Royal Society of Chemistry

Journals, books & databases

  • Author & reviewer hub
  • Author guidelines & information

Prepare your article

Top Image

How to write and structure an article for submission

how to write a chemistry research paper

← Previous: Choose the right journal

Next: The submission process →

These guidelines are relevant to all of our journals. Make sure that you check your chosen journal’s web pages for specific guidelines too.

On this page, find out how to prepare an article for publication in a Royal Society of Chemistry journal, and present your research clearly with all relevant information included.

How to write your article

Here you'll find guidance and tips for first-time and experienced authors on writing style and the best way to structure an article.

For help structuring and formatting your whole manuscript, choose one of these article templates .

For detailed information on acceptable formats for your figures, visit our section on  Figures, graphics, images & cover artwork .

For a quick reference checklist to help you prepare a high quality article, download our ‘How to publish’ guide .

I would like to know more about:

  • Microsoft Word templates
  • LaTeX templates
  • Referencing templates: Endnote style files
  • Chemical structure templates
  • Figures & graphics
  • Table of contents entry
  • Photographs
  • Chemical structures
  • Crystal structure images
  • Journal cover artwork
  • Article types
  • Format & layout of your article
  • Introduction
  • Experimental
  • Results & discussion
  • Conclusions
  • Author Contributions (optional)
  • Conflicts of interest
  • Acknowledgements
  • Bibliographic references & notes
  • ChemRxiv: the premier preprint server for authors around the world
  • Experimental reporting requirements for submission
  • Experimental data
  • General guidance
  • Characterisation within chemical biology
  • Presentation of experimental data
  • Guide to the presentation of experimental data
  • Small molecule single crystal data
  • Information for inclusion in the CIF
  • Information for inclusion in the manuscript
  • Powder diffraction data
  • Macromolecular structure & sequence data 
  • System models
  • Electrophoretic gels and blots
  • Human & animal welfare
  • Why is data sharing important?
  • What is research data?
  • Our data sharing policy
  • Choosing a repository
  • Subject specific repositories
  • General repositories
  • Data Availability Statements
  • Citing data
  • Citing software and code
  • Request the service

Other helpful resources

  • Pre-publication guide: preparing your article for maximum impact
  • Article promotion services
  • Researcher Development Grants

← Explore all information and guidelines for authors

This website uses cookies to improve your user experience. By continuing to use the site, you are accepting our use of cookies. Read the ACS privacy policy.

  • ACS Publications

4 Keys to Writing a Highly-Read Chemistry Research Paper

  • Apr 24, 2017

You spent a lot of time on your research, and now it is time to describe those findings to the scientific community. You know your work is important, but how do you make it stand out? To help answer this question, we asked ACS Editors, staff, and 2016’s most-read author to share their thoughts on […]

how to write a chemistry research paper

You spent a lot of time on your research, and now it is time to describe those findings to the scientific community. You know your work is important, but how do you make it stand out? To help answer this question, we asked ACS Editors, staff, and 2016’s most-read author to share their thoughts on the essential elements of a highly-read chemistry research paper. Here are a few tips to make sure your next chemistry research paper attracts the maximum amount of attention.

A Highly-Read Chemistry Research Paper Tells a Story

Your paper should be about more than data points and methods if you want to attract more readers. Your research needs to tell a story that will grab and hold readers’ attention.

“A paper should have a gripping narrative about why you undertook this piece of research. (What were you trying to prove? How is this an advancement over the state of the art?), the setbacks or learning moments you had along the way, and where the data took you in the end,” says Dr. Stefano Tonzani, Executive Editor, ACS Omega .

Telling a good story invests the reader into your research. Once they find themselves interested in how it begins, they will want to learn how it ends.

A Highly-Read Chemistry Research Paper Has a Hook

A timely research topic will go a long way to ensuring your paper is highly read. If your work covers an emerging research topic, such as the Zika virus, or a subject that affects large amounts of people, such as climate change, it’s more likely to attract attention. Other elements that can increase the number of people reading your research can include a human interest angle or a novel method.

Dr. Prashant Kamat , Editor-in-Chief, ACS Energy Letters , suggests a simple fix to help your paper stand out: Having an engaging title. With so many research papers out there, an appealing title is important. “The shorter the title, the better,” he says. He goes on to say, “An important step in writing the paper is to come up with an attractive title that will appeal to a broad readership. The title should be simple, effective, and accurately reflect the content of the paper. If you are submitting a paper to a physical chemistry journal, avoid using phrases such as  Synthesis ,  Device Fabrication , or  Application  in the title as they imply the focus of the paper is highly specialized in nature. You should also avoid descriptive words such as s tudy , i nvestigation , or demonstration because they can undermine the uniqueness of the study. Similarly, avoid adjectives such as  Significant Enhancement ,  Highly Efficient ,  Novel ,  Facile , or  Green  unless you have a major finding that conclusively supports the claim.”

Visual elements are also key to holding a reader’s attention. Dr. Kamat suggests that you have attractive images and figures in your paper to break up the text and make the overall experience visually appealing.

A Highly-Read Chemistry Research Paper is Accessible

If you want to reach a lot of readers, you need to make your work accessible to them. One way to do this is to make your work open access, or free for anyone to read.

“At ACS Energy Letters , 13 out of the 20 most read articles are open access,” says Dr. Kamat, suggesting that accessibility is key to broader dissemination and readership.

Dr. Tonzani agrees that open access is important for a research paper. “In regards to my open access journal, ACS Omega, roughly half of the readers are working in industry or ingesting our research at places other than universities and companies that subscribe to ACS journals. Thus you can have an immediate doubling of the possible audience for your paper.”

At ACS Publications, we offer flexible open access options on all ACS Journals under an ACS AuthorChoice license. With multiple discounts available, including a 50% ACS Membership discount, it’s more affordable than ever to open up your research to the world. Through the end of the year, researchers may also use their ACS Author Rewards to cover the cost of open access fees.

A Highly-Read Chemistry Research Paper Gets Promoted

“It’s important to take the time to help a general audience understand the bigger picture of whatever you do, and reaching out through social and local media sources is imperative,” says Dr. Jacqueline Fries, Formulation Scientist at CoreRx, Inc. and 2016’s Most-Read Author  in an ACS Publications journal.

She and fellow researchers used an extract from a sponge found in Antarctica to create a new chemical that killed 98% of MRSA cells in laboratory tests . They call the chemical “darwinolide.” Dr. Fries says it is important that the public is made aware of this research, which is why they made every effort to use key communication channels to spread the word.

“We did this by doing an AMA on Reddit, posting on Facebook, doing interviews with local media, and eventually being contacted by national media such as Wired Magazine and National Geographic ,” she says.

The public outreach helped attract attention to the work, and allowed her to communicate “that darwinolide is just a small step in the right direction for antibiotics research.” That effort paid off, as it certainly contributed to making her research paper the most-read of 2016.

Are you looking for more helpful tips on publishing highly-read chemistry research paper? Check out our Top 10 Poster Presentation Tips . Or, click through to read tips from ACS Editors on how to master the art of scientific publication.

Want the latest stories delivered to your inbox each month?

Royal Society of Chemistry

A guide to research question writing for undergraduate chemistry education research students

ORCID logo

Welcome to chemistry education research

There is no doubt that there are particular challenges associated with chemistry students taking up a project that brings together familiar aspects of chemistry with aspects of social sciences that are likely unfamiliar. There is a new world of terminology and literature and approaches that may initially seem insurmountable. However, as chemistry students, you bring something unique to the discussion on education: your expertise in chemistry and your experience of being a chemistry student. The combination of discipline speciality and focus on education has given rise to a new genre of education research, known as discipline based education research, or DBER ( NRC, 2012 ). The focus on chemistry, known as chemistry education research , intends to offer insights into issues affecting teaching and learning of chemistry from the perspective of chemistry, and offers enormous insight into factors affecting learning in our discipline. This journal ( ) along with the Journal of Chemical Education published by the American Chemical Society ( and Chemistry Teacher International published for IUPAC ( focus on discipline specific issues relating to chemistry education, and their prominence in being associated with major societies in chemistry indicates the high status chemistry education and chemistry education research has attained with the family of chemistry sub-disciplines.

In an attempt to help students new to chemistry education research take some first steps in their research work, this editorial focuses on the important early stage of immersing in project work: deciding what it is you want to research. Other sources of information relating to project work include the associated editorials in this journal describing more fully other parts of conducting research ( Seery et al. , 2019 ), as well as thinking about how theses published as part of university studies compare to education research publications ( Lawrie et al. , 2020 ). These editorials should be useful to students in the planning and writing stages of their research work respectively and, like all articles published in this journal, are free to access. Guidance on completing a literature review in chemistry education research is available online ( Seery, 2017 ).

What do you want to find out? Defining your research question

The “good” news is that this initial experience is very common. The task at the beginning stage of your first project is to determine what general area you would like to research, and narrow this down iteratively until you decide on a particular question you would like to answer. We will go through this process below, but an important thing to keep in mind at this stage is that work on your first project is both about the research you will do and also what you learn about doing research. Choosing a topic of interest is important for your own motivation. But regardless of the topic, doing a project in this field will involve lots of learning about the research processes and this research field. These associated skills and knowledge will likely be of most benefit to you after you complete your dissertation and go on into a future career and further studies.

Deciding on your research topic

Choosing what you want to work on when you are not quite sure of the menu to select from is very difficult. Start by writing down what kinds of things interest you that could form general topics of study. You could structure these using the following prompts:

• What from your own learning experience was satisfactory or unsatisfactory? When did you feel like you really understood something, or when did you feel really lost? Sketch out some thoughts, and discuss with some classmates to see if they had similar experiences. The task is to identify particular topics in chemistry or particular approaches of teaching that emerge, and use those as a basis for narrowing your interest to a specific theme.

• What issues from the media are topical in relation to education? Perhaps there have been changes to assessment approaches in schools, or there is a focus on graduate employability? What issues relating to education are emerging in reaction to the impact of COVID-19? Is there something current that interests you that you would like to focus on?

• Are there societal issues that are important to you? Perhaps you would like to explore the experience or performance of particular groups within education, or look at historical data and research trends. You might wish to explore education policy and subsequent impact in chemistry education.

It is likely that several broad topics will emerge that will be of interest to you. But you only have one year and one project, so you will need to choose one! So before you choose, take a shortlist of about three broad topics that interest you and find out a little more about them. The aim here is to dip your toe in the water of these topics and get a feel for what kinds of things people do, and see which one piques your interest most, and which one has most potential for a meaningful and achievable research project.

