, bar graphs, and pie charts where users can showcase the percentages and statistical findings of their research examination.
• Conclusion: In this slide, you can conclude all the points to maximize content retention.

On each slide, there is a vertical colorful bar with the title of the research paper that creates an attractive look for the slide. Students, professors, experts, scientists, and researchers can leverage this template to showcase their analytical findings comprehensively. You can also use this free slide presentation template to educate people about certain consequences for awareness. Furthermore, professionals can use this template at conferences to showcase their research and analytical skills to acquire projects and collaborate with companies. So, if you want to display your research data points uniquely and attractively, download this template and personalize it now!

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Writing a Research Paper

Jan 06, 2020

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Writing a Research Paper. What is a research paper?. definition. A substantial piece of academic writing, usually done as a requirement for a class, in which the author does independent research into a topic and writes a description of the findings of that research.

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Presentation Transcript

What is a research paper?

definition • A substantial piece of academic writing, usually done as a requirement for a class, in which the author does independent research into a topic and writes a description of the findings of that research. • A formal written report that includes research findings and a student's own ideas. • Research papers are all about organizing your ideas in a linear, understandable format. • A research paper is a report summarizing the answers to the research questions you generated from the sources you gathered information from. Then presenting the work in research paper format.

A narrative is • A narrative tells about a personal experience, a research paper give information about a topic, and an argument tried to persuade reader to feel a certain way about a topic. Narrative does not incorporate research, a research paper does and include and argument.

How do I write a research paper?

Step 1. Choose a Topic Choose a topic which interests and challenges you. Your attitude towards the topic may well determine the amount of effort and enthusiasm you put into your research. Focus on a limited aspect - narrow it down from Religion to World Religion to Buddhism. Obtain teacher approval for your topic before embarking on full-scale research. Select a subject you can manage. Avoid subjects that are too technical, learned, or specialized. Avoid topics that have only a very narrow range of source materials. A Research Guide for Research Students http://www.aresearchguide.com/1steps.html

Step 2. Find Information Surf the net • Pay attention to domain name extensions (.edu, .gov, .org) as these tend to be more reliable. Be selective of .com sites. Learn how to evaluate sites critically and to search effectively on the Internet. Check out print materials at the library • Almanacs, atlases, encyclopedias, guides, reports, government publications Read and evaluate, bookmark, print out, photocopy and take notes of relevant information. As you gather your resources, jot down full bibliographical information (author, title, place of publication, publisher, date of publication, page numbers, URLs, creation or modification dates on Web pages and your date of access) on work sheet, printout, or enter the information on your computer. Remember that an article without bibliographical information is useless since you cannot cite its source. A Research Guide for Research Students http://www.aresearchguide.com/1steps.html

Career Information Sources • Finding the Right Career http://www.helpguide.org/life/finding_career.htm • O’Net http://www.onetonline.org/ • Ohio Means Jobs https://ohiomeansjobs.com/omj/ • Occupational Outlook Handbook http://www.bls.gov/oco/ • Career Guide to Industries http://stats.bls.gov/oco/cg/ • Employment Projections http://www.bls.gov/emp/ • Best Careers http://money.usnews.com/money/careers

Step 3. State Your Thesis • Do some critical thinking and write your thesis statement down in one sentence. Your thesis statement is like a declaration of your belief. The main portion of your essay will consist of arguments to support and defend this belief. • With the skills and abilities I exhibit, the medical field is the career path I am interested in pursing. A Research Guide for Research Students http://www.aresearchguide.com/1steps.html

Step 4. Make a Tentative Outline • The purpose of an outline is to help you think through your topic carefully and organize it logically before you start writing. Include an Introduction, a Body, and a Conclusion. Make the first outline tentative. • Introduction Share your thesis and purpose clearly. What is the chief reason for the paper? Explain briefly the major points and why readers should be interested in your topic. • Body Present your arguments to support your thesis statement. Remember the rule of 3 – find three supporting arguments for each position. • Conclusion Restate your thesis, summarize your arguments, and explain why you have come to this particular conclusion. A Research Guide for Research Students http://www.aresearchguide.com/1steps.html