To find out a little more, you should engage in preliminary reading. This is not a literature review – the task here is to find one or two recent articles associated with each topic. To achieve this, you could go directly to one of the journal pages linked above and type in some search terms. With each article of interest you retrieve, use the following prompts to guide your reading:

1. The introduction to the article usually sets the context of the research, with some general issues relating to the research in this topic, while the final section of the paper (“limitations” or “conclusions” sections) give some specific detail on what needs further study. Read over these sections: are the issues being discussed of interest to you?

2. The experimental or methods section of the article usually describes the sample used in the study. If you were to research in this area, can you see how questions you are interested in would translate to your setting? While we will discuss scope of research more carefully below, the task here is to put yourself in the moment of doing a research project to think: what would I do? And then ask; does that moment pique your interest?

3. The results and discussion section of the article describes data the researchers report and what they think it means in the wider context of the research area. Again, while the data that you get in your project will depend on what you set out to do, use this reading to see what kind of data is impressing you, and whether you find the discussion of interest.

This kind of “sampling” of the vast literature available is a little ad hoc , but it can be useful to help bring focus on the kinds of research that are feasible and help refine some conversations that you can have with your research supervisor. While embarking on a new project will always have a big “unknown” associated with it, your task is to become as familiar as possible with your chosen topic as you can in advance, so that you are making as informed a decision as possible about your research topic. Once you have – you are ready to continue your research!

From research topic to research question

While we don’t often explicitly state the research question in chemistry research, scientists do have an implicit sense that different questions lean on different areas of theory and require different methods to answer them. We can use some of this basis in translating the context to chemistry education research; namely that the research question and the underpinning theory are clearly interdependent, and the research question we ask will mandate the approaches that we take to answer it.

In fact, in (chemistry) education research, we are very explicit with research questions, and setting out the research question at the start of a study is a major component of the research process ( White, 2008 ). As you will find repeatedly in your project, all the components of a research process are interdependent, so that the research question will determine the methods that will determine the kinds of data you can get, which in turn determine the question you can answer. The research question determines what particular aspect within a general research topic you are going to consider. Blaikie (2000, p. 58) wrote (emphasis in original):

“In my view, formulating research questions is the most critical and, perhaps, the most difficult part of a research design… Establishing research questions makes it possible to select research strategies and methods with confidence. In other words, a research project is built on the foundation of research questions .”

So there is a lot of pressure on research questions! The good news is that while you do need to start writing down your research question near the beginning of the project, it will change during the early stages of scoping out projects when considering feasibility, and as you learn more from reading. It could change as a result of ethical considerations ( Taber, 2014 ). And it will probably change and be fine-tuned as you refine your instruments and embark on your study. So the first time you write out a research question will not be the last. But the act of writing it out, however bluntly at the start, helps set the direction of the project, indicates what methods are likely to be used in the project (those that can help answer the question), and keeps the project focussed when other tempting questions arise and threaten to steer you off-course. So put the kettle on, get out a pen and a lot of paper, and start drafting your first research question!

Defining your research question

To assist your thinking and guide you through this process, an example is used to show how this might happen in practice. In this example, a student has decided that they want to research something related to a general topic of work-experience in chemistry degree programmes. The student had previously completed some work experience in an industrial chemistry laboratory, and knows of peers who have completed it formally as part of their degree programme. The student's experience and anecdotal reports from peers are that this was a very valuable part of their undergraduate studies, and that they felt much more motivated when returning to study in formal teaching at university, as well as having a much clearer idea on their career aspirations after university.

Stage 1: what type of question do you want to answer?

Some foreshadowed questions that might emerge in early stages of this research design might include:

• What kinds of industrial experience options are available to chemistry students?

• What experiences are reported by students on industrial experience?

• Why do some students choose to take up industrial placements?

• How does a students’ perception of their career-related skills change as a result of industrial experience?

• How do students on industrial experience compare to students without such experience?

All of these questions – and you can probably think of many more – are specific to the general topic of industrial experience. But as they stand, they are too broad and need some focussing. To help, we will first think about the general kind of research we want to do ( White, 2008 ).

Types of research

A second broad area of research is explanatory research, which tends to answer questions that start with “how” or “why”. Explanatory research has less of a focus on the subject of the research, and more on the processes the subjects are engaged with, seeking to establish what structures led to observed outcomes so that reasons for them can be elucidated.

A third broad area of research is comparative research, which tends to compare observations or outcomes in two or more different scenarios, using the comparison to identify useful insights into the differences observed. Many people new to education research seek to focus on comparative questions, looking to answer the generic question of is “X” better than “Y”? This is naturally attractive, especially to those with a scientific background, but it is worthwhile being cautious in approaching comparative studies. Even in well-designed research scenarios where research does find that “X” is indeed better than “Y” (and designing those experimental research scenarios is fraught with difficulty in education studies), the question immediately turns to: “but why”? Having richer research about descriptions or explanations associated with one or both of the scenarios is necessary to begin to answer that question.

Let us think again about our foreshadowed questions in the context of general types of question. The aim here is to simply bundle together foreshadowed questions by question type, and using the question type, begin to focus a little more on the particular aspects of interest to us. The intention here is to begin to elaborate on what these general questions would involve in terms of research (beginning to consider feasibility), as well as the kinds of outcomes that might be determined (beginning to consider value of research).

The descriptive questions above could be further explored as follows:

• What kinds of industrial experience options are available to chemistry students? In answering this question, our research might begin to focus on describing the types of industrial experience that are available, their location, their length, placement in the curriculum, and perhaps draw data from a range of universities. In this first iteration, it is clear that this question will provide useful baseline data, but it is unlikely to yield interesting outcomes on its own.

• What experiences are reported by students on industrial experience? In answering this question, we are likely going to focus on interviewing students individually or in groups to find out their experience, guided by whatever particular focus we are interested in, such as questions about motivation, career awareness, learning from placement, etc. This research has the potential to uncover rich narratives informing our understanding of industrial placements from the student perspective.

The explanatory questions above can be further explored as follows:

• How does students’ perception of their career-related skills change as a result of industrial experience? In answering this question, our research would remain focussed on student reports of their experiences, but look at it in the context of their sense of career development, their awareness of development of such skills, or perhaps identifying commonalities that emerge across a cohort of students. This research has the potential to surface such issues and inform the support of career development activities.

• Why do some students choose to take up industrial placements? In answering this question, our research would likely involve finding out more about individual students’ choices. But it is likely to uncover rich seams that can be explored across cohorts – do particular types of students complete placements, or are there any barriers to identify regarding encouraging students to complete placements? “Why” questions tend to throw up a lot of follow-on questions, and their feasibility and scope need to be attended to carefully. But they can offer a lot of insight and power in understanding more deeply issues around particular educational approaches.

The comparative question above can be further explored as follows:

• How do students on industrial experience compare to students without such experience? In answering this question, research might compare educational outcomes or reports of educational experience of students who did and did not complete industrial experience, and draw some inference from that. This type of question is very common among novice researchers, keen to find out whether a particular approach is better or worse, but extreme caution is needed. There may be unobservable issues relating to students who choose particular options that result in other observable measures such as grades, and in uncovering any differences in comparing cohorts, care is needed that an incorrect inference is not made. Handle comparisons with caution!

At this stage, you should pause reading, and dwell on your research topic with the above considerations in mind. Write out some general research areas that have piqued your interest (the foreshadowed questions) and identify them as descriptive, explanatory, or comparative. Use those headline categories to tease out a little more what each question entails: what would research look like, who would it involve, and what information would be obtained (in general terms). From the list of questions you identify, prioritise them in terms of their interest to you. From the exercise above, I think that the “how” question is of most interest to me – I am an educator and therefore am keen to know how we can best support students’ return to studies after being away on placement. I want to know more about difficulties experienced in relation to chemistry concepts during that reimmersion process so that I can make changes and include supports for students. For your research area and your list of foreshadowed questions, you should aim to think about what more focussed topics interest and motivate you, and write out the reason why. This is important; writing it out helps to express your interest and motivation in tangible terms, as well as continuing the process of refining what exactly it is you want to research.

Once you have, we can begin the next stage of writing your research question which involves finding some more context about your research from the literature.

Stage 2: establishing the context for your research

Finding your feet, types of context.

Let's make some of this tangible. In focussing my foreshadowed questions, I have narrowed my interest to considering how students on work experience are aware of their career development, how they acknowledge skills gained, and are able to express that knowledge. Therefore I want to have some theoretical underpinnings to this – what existing work can I lean on that will allow me to further refine my question.

As an example of how reading some literature can help refine the question, consider the notes made about the following two articles.

• A 2017 article that discusses perceived employability among business graduates in an Australian and a UK university, with the latter incorporating work experience ( Jackson and Wilton, 2017 ): this study introduces me to the term “perceived employability”, the extent to which students believe they will be employed after graduation. It highlights the need to consider development of career awareness at the individual level. It discusses the benefits of work experience on perceived employability, although a minimum length is hinted at for this to be effective. It introduces (but does not measure) concepts of self-worth and confidence. Data to inform the paper is collected by a previously published survey instrument. Future work calls for similar studies in other disciplines.

• A 2017 article that discusses undergraduate perceptions of the skills gained from their chemistry degree in a UK university ( Galloway, 2017 ): this study reports on the career relevant skills undergraduate students wished to gain from their degree studies. This study informs us about the extent to which undergraduates are thinking about their career skills, with some comparison between students who were choosing to go on to a chemistry career and those who were considering some other career. It identifies career-related skills students wished to have more of in the chemistry curriculum. Most of the data is collected by a previously published survey. This work helps me locate my general reading in the context of chemistry.

Just considering these two articles and my foreshadowed question, it is possible to clarify the research question a little more. The first article gives some insight into some theoretical issues by introducing a construct of perceived employability – that is something that can be measured (thinking about how something can be measured is called operationalisation). This is related to concepts of self-worth and confidence (something that will seed further reading). Linking this with the second article, we can begin to relate it to chemistry; we can draw on a list of skills that are important to chemistry students (whether or not they intend to pursue chemistry careers), and the perceptions about how they are developed in an undergraduate context. Both articles provide some methodological insights – the use of established surveys to elicit student opinion, and the reporting of career-important skills from the perspective of professional and regulatory bodies for chemistry, as well as chemistry students.