Example of an outline • INTRODUCTION – Brief comment leading into subject matter - Thesis statement on Shakespeare • BODY - Shakespeare's Early Life, Marriage, Works, Later Year • Early life in Stratford • Shakespeare's family • Shakespeare's father • Shakespeare's mother • Shakespeare's marriage • Life of Anne Hathaway • Reference in Shakespeare's Poems • Shakespeare's works • Plays • Tragedies • Hamlet • Romeo and Juliet • Comedies • The Tempest • Much Ado About Nothing • Histories • King John • Richard III • Henry VIII • Sonnets • Other poems • Shakespeare's Later Years • Last two plays • Retired to Stratford • Death • Burial • Epitaph on his tombstone • CONCLUSION • Analytical summary • Shakespeare's early life • Shakespeare's works • Shakespeare's later years • Thesis reworded • Concluding statement A Research Guide for Research Students http://www.aresearchguide.com/1steps.html

Step 5. Organize Your Notes Organize, analyze, synthesize, sort and digest the information gathered to effectively communicate your thoughts, ideas, insights and research finding to others. This is the most important stage in writing a research paper. Include only relevant and understandable information. Make sure you have used your own words and has been carefully noted. Document all ideas borrowed or quotes used to avoid plagiarism. Jot down detailed bibliographical information and it ready for your Works Cited page. A Research Guide for Research Students http://www.aresearchguide.com/1steps.html

What is plagiarism?

plagiarism simply means… A piece of writing that has been copied from someone else and is presented as being your own work; to steal and pass off (the ideas or words of another) as one's own; to use (another's production) without crediting the source; to commit literary theft; to present as new and original idea or product derived from an existing source. In other words, plagiarism is an act of fraud. It involves both stealing someone else's work and lying about it afterward. All of the following are considered plagiarism: • turning in someone else's work as your own • copying words or ideas from someone else without giving credit • failing to put a quotation in quotation marks • giving incorrect information about the source of a quotation • changing words but copying the sentence structure of a source without giving credit • copying so many words or ideas from a source that it makes up the majority of your work, whether you give credit or not Most cases of plagiarism can be avoided, however, by citing sources. Simply acknowledging that certain material has been borrowed, and providing your audience with the information necessary to find that source, is usually enough to prevent plagiarism.

What is MLA style?

definition MLA stands for Modern Language Association, and it is the standard format for research and term papers. MLA format includes specific rules for quoting authors, called citations. It also allows for documenting source authors within the text of your research paper, called parenthetical citations. There is also a specific format for creating a Works Cited page, which is sometimes called a bibliography.  Writers who properly use MLA also build their credibility by demonstrating accountability to their source material. Most importantly, the use of MLA style can protect writers from accusations of plagiarism, which is the purposeful or accidental un-credited use of source material by other writers. If you are asked to use MLA format, be sure to consult the MLA Handbook for Writers of Research Papers (7th edition). MLA Formatting http://owl.english.purdue.edu/owl/section/2/

MLA General Guidelines • Type your paper on a computer and print it out on standard, white 8.5 x 11-inch paper. • Double-space the text of your paper, and use a legible font (e.g. Times New Roman). Whatever font you choose, MLA recommends that the regular and italics type styles contrast enough that they are recognizable one from another. The font size should be 12 pt. • Leave only one space after periods or other punctuation marks. • Set the margins of your document to 1 inch on all sides. • Indent the first line of paragraphs one half-inch from the left margin. MLA recommends that you use the Tab key. • Create a header that numbers all pages consecutively in the upper right-hand corner, one-half inch from the top and flush with the right margin. • Use italics throughout your essay for the titles of longer works and, only when absolutely necessary, providing emphasis. • If you have any endnotes, include them on a separate page before your Works Cited page. Entitle the section Notes (centered, unformatted). MLA Formatting http://owl.english.purdue.edu/owl/section/2/

First Page of Your Paper MLA Format • Do not make a title page for your paper unless specifically requested. • In the upper left-hand corner of the first page, list your name, your instructor's name, the course, and the date. Again, be sure to use double-spaced text. • Double space again and center the title. Do not underline, italicize, or place your title in quotation marks; write the title in Title Case (standard capitalization), not in all capital letters. • Use quotation marks and/or italics when referring to other works in your title, just as you would in your text: Fear and Loathing in Las Vegas as Morality Play; Human Weariness in "After Apple Picking" • Double space between the title and the first line of the text. • Create a header in the upper right-hand corner that includes your last name, followed by a space with a page number; number all pages consecutively with Arabic numerals (1, 2, 3, 4, etc.), one-half inch from the top and flush with the right margin. (Note: Your instructor or other readers may ask that you omit last name/page number header on your first page. Always follow instructor guidelines.) MLA Formatting http://owl.english.purdue.edu/owl/section/2/

In-Text Citations: Author-Page Style • MLA format follows the author-page method of in-text citation. This means that the author's last name and the page number(s) from which the quotation or paraphrase is taken must appear in the text, and a complete reference should appear on your Works Cited page. The author's name may appear either in the sentence itself or in parentheses following the quotation or paraphrase, but the page number(s) should always appear in the parentheses, not in the text of your sentence. For example: Wordsworth stated that Romantic poetry was marked by a "spontaneous overflow of powerful feelings" (263). Romantic poetry is characterized by the "spontaneous overflow of powerful feelings" (Wordsworth 263). • Both citations in the examples above, (263) and (Wordsworth 263), tell readers that the information in the sentence can be located on page 263 of a work by an author named Wordsworth. If readers want more information about this source, they can turn to the Works Cited page, where, under the name of Wordsworth, they would find the following information: Wordsworth, William. Lyrical Ballads. London: Oxford U.P., 1967. Print. MLA Formatting http://owl.english.purdue.edu/owl/section/2/

Basic Rules Works Cited Page • Begin your Works Cited page on a separate page at the end of your research paper. It should have the same one-inch margins and last name, page number header as the rest of your paper. • Label the page Works Cited (do not italicize the words Works Cited or put them in quotation marks) and center the words Works Cited at the top of the page. • Double space all citations, but do not skip spaces between entries. • Indent the second and subsequent lines of citations five spaces so that you create a hanging indent. • List page numbers of sources efficiently, when needed. If you refer to a journal article that appeared on pages 225 through 250, list the page numbers on your Works Cited page as 225-50. MLA Formatting http://owl.english.purdue.edu/owl/section/2/

Step 6. Write Your First Draft Summarize, paraphrase or quote directly for each idea you plan to use. Find a technique that suits you. • Use note cards or sheets of lined paper. Mark each card with your outline code (IB2a). Then put all cards in order. • If using a word processor, create filenames that match your outline codes to easily cut and past as you type your paper. A Research Guide for Research Students http://www.aresearchguide.com/1steps.html

Step 7. Revise Your Outline & Draft Read your paper for content errors, check facts, arrange and rearrange ideas to follow your outline. Reorganize outline if necessary. CHECKLIST ONE: 1.Is my thesis statement concise and clear? 2. Did I follow my outline? Did I miss anything? 3. Are my arguments presented in a logical sequence? 4. Are all sources properly cited to ensure that I am not plagiarizing? 5. Have I proved my thesis with strong supporting arguments? 6. Have I made my intentions and points clear in the essay? A Research Guide for Research Students http://www.aresearchguide.com/1steps.html Re-read your paper for grammatical errors. Use a dictionary or a thesaurus as needed. Do a spell check. Correct all errors that you can spot and improve the overall quality of the paper to the best of your ability. Get someone else to read it over. Sometimes a second pair of eyes can see mistakes that you missed. CHECKLIST TWO: 1. Did I begin each paragraph with a proper topic sentence? 2. Have I supported my arguments with documented proof or examples? 3. Any run-on or unfinished sentences? 4. Any unnecessary or repetitious words? 5. Varying lengths of sentences? 6. Does one paragraph or idea flow smoothly into the next? 7. Any spelling or grammatical errors? 8. Quotes accurate in source, spelling, and punctuation? 9. Are all my citations accurate and in correct format? 10. Did I avoid using contractions? Use "cannot" instead of "can't", "do not" instead of "don't"? 11. Did I use third person as much as possible? Avoid using phrases such as "I think", "I guess", "I suppose“ 12. Have I made my points clear and interesting but remained objective? 13. Did I leave a sense of completion for my reader(s) at the end of the paper?

Step 8. Type Final Paper All formal reports or essays should be typewritten and printed. Read the assignment sheet again to be sure that you understand fully what is expected of you, and that your essay meets the requirements as specified by your teacher. Know how your essay will be evaluated, compare to the rubric. Proofread final paper carefully for spelling, punctuation, missing or duplicated words. Make the effort to ensure that your final paper is clean, tidy, neat, and attractive. Aim to have your final paper ready a day or two before the deadline. A Research Guide for Research Students http://www.aresearchguide.com/1steps.html

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CEE Comm Lab helps first-year undergraduates present scientific research

The following is a modified excerpt from the MIT News article, “ First-year MIT students gain hands-on research experience in supportive peer community ” by Callie Ayoub.