Taking these two readings into account, we might further refine our question. The original foreshadowed question was:

“ How does students’ perception of their career-related skills change as a result of industrial experience? ”

If we wished to draw on the literature just cited, we could refine this to:

“ How does undergraduate chemistry students’ perceived employability and awareness of career-related skills gained change as a result of a year-long industrial placement? ”

This step in focussing is beginning to move the research question development into a phase where particular methods that will answer it begin to emerge. By changing the phrase “perception” to “perceived employability”, we are moving to a particular aspect of perception that could be measured, if we follow methods used in previous studies. We can relate this rather abstract term to the work in chemistry education by also incorporating some consideration of students’ awareness of skills reported to be important for chemistry students. We are also making the details of the study a little more specific; referring to undergraduate chemistry students and the length of the industrial placement. This question then is including:

– The focus of the research: perception of development of career skills.

– The subject of the research: undergraduate chemistry students on placement.

– The data likely to be collected: perceived employment and awareness of career related skills.

It is likely that as more reading is completed, some aspects of this question might change; it may become more refined or more limited in scope. It may change subject from looking at a whole cohort to just one or two individual student journeys. But as the question crystallises, so will the associated methodology and it is important in early readings not to be immediately swayed in one direction or another. Read as broadly as you can, looking at different methods and approaches, and find something that lines up with what it is you want to explore in more detail.

Stage 3: testing your research question

Personal biases.

Whatever we like to tell ourselves, there will always be personal bias. In my own research on learning in laboratories, I have a bias whereby I cannot imagine chemistry programmes without laboratory work ( Seery, 2020 ). If I were to engage in research that examined, for example, the replacement of laboratory work with virtual reality, my personal bias would be that I could not countenance that such an approach could replace the reality of laboratory work. This is a visceral reaction – it is grounded in emotion and personal experience, rather than research, because at the time of writing, little research on this topic exists. Therefore I would need to plan carefully any study that investigated the role of virtual reality in laboratory education to ensure that it was proofed from my own biases, and work hard to ensure that voices or results that challenged my bias were allowed to emerge. The point is that we all have biases, and they need to be openly acknowledged and continually aired. I suggest to my students that they write out their own biases related to their research early in their studies as a useful checkpoint. Any results that come in that agree with the tendency of a bias are scrutinised and challenged in detail. This can be more formally done by writing out a hypothesis, which is essentially a prediction or a preconception of what a finding might be. Hypotheses are just that – they need to be tested against evidence that is powerful enough to confirm or refute them.

Bias can also emerge in research questions. Clearly, our research question written in the format: “why are industrial placements so much better than a year of lecture courses?” is exposing the bias of the author plainly. Biases can be more subtle. Asking leading questions such as “what are the advantages of…” or “what additional benefits are there to…” are not quite as explicitly biased, but there is an implicit suggestion that there will be advantages and benefits. Your research question should not pre-empt the outcome; to do so negates the power of your research. Similarly, asking dichotomous questions (is placement or in-house lecturing best?) implies the assumption that one or the other is “best”, when the reality is that both may have distinct advantages and drawbacks, and a richer approach is to explore what each of those are.

Question scope

Feasibility relates to lots of aspects of the project. In our study on industrial experience, the question asks how something will change, and this immediately implies that we will at least find out what the situation was at the beginning of the placement and at some point during or after the placement. Will that be feasible? Researchers should ask themselves how they will access those they wish to research. This becomes a particular challenge if the intention is to research students based in a different institution. The question should also be reviewed to ensure that it is feasible to achieve an answer with the resources you have to hand. Asking for example, whether doing an industrial placement influences future career choices would be difficult to answer as it would necessitate tracking down a sufficient sample of people who had (and had not) completed placements, and finding a robust way of exploring the influence of placement on their career choice. This might be feasible, but not in the timeframe or with the budget you have assigned to you. Finally, feasibility in terms of what you intend to explore should be considered. In our example research question, we have used the term “perceived employability”, as this is defined and described in previous literature with an instrument that can elicit some value associated with it. Care is needed when writing questions to ensure that you are seeking to find something that can be measured.

Of course researchers will naturally over-extend their research intentions, primarily because that initial motivation they have tapped into will prompt an eagerness to find out as much as possible about their topic of study. One way of addressing this is to write out a list of questions that draw from the main research question, with each one addressing some particular aspect of the research question. For our main research question:

we could envisage some additional related questions:

(a) Are there differences between different types of placement?

(b) Are the observations linked to experience on placement or some other factors?

(c) What career development support did students get during placement?

(d) How did students’ subsequent career plans change as a result of placement?

And the list could go on (and on). Writing out a list of related questions allows you to elaborate on as many aspects of the main question as you can. The task now is to prioritise them. You may find that in prioritising them, one of these questions itself becomes your main question. Or that you will have a main question and a list of subsidiary questions. Subsidiary questions are those which relate to the main question but take a particular focus on some aspect of the research. A good subsidiary question to our main question is question (a), above. This will drill down into the data we collect in the main question and elicit more detail. Care should be taken when identifying subsidiary questions. Firstly, subsidiary questions need to be addressed in full and with the same consideration as the main questions. Research that reports subsidiary question findings that are vague or not fully answered is poor, and undermines the value and power of the findings from the main research questions. If you don’t think you can address it in the scope of your study, it is best to leave it out. Secondly, questions that broaden the scope of the study rather than lead to a deeper focus are not subsidiary questions but rather are ancillary questions. These are effectively new and additional questions to your main research, and it is unlikely that you will have the time or scope to consider them in this iteration. Question (d) is an example of an ancillary question.

Question structure

The length of a research question is the subject of much discussion, and in essence, your question needs to be as long as it needs to be, but no longer. Questions that are too brief will not provide sufficient context for the research, whereas those that are too long will likely confuse the reader as to what it is you are actually looking to do. New researchers tend to write overly long questions, and tactics to address this include thinking about whether the question includes too many aspects. Critiquing my own question, I would point out that I am asking two things in one question – change in perceived employability and change in awareness of career-related skills gained – and if I were to shorten it, I could refer to each of those aspects in subsidiary questions instead. This would clarify that there are two components to the research, and while related, each will have their own data collection requirements and analysis protocols.

Research questions should be written as clearly as possible. While we have mentioned issues relating to language to ensure it is understandable, language issues also need to be considered in our use of terms. Words such as “frequent” or “effective” or “successful” are open to interpretation, and are best avoided, using more specific terms instead ( Kane, 1984 ). The word “significant” in education research has a specific meaning derived from statistical testing, and should only be used in that context. Care is needed when referring to groups of people as well. Researching “working class” students’ experiences on industrial placement is problematic, as the term is vague and can be viewed as emotive. It is better to use terms that can be more easily defined and better reflect a cohort profile (for example, “first generation” refers to students who are the first in their family to attend university) or terms that relate to government classifications, such as particular postcodes assigned a socio-economic status based on income.

As well as clarity with language, research questions should aim to be as precise as possible. Vagueness in research questions relating to what is going to be answered or what the detail of the research is in terms of sample or focus can lead to vagueness in the research itself, as the researcher will not have a clear guide to keep them focussed during the research process. Check that your question and any subsidiary questions are focussed on researching a specific aspect within a defined group for a clear purpose.

Moving on from research question writing

  • Blaikie N., (2000), Designing social research , Oxford: Blackwell.
  • Galloway K. W., (2017), Undergraduate perceptions of value: degree skills and career skills, Chem. Educ. Res. Pract. , 18 (3), 435–440.
  • Jackson D. and Wilton N., (2017), Perceived employability among undergraduates and the importance of career self-management, work experience and individual characteristics, High. Educ. Res. Dev. , 36 (4), 747–762.
  • Kane E., (1984), Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities , London: Marion Boyars.
  • Lawrie G. A., Graulich N., Kahveci A. and Lewis S. E., (2020), Steps towards publishing your thesis or dissertation research: avoiding the pitfalls in turning a treasured tome into a highly-focussed article for CERP, Chem. Educ. Res. Pract. , 21 (3), 694–697.
  • NRC, (2012), Discipline-based education research: Understanding and improving learning in undergraduate science and engineering , National Academies Press.
  • RSC, (2015), Accreditation of Degree Programmes , Cambridge: Royal Society of Chemistry.
  • Seery M. K., (2009), The role of prior knowledge and student aptitude in undergraduate performance in chemistry: a correlation-prediction study, Chem. Educ. Res. Pract. , 10 (3), 227–232.
  • Seery M. K., (2017), How to do a literature review when studying chemistry education. Retrieved from
  • Seery M. K., (2020), Establishing the Laboratory as the Place to Learn How to Do Chemistry, J. Chem. Educ. , 97 (6), 1511–1514.
  • Seery M. K., Kahveci A., Lawrie G. A. and Lewis S. E., (2019), Evaluating articles submitted for publication in Chemistry Education Research and Practice, Chem. Educ. Res. Pract. , 20 , 335–339.
  • Taber K. S., (2014), Ethical considerations of chemistry education research involving ‘human subjects’, Chem. Educ. Res. Pract. , 15 (2), 109–113.
  • White P., (2008), Developing Research Questions: A Guide for Social Scientists , Basingstoke: Palgrave MacMillan.
  • ACS Publications
  • ACS Publishing Center

Scientific Writing

It is critical to understand the important elements of a scientific paper and how to most effectively describe research results in the context of your manuscript. While writing your paper can seem like an afterthought compared to years of work in the lab, the way you convey your findings can have a profound impact on editors, reviewers, and your readers. Here are some resources to help you maximize the potential of your next ACS paper.

Mastering the Art of Scientific Publication

ACS editors are highly experienced researchers full of valuable observations from years of running top journals. We’ve collected a number of their top suggestions about making your research stand out and clearly conveying your findings into a virtual issue entitled "Mastering the Art of Scientific Publication." Visit the issue to learn more about writing cover letters, employing best practices for citations, writing compelling results and methods sections, choosing the best title, and much more.

Before You Write Your Paper

Writing a good paper starts well before you type the first letter. Link your data together to tell a story to your peers (and the world). And remember to start early! Don’t wait until all the data is complete, because your results may take you in a new direction with additional controls or experiments. Find out more with these ACS Author University videos:

How to Write a Scientific Article: Find the Story

How to write a scientific article: when should you start writing your research article, getting started writing your paper.

With your story in mind, the next step is turning that into a written document that is ready to share. In the ACS Author University videos below, ACS editors share their tips for starting with an outline, preparing your figures, and much more, so you can use your time efficiently and produce a top-quality draft.

How Do You Start Writing a Paper? Create an Outline

How do you start writing a paper create the figures, how do you start writing a paper tips from acs editors.

Best of luck with your next paper! We hope you’ll consider submitting to an ACS journal when it’s ready.

how to write a chemistry research paper

1155 Sixteenth Street N.W. Washington, DC 20036


Copyright © 2017 American Chemical Society

  • Journals A–Z
  • C&EN Archives
  • ACS Legacy Archives

User Resources

  • ACS Members
  • Authors & Reviewers
  • Website Demos
  • Privacy Policy
  • Mobile Site
  • For Advertisers
  • Institutional Sales
  • Connect with ACS Publications


Enago Academy

How to Write an Effective Chemistry Research Paper (Part 2)

' src=

In this article, we describe the scientific conventions and writing styles followed in Chemistry papers.

Beginning a Sentence

Avoid starting a sentence with a symbol or numerical value.

✖ 0.5 g of NaOH was added to 5 ml of DW, and the solution was heated.

✔︎ After addition of 0.5 g of NaOH to 5 ml of DW, the solution was heated.

Pedagogical Phrases

Avoid including phrases which address the process of learning and not the science of the experiment.

This experiment helped us learn about…

The goal of this experiment was to learn about…

Although such sentences are preferred in Original Articles, scientific reports/communication should ideally focus only on the data and results.

Illogical Constructions

Check that a modifier phrase or the pronoun “it” actually refers to the intended subject.

To avoid dangling modifiers and unclear antecedents, think about the subject.

✖ Being coated with grease, I cleaned the flask before adding reagents.

Was I coated with grease or the flask?

The flask was coated with grease, and so,

✔︎ Because the flask was coated with grease, it was cleaned before adding reagents.

Personal Pronouns

Because scientific experiments demonstrate facts that do not depend on the observer, reports should avoid using the first and second person (I/we/our/us).

✖ I filtered the solution and noticed production of a yellow powder.

✔︎ Filtration of the solution, yielded a yellow powder.

However, when referring to your own results or conclusions, it is better to use the first or second person.

While AB et al. report X value, the authors’ data indicates Y value.

AB et al. report X value, but our data yield Y value.

Active Voice

When possible, replace passive voice with active voice for clarity.

✖ Passive: There was some solid that did not dissolve.

✔︎ Active: Some solid did not dissolve.


Do not personify compounds and equipments.

✖ The spectrum shows two bands of equal intensity.

✔︎ Two bands of equal intensity appear in the spectrum.

Plural Nouns

Usage of verbs when mentioning amount of chemical reagent and terms like data (singular: datum) and spectra (spectrum) is often confused.

A quantity used is a singular subject, even when that quantity is in a plural form of units.

✖  While the solution boiled, 5.0 g of KBr were added.

✔︎ While the solution boiled, 5.0 g of KBr was added.

Verb Tense and “Verbing” a Noun

Usually the journal guidelines specify the tense to be followed in each section of the manuscript.

Use past tense to describe a procedure:

Hydrochloric acid was added to the flask slowly in order to prevent decomposition of the product.

Use present tense to describe a scientific fact:

Hydrochloric acid is a caustic substance that must be used with caution.

“Verbing” a noun, i.e., turning a noun into a verb makes the sentence unclear and should be avoided.

✖ X complexes to Y

✔︎ X forms complexes with Y

Abbreviations, Formulae, and Numerals

Define abbreviations for chemical compounds or ligands at the first instance. However, standard organic abbreviations (e.g., Me = methyl, Pr = iso -propyl) can be used. Use chemical formulae for standard compounds but not when the name is shorter or more precise.

  • NaOH (aq) for sodium hydroxide
  • Caffeine for C 8 H 10 N 4 O 2

Long compound names can be numbered if repeated many times. The number should be bold or underlined, defined when first presented and appear in parenthesis when used as an adjective.

Investigations into 8-hydroxyquinoline (1) and 4-iodo-8-hydroxyquinoline (2) are described. Recrystallization of 1 and 2 …

Use a leading zero for values less than unity and avoid values with many zero (use scientific notation instead) for decimals.

✖ .15 mm,  ✔︎ 0.15 mm

✖ 0.000024 mM, ✔︎ 2.3 × 10 –4  mM

Chemical Names

The names of chemicals are not capitalized, unless they are trade names (e.g., “Tylenol”).

✖ The reaction of Cobalt (II) was…

✔︎ The reaction of cobalt (II) was…

Terms and Expressions

Use terms like “ synthesizing ” new compounds and “ preparing ” solutions, avoid terms like “products were created .” With/Using/By/On—avoid using these interchangeably, as they might be incorrect in some cases

Spectra are measured “ with / using ” and not “ on ” a spectrometer.

Spectrometers, colorimeters, etc. should be referred to as “instruments” not “machines.” 

The intransitive verb “ react ” is the most used term in chemistry papers. It should not have an object and should not have a passive voice. Chemical reagents react with each other, they are not reacted.

✖ A and B were reacted to produce C and D.

✔︎ The reaction of A and B, potassium hydroxide and hydrochloric acid, produced C and D.

A hypothesis can be “ tested ”; however, for most laboratory work, the terms “measured,” “investigated,” “determined,” “calculated,” and “obtained” are better.

✖ The absorbance of the solution was tested using…

✔︎ The absorbance of the solution was measured using…


Rate this article Cancel Reply

Your email address will not be published.

how to write a chemistry research paper

Enago Academy's Most Popular Articles

Beyond spellcheck- How Copyediting guarantees an error-free submission

  • Reporting Research

Beyond Spellcheck: How copyediting guarantees error-free submission

Submitting a manuscript is a complex and often an emotional experience for researchers. Whether it’s…

  • Old Webinars
  • Webinar Mobile App

How to Find the Right Journal and Fix Your Manuscript Before Submission

Selection of right journal Meets journal standards Plagiarism free manuscripts Rated from reviewer's POV

how to write a chemistry research paper

  • Manuscripts & Grants

Research Aims and Objectives: The dynamic duo for successful research

Picture yourself on a road trip without a destination in mind — driving aimlessly, not…

how to write a chemistry research paper

How Academic Editors Can Enhance the Quality of Your Manuscript

Avoiding desk rejection Detecting language errors Conveying your ideas clearly Following technical requirements

Effective Data Presentation for Submission in Top-tier Journals

Importance of presenting research data effectively How to create tables and figures How to avoid…

Top 4 Guidelines for Health and Clinical Research Report

Top 10 Questions for a Complete Literature Review

how to write a chemistry research paper

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

  • 2000+ blog articles
  • 50+ Webinars
  • 10+ Expert podcasts
  • 50+ Infographics
  • 10+ Checklists
  • Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

how to write a chemistry research paper

What should universities' stance be on AI tools in research and academic writing?

WashU Libraries

Chemistry writing resources, starting a lab report or research paper, general writing style information, parts of research paper or report.

  • Citations and References
  • Return to Main Chemistry Guide

To get started writing a research paper or laboratory report, it is important to consider if you have enough data or enough information to compose a paper.  Additionally, it is also important to consider what you want you want to report and how to report it--clear communication of results is crucial when discussing the experiments. 

This American Chemical Society (ACS) blog post on  How to Write a Research Paper provides some general guidelines to determine when to write a paper and how to get started when it comes to reporting and communicating the results of an experiment or experiments.

Every discipline has a style and format that is used for scholarly communication, and chemistry as a field has a certain format for papers as well as a a style of writing that developed as the field itself grew and information was shared and published.

General Style and Writing Guidelines:

  • Chemistry is always written in the third person, in the past-tense and passive voice. 
  • Pronouns like "I", "We", and "Us" are not typically used
  • Be succinct when describing observations and processes
  • It is not necessary to provide detailed descriptions of standard practices or techniques. 

For information on specific sections that might appear in a scholarly article or laboratory report you may wish to go to the next section in this guide that provides a summary on all the different Parts of A Research Paper and provides links to articles that provide significant detail regarding the style and content for each major section.

Note: While the resources in the guide are meant to help, it is always important to follow the guidelines of the publication or course instructor that you are writing for.

Adapted from information found in Chapter 2 of the ACS Style Guide

Additional resources and information on each sections are also provided from the journal Clinical Chemistry from the section of their journal "Guide To Scientific Writing." Click on the title for a direct link to the PDF or use the corresponding citation for each article to view the online version. All articles are open access articles.

The title should be brief and specific enough to clearly communicate the contents of the paper/research, but should not be overly technical.

  • Clinical Chemistry -Guide to Scientific Writing: The Title Says it All

Thomas M Annesley, The Title Says It All, Clinical Chemistry , Volume 56, Issue 3, 1 March 2010, Pages 357–360,

The byline or list of authors includes all individuals that contributed in a substantial manner to the research being reported.

Generally, the person that did the research is listed as the first author of the paper and names are traditionally formatted as "first name, middle initial, and surname"

The abstract should provide an informative and brief summary of what is written in the paper, and should allow for a reader to quickly understand the nature/purpose of the research, the methods used, the results observed, and any major conclusions that came from the research.

  • Clinical Chemistry -Guide to Scientific Writing: The Abstract and the Elevator Talk: A Tale of Two Summaries

Thomas M Annesley, The Abstract and the Elevator Talk: A Tale of Two Summaries, Clinical Chemistry , Volume 56, Issue 4, 1 April 2010, Pages 521–524,

An introduction puts the experiment or research into context; it should provide background regarding the question or problem being explored and using applicable scientific literature and references help explain why the question being answered or the research being pursued is relevant and/or important.

  • Clinical Chemistry -Guide to Scientific Writing: It was a cold and rainy night”: Set the Scene with a Good Introduction

Thomas M Annesley, “It was a cold and rainy night”: Set the Scene with a Good Introduction, Clinical Chemistry , Volume 56, Issue 5, 1 May 2010, Pages 708–713,

Depending upon the publication or style, this section has many different possible names; chose the correct name for the section based upon the publication to which the research is being submitted or the laboratory report is meant to emulate. 

This section should provide information regarding the techniques used in answering your research question and should say HOW the research question was probed or answered with enough information that another practitioner in the field could reproduce the experiment and results.  In order to accomplish these goals, the experimental section should  identify the materials used and must also provide sufficient details about characterization methods, experimental procedures, or any apparatus used  that is not standard for the field.

  • Clinical Chemistry -Guide to Scientific Writing: Who, What, When, Where, How, and Why: The Ingredients in the Recipe for a Successful Methods Section

Thomas M Annesley, Who, What, When, Where, How, and Why: The Ingredients in the Recipe for a Successful Methods Section, Clinical Chemistry , Volume 56, Issue 6, 1 June 2010, Pages 897–901,

The data collected or the results of the research/experiment are presented and summarized in this section often using graphs, tables, or equations.  When dealing with a large amount of data, the results section provides a summary while additional results or data can be included in a supporting information section. 

It is important to remember that in this section, the results are NOT put into context nor are the results or observations explained. 