During MIT’s Independent Activities Period (IAP) this January, first-year students interested in civil and environmental engineering (CEE) participated in a four-week undergraduate research opportunities program known as the mini-UROP (1.097). The six-unit subject pairs first-year students with a CEE graduate student or postdoc mentor, providing them with an inside look at the research being conducted in the department. The program culminates with a presentation event open to the entire CEE community.

Overall, eight labs in the department opened their doors to the 2024 cohort, who were eager to take advantage of the opportunity to collaborate with current students and build a community around their interests. The interdisciplinary nature of the department’s research offered participants a wide range of projects to work on, from redefining autonomous vehicle deployment to mitigating the effects of drought on crops.

Mini-UROP participant Iraira Rivera Rojas works in the Marelli Lab in CEE.

Throughout the duration of the mini-UROP, participants attended three workshops led by Jared Berezin , the manager of the Civil and Environmental Engineering Communication Lab (CEE Comm Lab). The communication lab is a free resource to undergraduates, graduates, and postdocs in the CEE community, providing one-on-one coaching and interactive workshops. Held on Fridays during IAP, the workshops focused on visual and oral communication strategies to help students contextualize their projects, explain scientific concepts, describe their methodologies, and present their results.

“Students were fortunate to have research mentors in the lab, and my goal was to provide communication mentorship outside of the lab,” says Berezin. “Our weekly workshops focused on scientific communication strategies, but perhaps more importantly I’d prompt them to talk about their projects, ask questions, and brainstorm together. They really embraced the opportunity to foster a supportive peer community, which I think is a core part of the CEE experience.”

Mini-UROP participants present their research to fellow students, staff, and faculty.

A significant challenge students face while completing the program is condensing their research down to a clear and concise two-minute presentation. To assist with this task, the workshops also featured presentations by CEE Communication Fellows Ignacio Arzuaga and Matthew Goss , providing students with a preview of how their own presentations may take shape. Before the final presentation event, students also had the option to meet with Comm Fellows to receive feedback, rehearse their talks, and practice responding to questions about their work.

“The final talks were impressive, and I was proud of the students for approaching both their research and communication challenges with such curiosity and thoughtfulness,” Berezin remarks.

To learn more about the experiences of students and mentors during the 2024 mini-UROP, you can read the full MIT News article .

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• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

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

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

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

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

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Introduction

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

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

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

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

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

Extensive analysis identifies relationships between chemical compounds in beer

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

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

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

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

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

Tasting panel assessments reveal sensorial relationships in beer

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

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

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

Tasting panel assessments of specific flavors correlate with chemical composition

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

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

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

Public consumer reviews complement expert panel data

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

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

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

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

Models can predict beer sensory profiles from chemical data

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Model validation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Beer selection

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

Chemical dataset

Sample preparation.

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

HS-GC-FID/FPD

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

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

HS-SPME-GC-MS

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

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

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

Discrete photometric and enzymatic analysis

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

NIR analyses

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

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

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

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

Online reviews from a public database

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

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

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

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

Beer price collection and processing

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

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

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

Model dissection

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

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

Validation of causal chemical properties

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

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

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

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

Reporting summary

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

Data availability

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

Code availability

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

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Acknowledgements

We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

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

Authors and Affiliations

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

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

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

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

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

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

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

Łukasz Kreft & Alexander Botzki

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

Philippe Malcorps & Luk Daenen

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Contributions

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

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Correspondence to Kevin J. Verstrepen .

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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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Winners of national penmanship contest crowned as handwriting is 'having a moment'

It’s regarded, hands down, as the Super Bowl of penmanship tournaments.

The Zaner-Bloser National Handwriting Contest, now in its 33rd year, crowned its 2024 grand champions on Monday, rewarding nine students from six states for their picture-perfect letters.

Ten-year old Zita Miller of White Bear Lake, Minnesota, took top honors in the fifth grade category. Her winning submission was one of the contest’s 80,000 entries.

“I like handwriting because it’s like art, drawing swirls and vines and curls,” Miller said, adding that she enjoys penning original mystery stories by hand.

Namuun Baasanbold, from Carmel, Indiana, was named grand champion in the first grade category, and said she likes to give handwritten “love notes” to family and friends.