  • Clinical Chemistry -Guide to Scientific Writing: Show Your Cards: The Results Section and the Poker Game

Thomas M Annesley, Show Your Cards: The Results Section and the Poker Game, Clinical Chemistry , Volume 56, Issue 7, 1 July 2010, Pages 1066–1070,

  • Clinical Chemistry -Guide to Scientific Writing: If an IRDAM Journal Is What You Choose, Then Sequential Results Are What You Use

              IRDAM = Introduction, Results, Discussion, Methods in terms of order of sections. Many ACS Journals follow this format!

              IMRAD = Introduction, Methods, Results, Discussion in terms of order of sections

Pamela A Derish, Thomas M Annesley, If an IRDAM Journal Is What You Choose, Then Sequential Results Are What You Use, Clinical Chemistry , Volume 56, Issue 8, 1 August 2010, Pages 1226–1228,

The discussion section highlights and interprets the results or data obtained and explains how the resulting data relates to the original research question.  It explains how and why the results obtained  are significant.  It is appropriate to examine and explain why the results were observed and why the data was interpreted in a specific way. This is also the section where additional research or further work regarding the research question can be stated.

The results and the discussion can be presented as a combined "Results and Discussion" section if it makes sense to do so.

  • Clinical Chemistry -Guide to Scientific Writing: The Discussion Section: Your Closing Argument

Thomas M Annesley, The Discussion Section: Your Closing Argument, Clinical Chemistry , Volume 56, Issue 11, 1 November 2010, Pages 1671–1674, '

Figures and tables should be included in the Results or the Results and discussion section and should support, clarify, and make your work more clear through a visual, organized, representation of the data collected.

  • Clinical Chemistry -Guide to Scientific Writing: Put Your Best Figure Forward: Line Graphs and Scattergrams

Thomas M Annesley, Put Your Best Figure Forward: Line Graphs and Scattergrams, Clinical Chemistry , Volume 56, Issue 8, 1 August 2010, Pages 1229–1233,

  • Clinical Chemistry -Guide to Scientific Writing: Bars and Pies Make Better Desserts than Figures

Thomas M Annesley, Bars and Pies Make Better Desserts than Figures, Clinical Chemistry , Volume 56, Issue 9, 1 September 2010, Pages 1394–1400,

  • Clinical Chemistry -Guide to Scientific Writing: Bring Your Best to the Table

Thomas M Annesley, Bring Your Best to the Table, Clinical Chemistry , Volume 56, Issue 10, 1 October 2010, Pages 1528–1534,

The conclusion provides a brief summary of what was accomplished in a manner similar to the abstract, but the conclusion should specifically address how the results of the research relate back to the original question or problem.

A list of the published works that were cited in the paper or report using the proper citation and reference format for the field and publication (e.g. citing and providing a reference list using the American Chemical Society guidelines).

  • Clinical Chemistry -Guide to Scientific Writing: Giving Credit: Citations and References

Thomas M Annesley, Giving Credit: Citations and References, Clinical Chemistry , Volume 57, Issue 1, 1 January 2011, Pages 14–17,

  • << Previous: Home
  • Next: Citations and References >>
  • Last Updated: Sep 8, 2022 2:41 PM
  • URL:

Chemistry Writing Guide

Introduction, writing assignments, discipline-specific strategies, watch out for..., professor's comments and websites.

Writing in chemistry is similar to writing in other disciplines in that your paper must have a clear purpose that explains why you are writing, a thesis statement or main idea that defines the problem to be addressed, and background information wherever necessary. In addition, you should include evidence in the form of figures, graphs, and tables to support your argument.

You will be asked to write an abstract -- a single-spaced paragraph summary that briefly states the purpose of the experiment, important results (and how the results were obtained), and conclusions. Ideally, the abstract can be thought of as one or two sentences from each section of the paper that form a cohesive paragraph that summarizes the entire paper. The abstract should be single spaced unless you receive other instructions from your professor.

When writing an abstract, you should avoid too much experimental detail (e.g. concentration of stock solutions used) or preliminary results (i.e. "raw" data). In addition, make certain that the purpose of the experiment is stated clearly and early in the abstract. Ideally, it should be stated in the first or second sentence.

Lab Reports

There are six main sections in a chemistry paper: introduction, experimental section, results section, discussion section, conclusion, and list of references. As with most disciplines, the introduction should include your background knowledge of the experiment, including theory and past research, the relevance of your research, and the thesis statement. You may also state in your introduction any general conclusions you discovered, but try to avoid making your introduction longer than a page. The purpose of the introduction in a chemical journal is to provide (1) a literature review of what has been published on the subject to justify the importance of your research, (2) an explanation of any unusual experimental approaches, and (3) any background information or explanations that will help the reader understand your experiment and your results. Ultimately, the introduction should explain how the experimental approach you chose allows you to find the numerical or qualitative results you are looking for. For example, if you're going to determine if the substance you synthesized is a particular compound by examining its UV-Vis spectrum, you should find in the literature or a reference book the maximum wavelength of the compound and present it in the introduction. The experimental section focuses on the details of the experiment. Be certain to include enough information so that the reader could repeat the experiment and obtain similar results within the limits of uncertainty. The following should be addressed in this section: treatment of data (e.g. calculations or computations used to generate graphs) and an identification of instruments and sources of materials used (e.g. synthesized within the lab or bought from Aldrich, Sigma, or Fluka). For commercially available equipment, the manufacturer and the model should be mentioned (e.g. JASCO UV-Vis Spectrophotometer). The results section should include any figures, graphs, and tables that summarize the data. The material in this section should be presented in the order that best defends the thesis and the order in which they will be addressed in the discussion section. The order in which the data was collected is rarely important. For example, just because the data for graph N was collected before that of graph M does not mean that M shouldn't be presented first if it makes the presentation of data more coherent. In the results section, graphs are usually listed as figures. Tables are numbered and given specific titles (must include concentrations, volumes, etc.), which are placed at the top of the table. Figures (graphs or any other visuals) are numbered and given a caption, not a title. The caption should be several sentences long and explain what the figure is, what result is found from the figure, and the importance of the result. Captions are placed below the figure. For a results section, the text, tables, and figures should mirror each other. That is, the text must include all of the important information given in the graphs and tables, but in written form. If a table or figure is included in the report, it must be specifically referenced in the text as at the end of this sentence (Table 1). It might also be worthwhile to note that figures and tables are usually submitted to a journal and also to a professor with the tables and figures attached to the end of the report, not interspersed throughout the text. Journals insert your figures and tables according to their page format. In the discussion section, you should explain your results and observations and illustrate how they support your thesis, discuss any possible sources of error, and suggest potential future research stemming from your results. You may also want to mention any past research in the field that may pertain to your experiment's results.

Something to think about: results and discussion sections are often combined in chemical journals. In that case, each result is presented and then its relevance is explained. If you are writing a results section alone, you should only present, not interpret, your results. For example, a statement like, "The UV-Vis spectrum of the complex showed a peak at 291 nm" is a statement of your numerical result and is appropriate for a results section. A statement like, "The peak at 291 nm indicates that the complex changed conformation" is interpretive and belongs in a discussion section. Your conclusion should contain a brief summary of the paper and must state important results (e.g. yield of product) and assess the research with respect to the purpose. This section may be combined with the discussion section; that is, the last paragraph of the discussion section may act as a conclusion. In the reference section you must list all non-original sources used in the paper in the order in which they appear with the appropriate number. Citations should be made according to the format of the journal to which you will submit your paper. For a Swarthmore class, the Journal of the American Chemical Society format is appropriate. Unlike other disciplines, citations in a chemistry paper are usually not in-text or parenthetical, but incorporated using superscripts as at the end of this sentence. 1 It is sometimes appropriate in a discussion section to refer to other researchers by name and end the sentence with a reference. For example, "Khmelnitksy, et al. found that trypsin denatures in 2-propanol." 2

  • Chemistry papers should be written in passive voice (unless you receive other instructions from your professor).
  • Abbreviations or acronyms must be explained the first time they are used.
  • Figures, graphs, and tables must be titled and referenced in the text.
  • References (including textbooks and lab manuals) must be cited and numbered consecutively with the superscript number corresponding to that reference in the reference section of the paper. The use of superscript suffices as the mode of reference because it eliminates the need for in-text citations and footnotes.

I. Organization: As for all lab reports, chemistry reports are very structured and must be highly organized in a logical way. Organization of results is especially important. Your results and discussion sections, as well as tables and figures, should be organized in a way that leads the reader to draw the same conclusion that you did based on your data. Don't just tack on a graph at the end of the paper or arbitrarily put your results into a table. Think about how you can use tables to make comparisons between your data and literature or reference values. Think about the format of your tables and the chronology of your results section. How can you present your results so that the reader is already convinced of your conclusion before you explicitly state it?

II. Repetition: If you've already said it once, or it's already been published somewhere else, don't say it again. You can refer to other parts of your paper instead of repeating explanations or facts. If you've already written an experimental methods section, you've already explained your procedure; there is no need to provide procedural details again when you talk about results. If the procedure you used came from a published article, provide a short summary, explain any alterations, and then give the citation. Also, if you explain someone else's experimental results in the introduction, it is acceptable to write statements like, "As discussed above, Khmelnitsky, et al. found contradictory results" in your results section. Journals have page limits. Repetitious or unnecessary words or figures are unwelcome.

III. Distraction: Remember that the whole point of writing a chemistry paper is to present results and prove your conclusion based on those results. There are a lot of numbers, facts, and procedure information that you can easily get bogged down by. Just remember that ultimately you have to convince the reader that your conclusion is accurate. If you feel overwhelmed by the amount of information you have to include, try making a flow chart that shows the logical progression of your procedure. Or create your figures and tables first, and then use them as an outline or guide to write your results section. Take a look at published articles to get a sense of how others organize papers and what kinds of phrases and sentence structure are useful and accepted.

Courses Taught: General Chemistry, Organic I and II laboratories

Particular stylistic issues you should keep in mind:

"Write as concisely as possible. Know the meanings of the words you use and choose the best word for your purpose."

Grammar/spelling and word choice pet peeves:

  • Using "this" and "that' as undefined pronouns
  • Using "so" without "that" or "as"
  • Misspelling of terms that are presented in the manual

Additional Site Navigation

Social media links, additional navigation links.

  • Alumni Resources & Events
  • Athletics & Wellness
  • Campus Calendar
  • Parent & Family Resources

Helpful Information

Dining hall hours, next trains to philadelphia, next trico shuttles.

Swarthmore Traditions

Student holds candle at night

How to Plan Your Classes

student speaks with professor

The Swarthmore Bucket List

Students in makeshift boat on creek

Search the website

College of Staten Island Home

  • CSI Library Home
  • CSI Library
  • Writing a Research Paper
  • More e-Books...recent acquisitions new!
  • Evaluate Journals
  • Live-Streaming Media
  • Statistical Resources
  • Citation Style Guides
  • Detecting Fake News
  • What is Plagiarism? Why Cite? What are the consequences?
  • Scholarly Communications
  • Jobs and Careers new titles added
  • RefWorks - Citation Manager
  • Off-Campus Access

Our online chat service is available 24/7 and is designed to support the CSI community needing assistance with research and other curricular issues. If a CUNY librarian is not available, an academic librarian from one of our partner libraries will assist you.

Start with choosing a topic...

Find articles in these databases....

  • ACS Publications in Chemistry This link opens in a new window Full text of journals published by the American Chemical Society (ACS). Find articles in all areas of chemistry as well as related fields such as environmental science, biotechnology, material science, pharmacology, and toxicology. Click here for Chemical and Engineering News . Coverage Dates: 1879 to present
  • National Science Digital Library This link opens in a new window The NSDL provides access to resources in science, technology, engineering, and mathematics (STEM) education and research. It includes labs, lectures, readings, games, and full course outlines. Most resources in the library adhere to principles of Open Educational Resource (OER) access.
  • Nature Publishing Group List of Journals Nature Publishing Group (NPG) is a publisher of high impact scientific and medical information in print and online. NPG publishes journals, online databases, and services across the life, physical, chemical and applied sciences and clinical medicine. CSI subscribes to a selected list of these titles. Please see our Journal Search on the home page to check for any particular title.
  • PubMed (MEDLINE) Free database of medical literature produced by the National Library of Medicine. Contains citations and abstracts of articles in medicine, dentistry, nursing, and related fields. Coverage Dates:  1950 to present
  • ScienceDirect This link opens in a new window Abstracts of scholarly literature in science, technology, and medicine with full text available for articles published after 1995. Core subjects include physics, chemistry, engineering, biology, environmental science, health and medicine. Also provides coverage of a number of titles in the social sciences and humanities (particularly management, economics, psychology, and linguistics). Please note that the Library does not subscribe to all journals in ScienceDirect. Subscribed content is marked by a green "full text available" icon.
  • SpringerLink eBooks and Journals This link opens in a new window Over 20,000 full text ebooks and reference works, and 2,500 journals published by Springer. Core focus is science, technology, and medicine but includes some titles in the social sciences and humanities.

Research Methods & Writing Grant Proposals

Cover Art

  • << Previous: Statistical Resources
  • Next: Citation Style Guides >>

link to CSI Website

Facebook Twitter Instagram

  • URL:
  • Last Updated: Mar 19, 2024 9:47 AM
  • Request Info

Waidner-Spahr Library

  • Do Research
  • About the Library
  • Dickinson Scholar
  • Ask a Librarian
  • My Library Account

Chemistry: Citing Sources in Chemistry

  • Online Tools
  • Web Resources
  • Biographical Information
  • Citing Sources in Chemistry
  • Finding Patents

Library Catalog

Enter your search:

Citing Sources

College Policy on Citing Sources & Plagiarism

It is necessary for you to give proper credit to all of the resources you use in your research papers. Plagiarism is a violation of Dickinson's Student Code of Conduct, and is a specific form of cheating defined in the code as follows:

1) To plagiarize is to use without proper citation or acknowledgment the words, ideas, or work of another. Whenever one relies on someone else for phraseology, even for only two or three words, one must acknowledge indebtedness by using quotation marks and giving the source, either in the text or in a footnote.

2) When one includes information that is not a matter of general knowledge, including all statistics and translations, one must indicate one's indebtedness in the text or footnote. When one borrows an idea or the logic of an argument, one must acknowledge indebtedness either in a footnote or in the text. When in doubt, footnote. (Academic Standards Committee, November, 1965) You should include appropriate citations in all of your research. Your professor will direct you as to what specific citation style they may prefer.

How to Cite

In addition to the examples below, see the new online-only style guide:

In chemistry, the references are used in a paper may be presented in a number of formats, so always ask what your professor requires. ACS Style is used by the American Chemical Society:

Journal article citation elements for ACS Style :

Author one surname, first and middle initials; author 2 name, initials. Standard Abbreviation for Journal Title in Italics . Year in bold , Volume number in italics , first page-last page.

  • List all authors using last names and initials.
  • Do NOT make up or guess journal abbreviations! You can look them up using the CAS Source Index (CASSI) Search Tool .

For example:

1) online journal article (the S after the page numbers is because this specific article was in a supplement to the main journal)

2) print journal article

When a quotation or idea needs to be cited within the text of the paper, an italicized number corresponding to the appropriate source in the enumerated list is included within the sentence, in parentheses and italicized, immediately following the phrase that requires credit, like so: "...quotation from article" (2) .

Book citation elements for  ACS Style :

Author (or editor), book title, date of publication, publisher, and place of publication. 

1) Book with two authors

Beall, H.; Trimbur, J. A Short Guide to Writing about Chemistry , 2nd ed.; Longman: New York, 2001. 

2) Chapter in an edited book (italicize the title of the edited volume, not the chapter title, and include page numbers for the chapter being cited)

McBrien, M. Selecting the Correct pH Value for HPLC. In HPLC Made to Measure: A Practical Handbook for Optimization ; Kromidas, S., Ed.; Wiley-VCH: Weinheim, Germany, 2006; pp 89-103.

Website  citation elements for  ACS Style :

Author (if known), title of the website, URL, date of access. 

1) Website with no listed author:

Penn State Department of Chemistry. (accessed June 7, 2010).

2) Website with author:

U.S. Environmental Protection Agency. (accessed Nov 15, 2004). 

The University of Wisconsin - Madison Chemistry Library has a nice online guide to citing in ACS Style with many more examples.

Subject Guide

Profile Photo

Mobile Librarian Hours: Tuesdays 10 a.m.- Noon: Kaufman Entrance Wednesdays 10 a.m.- Noon: Rector Atrium Thursdays 10 a.m - 11 a.m.: Tome 2nd Floor

  • << Previous: Biographical Information
  • Next: Finding Patents >>
  • Last Updated: Mar 4, 2024 12:05 PM
  • URL:

How to Write a Research Paper Introduction (with Examples)

How to Write a Research Paper Introduction (with Examples)

The research paper introduction section, along with the Title and Abstract, can be considered the face of any research paper. The following article is intended to guide you in organizing and writing the research paper introduction for a quality academic article or dissertation.

The research paper introduction aims to present the topic to the reader. A study will only be accepted for publishing if you can ascertain that the available literature cannot answer your research question. So it is important to ensure that you have read important studies on that particular topic, especially those within the last five to ten years, and that they are properly referenced in this section. 1 What should be included in the research paper introduction is decided by what you want to tell readers about the reason behind the research and how you plan to fill the knowledge gap. The best research paper introduction provides a systemic review of existing work and demonstrates additional work that needs to be done. It needs to be brief, captivating, and well-referenced; a well-drafted research paper introduction will help the researcher win half the battle.

The introduction for a research paper is where you set up your topic and approach for the reader. It has several key goals:

  • Present your research topic
  • Capture reader interest
  • Summarize existing research
  • Position your own approach
  • Define your specific research problem and problem statement
  • Highlight the novelty and contributions of the study
  • Give an overview of the paper’s structure

The research paper introduction can vary in size and structure depending on whether your paper presents the results of original empirical research or is a review paper. Some research paper introduction examples are only half a page while others are a few pages long. In many cases, the introduction will be shorter than all of the other sections of your paper; its length depends on the size of your paper as a whole.

  • Break through writer’s block. Write your research paper introduction with Paperpal Copilot

Table of Contents

What is the introduction for a research paper, why is the introduction important in a research paper, craft a compelling introduction section with paperpal. try now, 1. introduce the research topic:, 2. determine a research niche:, 3. place your research within the research niche:, craft accurate research paper introductions with paperpal. start writing now, frequently asked questions on research paper introduction, key points to remember.

The introduction in a research paper is placed at the beginning to guide the reader from a broad subject area to the specific topic that your research addresses. They present the following information to the reader

  • Scope: The topic covered in the research paper
  • Context: Background of your topic
  • Importance: Why your research matters in that particular area of research and the industry problem that can be targeted

The research paper introduction conveys a lot of information and can be considered an essential roadmap for the rest of your paper. A good introduction for a research paper is important for the following reasons:

  • It stimulates your reader’s interest: A good introduction section can make your readers want to read your paper by capturing their interest. It informs the reader what they are going to learn and helps determine if the topic is of interest to them.
  • It helps the reader understand the research background: Without a clear introduction, your readers may feel confused and even struggle when reading your paper. A good research paper introduction will prepare them for the in-depth research to come. It provides you the opportunity to engage with the readers and demonstrate your knowledge and authority on the specific topic.
  • It explains why your research paper is worth reading: Your introduction can convey a lot of information to your readers. It introduces the topic, why the topic is important, and how you plan to proceed with your research.
  • It helps guide the reader through the rest of the paper: The research paper introduction gives the reader a sense of the nature of the information that will support your arguments and the general organization of the paragraphs that will follow. It offers an overview of what to expect when reading the main body of your paper.

What are the parts of introduction in the research?

A good research paper introduction section should comprise three main elements: 2

  • What is known: This sets the stage for your research. It informs the readers of what is known on the subject.
  • What is lacking: This is aimed at justifying the reason for carrying out your research. This could involve investigating a new concept or method or building upon previous research.
  • What you aim to do: This part briefly states the objectives of your research and its major contributions. Your detailed hypothesis will also form a part of this section.

How to write a research paper introduction?

The first step in writing the research paper introduction is to inform the reader what your topic is and why it’s interesting or important. This is generally accomplished with a strong opening statement. The second step involves establishing the kinds of research that have been done and ending with limitations or gaps in the research that you intend to address. Finally, the research paper introduction clarifies how your own research fits in and what problem it addresses. If your research involved testing hypotheses, these should be stated along with your research question. The hypothesis should be presented in the past tense since it will have been tested by the time you are writing the research paper introduction.

The following key points, with examples, can guide you when writing the research paper introduction section:

  • Highlight the importance of the research field or topic
  • Describe the background of the topic
  • Present an overview of current research on the topic

Example: The inclusion of experiential and competency-based learning has benefitted electronics engineering education. Industry partnerships provide an excellent alternative for students wanting to engage in solving real-world challenges. Industry-academia participation has grown in recent years due to the need for skilled engineers with practical training and specialized expertise. However, from the educational perspective, many activities are needed to incorporate sustainable development goals into the university curricula and consolidate learning innovation in universities.