“Writing by hand makes me feel special,” she said.

The contest celebrates a centuries-old practice, but the victories come as handwriting is experiencing a kind of renaissance in the U.S. In January, California became the 22nd state to require cursive to be taught in schools — a significant jump from 2016, when just 12 states mandated it.

At the same time, various studies published over the past decade have detailed how writing with pencil and paper can benefit memory, cognitive development, reading comprehension and fine motor skills.

“Handwriting is definitely having a moment,” said Sharon Quirk-Silva, a member of the California state Assembly who sponsored the bill. She said she heard from people from all over the country who penned “beautifully handwritten notes” of support for the new law.

“We live in a very polarized nation. So many issues are contentious. But with this handwriting bill, we had full bipartisan support and goodwill. The importance of handwriting is something people seem to agree on,” she said.

Quirk-Silva said she backed the bill, in large part, because of her own experience — before becoming a lawmaker, she taught elementary school for 25 years.

“For years, technology has been taking over the curriculum in schools, with many kids being dormant in front of the screen, using two or three screens a day. Now, there’s a feeling of, ‘Let’s get pens and pencils back in kids’ hands,’” she said.

Although the California law mandates that first through sixth graders in the state receive cursive instruction, Quirk-Silva said she believes that writing by hand — in print or cursive — is an important language arts tool.

“It’s a way of slowing down a little bit, taking your thoughts from your brain to your hand and physically doing the writing,” she said.

Sophia Vinci-Booher, an assistant professor of psychology and human development at Vanderbilt University, said her research found that writing by hand enabled preschool students to form connections in the brain that likely support early letter recognition.

For that study , published in 2016, 20 children were asked to practice certain letters by writing them over and over, and practice others by pressing a button.

“Then we asked the children to go into an MRI scanner and look at those letters they’d been practicing,” Vinci-Booher explained. Her team analyzed the children’s brain activity to assess the functional connectivity between different areas of their brains.

“We found that the connection was stronger with letters they wrote by hand than those they tapped,” she said.

The research underscores the importance of the physical act of forming symbols, Vinci-Booher added.

“Writing by hand is a good thing for kids because it supports early reading development and it engages the fine motor system, which is developmentally important,” she said.

A 2021 study measured people’s brain activity during a memory task, this time finding that University of Tokyo students exhibited stronger activity and better recall after they had written information down on paper than when they did on a smartphone or even with a stylus on a tablet. The researchers suggested that the physical act of writing on paper provides the brain more details that trigger memory, and concluded that using paper notebooks can help students retain information in part because of their “tangible permanence.”

A similar study published in January compared the brain activity of students at the Norwegian University of Science and Technology who took notes by hand to the activity of those who typed their notes. The findings suggested that the students who wrote by hand had higher levels of electrical activity across a wide range of brain regions responsible for sensory processing and memory.

The results come as little surprise to many educators.

“I’ve seen firsthand that the kids learn more when they write by hand,” said Geeta Kadakia, who teaches second through fifth grade at the DAV Montessori School in Houston. “The lightbulb goes off through those achievements in handwriting, and handwriting leads to achievements in other areas, even math. When students make their numbers more neatly, their math scores improve.”

Laura Gajderowicz taught elementary school for 33 years in Indiana before retiring in 2022. She said she worried as she watched handwriting take a back seat to technology in U.S. classrooms in the early 2000s.

“Writing by hand does so much to help with the development of a student’s eye-hand coordination,” Gajderowicz said, adding: “I’m not against technology — I just think there’s a place at the table for both technology and handwriting when it comes to learning.”

This year, Gajderowicz served as a regional judge in the Zaner-Bloser contest.

“I was pleasantly surprised to see how many entries we had, especially from children in the upper grades,” she said.

Gajderowicz selected winners using criteria that analyzed the mechanics and precision of the letters students wrote, including their shapes, sizes, slant and spacing.

Contestants were asked to write the sentence, “The quick brown fox jumps over the lazy dog,” because it includes the entire alphabet, as well as a sentence explaining why handwriting makes them a better reader and writer.

Baasanbold said she was “over the moon” to find out she won: “I screamed and celebrated with friends at a restaurant with pizza and an appetizer and a sundae for dessert,” she said.

Her prizes include a trophy and $500 — plus bragging rights.

“I like to use my handwriting to impress people,” she said.

Mary Pflum is a national field producer for NBC News, based in New York.