  • Reveal a gap in existing research or oppose an existing assumption
  • Formulate the research question

Example: There have been plausible efforts to integrate educational activities in higher education electronics engineering programs. However, very few studies have considered using educational research methods for performance evaluation of competency-based higher engineering education, with a focus on technical and or transversal skills. To remedy the current need for evaluating competencies in STEM fields and providing sustainable development goals in engineering education, in this study, a comparison was drawn between study groups without and with industry partners.

  • State the purpose of your study
  • Highlight the key characteristics of your study
  • Describe important results
  • Highlight the novelty of the study.
  • Offer a brief overview of the structure of the paper.

Example: The study evaluates the main competency needed in the applied electronics course, which is a fundamental core subject for many electronics engineering undergraduate programs. We compared two groups, without and with an industrial partner, that offered real-world projects to solve during the semester. This comparison can help determine significant differences in both groups in terms of developing subject competency and achieving sustainable development goals.

Write a Research Paper Introduction in Minutes with Paperpal

Paperpal Copilot is a generative AI-powered academic writing assistant. It’s trained on millions of published scholarly articles and over 20 years of STM experience. Paperpal Copilot helps authors write better and faster with:

  • Real-time writing suggestions
  • In-depth checks for language and grammar correction
  • Paraphrasing to add variety, ensure academic tone, and trim text to meet journal limits

With Paperpal Copilot, create a research paper introduction effortlessly. In this step-by-step guide, we’ll walk you through how Paperpal transforms your initial ideas into a polished and publication-ready introduction.

how to write a chemistry research paper

How to use Paperpal to write the Introduction section

Step 1: Sign up on Paperpal and click on the Copilot feature, under this choose Outlines > Research Article > Introduction

Step 2: Add your unstructured notes or initial draft, whether in English or another language, to Paperpal, which is to be used as the base for your content.

Step 3: Fill in the specifics, such as your field of study, brief description or details you want to include, which will help the AI generate the outline for your Introduction.

Step 4: Use this outline and sentence suggestions to develop your content, adding citations where needed and modifying it to align with your specific research focus.

Step 5: Turn to Paperpal’s granular language checks to refine your content, tailor it to reflect your personal writing style, and ensure it effectively conveys your message.

You can use the same process to develop each section of your article, and finally your research paper in half the time and without any of the stress.

The purpose of the research paper introduction is to introduce the reader to the problem definition, justify the need for the study, and describe the main theme of the study. The aim is to gain the reader’s attention by providing them with necessary background information and establishing the main purpose and direction of the research.

The length of the research paper introduction can vary across journals and disciplines. While there are no strict word limits for writing the research paper introduction, an ideal length would be one page, with a maximum of 400 words over 1-4 paragraphs. Generally, it is one of the shorter sections of the paper as the reader is assumed to have at least a reasonable knowledge about the topic. 2 For example, for a study evaluating the role of building design in ensuring fire safety, there is no need to discuss definitions and nature of fire in the introduction; you could start by commenting upon the existing practices for fire safety and how your study will add to the existing knowledge and practice.

When deciding what to include in the research paper introduction, the rest of the paper should also be considered. The aim is to introduce the reader smoothly to the topic and facilitate an easy read without much dependency on external sources. 3 Below is a list of elements you can include to prepare a research paper introduction outline and follow it when you are writing the research paper introduction. Topic introduction: This can include key definitions and a brief history of the topic. Research context and background: Offer the readers some general information and then narrow it down to specific aspects. Details of the research you conducted: A brief literature review can be included to support your arguments or line of thought. Rationale for the study: This establishes the relevance of your study and establishes its importance. Importance of your research: The main contributions are highlighted to help establish the novelty of your study Research hypothesis: Introduce your research question and propose an expected outcome. Organization of the paper: Include a short paragraph of 3-4 sentences that highlights your plan for the entire paper

Cite only works that are most relevant to your topic; as a general rule, you can include one to three. Note that readers want to see evidence of original thinking. So it is better to avoid using too many references as it does not leave much room for your personal standpoint to shine through. Citations in your research paper introduction support the key points, and the number of citations depend on the subject matter and the point discussed. If the research paper introduction is too long or overflowing with citations, it is better to cite a few review articles rather than the individual articles summarized in the review. A good point to remember when citing research papers in the introduction section is to include at least one-third of the references in the introduction.

The literature review plays a significant role in the research paper introduction section. A good literature review accomplishes the following: Introduces the topic – Establishes the study’s significance – Provides an overview of the relevant literature – Provides context for the study using literature – Identifies knowledge gaps However, remember to avoid making the following mistakes when writing a research paper introduction: Do not use studies from the literature review to aggressively support your research Avoid direct quoting Do not allow literature review to be the focus of this section. Instead, the literature review should only aid in setting a foundation for the manuscript.

Remember the following key points for writing a good research paper introduction: 4

  • Avoid stuffing too much general information: Avoid including what an average reader would know and include only that information related to the problem being addressed in the research paper introduction. For example, when describing a comparative study of non-traditional methods for mechanical design optimization, information related to the traditional methods and differences between traditional and non-traditional methods would not be relevant. In this case, the introduction for the research paper should begin with the state-of-the-art non-traditional methods and methods to evaluate the efficiency of newly developed algorithms.
  • Avoid packing too many references: Cite only the required works in your research paper introduction. The other works can be included in the discussion section to strengthen your findings.
  • Avoid extensive criticism of previous studies: Avoid being overly critical of earlier studies while setting the rationale for your study. A better place for this would be the Discussion section, where you can highlight the advantages of your method.
  • Avoid describing conclusions of the study: When writing a research paper introduction remember not to include the findings of your study. The aim is to let the readers know what question is being answered. The actual answer should only be given in the Results and Discussion section.

To summarize, the research paper introduction section should be brief yet informative. It should convince the reader the need to conduct the study and motivate him to read further. If you’re feeling stuck or unsure, choose trusted AI academic writing assistants like Paperpal to effortlessly craft your research paper introduction and other sections of your research article.

1. Jawaid, S. A., & Jawaid, M. (2019). How to write introduction and discussion. Saudi Journal of Anaesthesia, 13(Suppl 1), S18.

2. Dewan, P., & Gupta, P. (2016). Writing the title, abstract and introduction: Looks matter!. Indian pediatrics, 53, 235-241.

3. Cetin, S., & Hackam, D. J. (2005). An approach to the writing of a scientific Manuscript1. Journal of Surgical Research, 128(2), 165-167.

4. Bavdekar, S. B. (2015). Writing introduction: Laying the foundations of a research paper. Journal of the Association of Physicians of India, 63(7), 44-6.

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!  

Related Reads:

  • Scientific Writing Style Guides Explained
  • 5 Reasons for Rejection After Peer Review
  • Ethical Research Practices For Research with Human Subjects
  • 8 Most Effective Ways to Increase Motivation for Thesis Writing 

Practice vs. Practise: Learn the Difference

Academic paraphrasing: why paperpal’s rewrite should be your first choice , you may also like, what are journal guidelines on using generative ai..., quillbot review: features, pricing, and free alternatives, what is an academic paper types and elements , should you use ai tools like chatgpt for..., publish research papers: 9 steps for successful publications , what are the different types of research papers, how to make translating academic papers less challenging, self-plagiarism in research: what it is and how..., 6 tips for post-doc researchers to take their..., presenting research data effectively through tables and figures.

how to write a chemistry research paper

  • Developing a Research Question

by acburton | Mar 22, 2024 | Resources for Students , Writing Resources

Selecting your research question and creating a clear goal and structure for your writing can be challenging – whether you are doing it for the first time or if you’ve done it many times before. It can be especially difficult when your research question starts to look and feel a little different somewhere between your first and final draft. Don’t panic! It’s normal for your research question to change a little (or even quite a bit) as you move through and engage with the writing process. Anticipating this can remind you to stay on track while you work and that it’ll be okay even if the literature takes you in a different direction.

What Makes an Effective Research Question?

The most effective research question will usually be a critical thinking question and should use “how” or “why” to ensure it can move beyond a yes/no or one-word type of answer. Consider how your research question can aim to reveal something new, fill in a gap, even if small, and contribute to the field in a meaningful way; How might the proposed project move knowledge forward about a particular place or process? This should be specific and achievable!

The CEWC’s Grad Writing Consultant Tariq says, “I definitely concentrated on those aspects of what I saw in the field where I believed there was an opportunity to move the discipline forward.”

General Tips

Do your research.

Utilize the librarians at your university and take the time to research your topic first. Try looking at very general sources to get an idea of what could be interesting to you before you move to more academic articles that support your rough idea of the topic. It is important that research is grounded in what you see or experience regarding the topic you have chosen and what is already known in the literature. Spend time researching articles, books, etc. that supports your thesis. Once you have a number of sources that you know support what you want to write about, formulate a research question that serves as the interrogative form of your thesis statement.

Grad Writing Consultant Deni advises, “Delineate your intervention in the literature (i.e., be strategic about the literature you discuss and clear about your contributions to it).”

Start Broadly…. then Narrow Your Topic Down to Something Manageable

When brainstorming your research question, let your mind veer toward connections or associations that you might have already considered or that seem to make sense and consider if new research terms, language or concepts come to mind that may be interesting or exciting for you as a researcher. Sometimes testing out a research question while doing some preliminary researching is also useful to see if the language you are using or the direction you are heading toward is fruitful when trying to search strategically in academic databases. Be prepared to focus on a specific area of a broad topic.

Writing Consultant Jessie recommends outlining: “I think some rough outlining with a research question in mind can be helpful for me. I’ll have a research question and maybe a working thesis that I feel may be my claim to the research question based on some preliminary materials, brainstorming, etc.” — Jessie, CEWC Writing Consultant

Try an Exercise

In the earliest phase of brainstorming, try an exercise suggested by CEWC Writing Specialist, Percival! While it is normally used in classroom or workshop settings, this exercise can easily be modified for someone working alone. The flow of the activity, if done within a group setting, is 1) someone starts with an idea, 2) three other people share their idea, and 3) the starting person picks two of these new ideas they like best and combines their original idea with those. The activity then begins again with the idea that was not chosen. The solo version of this exercise substitutes a ‘word bank,’ created using words, topics, or ideas similar to your broad, overarching theme. Pick two words or phrases from your word bank, combine it with your original idea or topic, and ‘start again’ with two different words. This serves as a replacement for different people’s suggestions. Ideas for your ‘word bank’ can range from vague prompts about mapping or webbing (e.g., where your topic falls within the discipline and others like it), to more specific concepts that come from tracing the history of an idea (its past, present, future) or mapping the idea’s related ideas, influences, etc. Care for a physics analogy? There is a particle (your topic) that you can describe, a wave that the particle traces, and a field that the particle is mapped on.

Get Feedback and Affirm Your Confidence!

Creating a few different versions of your research question (they may be the same topic/issue/theme or differ slightly) can be useful during this process. Sharing these with trusted friends, colleagues, mentors, (or tutors!) and having conversations about your questions and ideas with other people can help you decide which version you may feel most confident or interested in. Ask colleagues and mentors to share their research questions with you to get a lot of examples. Once you have done the work of developing an effective research question, do not forget to affirm your confidence! Based on your working thesis, think about how you might organize your chapters or paragraphs and what resources you have for supporting this structure and organization. This can help boost your confidence that the research question you have created is effective and fruitful.

Be Open to Change

Remember, your research question may change from your first to final draft. For questions along the way, make an appointment with the Writing Center. We are here to help you develop an effective and engaging research question and build the foundation for a solid research paper!

Example 1: In my field developing a research question involves navigating the relationship between 1) what one sees/experiences at their field site and 2) what is already known in the literature. During my preliminary research, I found that the financial value of land was often a matter of precisely these cultural factors. So, my research question ended up being: How do the social and material qualities of land entangle with processes of financialization in the city of Lahore. Regarding point #1, this question was absolutely informed by what I saw in the field. But regarding point #2, the question was also heavily shaped by the literature. – Tariq

Example 2: A research question should not be a yes/no question like “Is pollution bad?”; but an open-ended question where the answer has to be supported with reasons and explanation. The question also has to be narrowed down to a specific topic—using the same example as before—”Is pollution bad?” can be revised to “How does pollution affect people?” I would encourage students to be more specific then; e.g., what area of pollution do you want to talk about: water, air, plastic, climate change… what type of people or demographic can we focus on? …how does this affect marginalized communities, minorities, or specific areas in California? After researching and deciding on a focus, your question might sound something like: How does government policy affect water pollution and how does it affect the marginalized communities in the state of California? -Janella

Our Newest Resources!

  • Transitioning to Long-form Writing
  • Integrating Direct Quotations into Your Writing
  • Nurturing a Growth Mindset to Overcome Writing Challenges and Develop Confidence in College Level Writing
  • An Introduction to Paraphrasing, Summarizing, and Quoting

Additional Resources

  • Graduate Writing Consultants
  • Instructor Resources
  • Student Resources
  • Quick Guides and Handouts
  • Self-Guided and Directed Learning Activities

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • View all journals
  • My Account Login
  • Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • Open access
  • Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

  • Michiel Schreurs   ORCID: 1 , 2 , 3   na1 ,
  • Supinya Piampongsant 1 , 2 , 3   na1 ,
  • Miguel Roncoroni   ORCID: 1 , 2 , 3   na1 ,
  • Lloyd Cool   ORCID: 1 , 2 , 3 , 4 ,
  • Beatriz Herrera-Malaver   ORCID: 1 , 2 , 3 ,
  • Christophe Vanderaa   ORCID: 4 ,
  • Florian A. Theßeling 1 , 2 , 3 ,
  • Łukasz Kreft   ORCID: 5 ,
  • Alexander Botzki   ORCID: 5 ,
  • Philippe Malcorps 6 ,
  • Luk Daenen 6 ,
  • Tom Wenseleers   ORCID: 4 &
  • Kevin J. Verstrepen   ORCID: 1 , 2 , 3  

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

39k Accesses

749 Altmetric

Metrics details

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

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

Similar content being viewed by others

how to write a chemistry research paper

BitterSweet: Building machine learning models for predicting the bitter and sweet taste of small molecules

Rudraksh Tuwani, Somin Wadhwa & Ganesh Bagler

how to write a chemistry research paper

Sensory lexicon and aroma volatiles analysis of brewing malt

Xiaoxia Su, Miao Yu, … Tianyi Du

how to write a chemistry research paper

Predicting odor from molecular structure: a multi-label classification approach

Kushagra Saini & Venkatnarayan Ramanathan


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 .

figure 1

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).

figure 2

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).

figure 3

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.

figure 4

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.

figure 5

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 (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 (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 .

Tieman, D. et al. A chemical genetic roadmap to improved tomato flavor. Science 355 , 391–394 (2017).

Article   ADS   CAS   PubMed   Google Scholar  

Plutowska, B. & Wardencki, W. Application of gas chromatography–olfactometry (GC–O) in analysis and quality assessment of alcoholic beverages – A review. Food Chem. 107 , 449–463 (2008).

Article   CAS   Google Scholar  

Legin, A., Rudnitskaya, A., Seleznev, B. & Vlasov, Y. Electronic tongue for quality assessment of ethanol, vodka and eau-de-vie. Anal. Chim. Acta 534 , 129–135 (2005).

Loutfi, A., Coradeschi, S., Mani, G. K., Shankar, P. & Rayappan, J. B. B. Electronic noses for food quality: A review. J. Food Eng. 144 , 103–111 (2015).

Ahn, Y.-Y., Ahnert, S. E., Bagrow, J. P. & Barabási, A.-L. Flavor network and the principles of food pairing. Sci. Rep. 1 , 196 (2011).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Bartoshuk, L. M. & Klee, H. J. Better fruits and vegetables through sensory analysis. Curr. Biol. 23 , R374–R378 (2013).

Article   CAS   PubMed   Google Scholar  

Piggott, J. R. Design questions in sensory and consumer science. Food Qual. Prefer. 3293 , 217–220 (1995).

Article   Google Scholar  

Kermit, M. & Lengard, V. Assessing the performance of a sensory panel-panellist monitoring and tracking. J. Chemom. 19 , 154–161 (2005).

Cook, D. J., Hollowood, T. A., Linforth, R. S. T. & Taylor, A. J. Correlating instrumental measurements of texture and flavour release with human perception. Int. J. Food Sci. Technol. 40 , 631–641 (2005).

Chinchanachokchai, S., Thontirawong, P. & Chinchanachokchai, P. A tale of two recommender systems: The moderating role of consumer expertise on artificial intelligence based product recommendations. J. Retail. Consum. Serv. 61 , 1–12 (2021).

Ross, C. F. Sensory science at the human-machine interface. Trends Food Sci. Technol. 20 , 63–72 (2009).

Chambers, E. IV & Koppel, K. Associations of volatile compounds with sensory aroma and flavor: The complex nature of flavor. Molecules 18 , 4887–4905 (2013).

Pinu, F. R. Metabolomics—The new frontier in food safety and quality research. Food Res. Int. 72 , 80–81 (2015).

Danezis, G. P., Tsagkaris, A. S., Brusic, V. & Georgiou, C. A. Food authentication: state of the art and prospects. Curr. Opin. Food Sci. 10 , 22–31 (2016).

Shepherd, G. M. Smell images and the flavour system in the human brain. Nature 444 , 316–321 (2006).

Meilgaard, M. C. Prediction of flavor differences between beers from their chemical composition. J. Agric. Food Chem. 30 , 1009–1017 (1982).

Xu, L. et al. Widespread receptor-driven modulation in peripheral olfactory coding. Science 368 , eaaz5390 (2020).

Kupferschmidt, K. Following the flavor. Science 340 , 808–809 (2013).

Billesbølle, C. B. et al. Structural basis of odorant recognition by a human odorant receptor. Nature 615 , 742–749 (2023).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Smith, B. Perspective: Complexities of flavour. Nature 486 , S6–S6 (2012).

Pfister, P. et al. Odorant receptor inhibition is fundamental to odor encoding. Curr. Biol. 30 , 2574–2587 (2020).

Moskowitz, H. W., Kumaraiah, V., Sharma, K. N., Jacobs, H. L. & Sharma, S. D. Cross-cultural differences in simple taste preferences. Science 190 , 1217–1218 (1975).

Eriksson, N. et al. A genetic variant near olfactory receptor genes influences cilantro preference. Flavour 1 , 22 (2012).

Ferdenzi, C. et al. Variability of affective responses to odors: Culture, gender, and olfactory knowledge. Chem. Senses 38 , 175–186 (2013).

Article   PubMed   Google Scholar  

Lawless, H. T. & Heymann, H. Sensory evaluation of food: Principles and practices. (Springer, New York, NY). (2010).

Colantonio, V. et al. Metabolomic selection for enhanced fruit flavor. Proc. Natl. Acad. Sci. 119 , e2115865119 (2022).

Fritz, F., Preissner, R. & Banerjee, P. VirtualTaste: a web server for the prediction of organoleptic properties of chemical compounds. Nucleic Acids Res 49 , W679–W684 (2021).

Tuwani, R., Wadhwa, S. & Bagler, G. BitterSweet: Building machine learning models for predicting the bitter and sweet taste of small molecules. Sci. Rep. 9 , 1–13 (2019).

Dagan-Wiener, A. et al. Bitter or not? BitterPredict, a tool for predicting taste from chemical structure. Sci. Rep. 7 , 1–13 (2017).

Pallante, L. et al. Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach. Sci. Rep. 12 , 1–11 (2022).

Malavolta, M. et al. A survey on computational taste predictors. Eur. Food Res. Technol. 248 , 2215–2235 (2022).

Lee, B. K. et al. A principal odor map unifies diverse tasks in olfactory perception. Science 381 , 999–1006 (2023).

Mayhew, E. J. et al. Transport features predict if a molecule is odorous. Proc. Natl. Acad. Sci. 119 , e2116576119 (2022).

Niu, Y. et al. Sensory evaluation of the synergism among ester odorants in light aroma-type liquor by odor threshold, aroma intensity and flash GC electronic nose. Food Res. Int. 113 , 102–114 (2018).

Yu, P., Low, M. Y. & Zhou, W. Design of experiments and regression modelling in food flavour and sensory analysis: A review. Trends Food Sci. Technol. 71 , 202–215 (2018).

Oladokun, O. et al. The impact of hop bitter acid and polyphenol profiles on the perceived bitterness of beer. Food Chem. 205 , 212–220 (2016).

Linforth, R., Cabannes, M., Hewson, L., Yang, N. & Taylor, A. Effect of fat content on flavor delivery during consumption: An in vivo model. J. Agric. Food Chem. 58 , 6905–6911 (2010).

Guo, S., Na Jom, K. & Ge, Y. Influence of roasting condition on flavor profile of sunflower seeds: A flavoromics approach. Sci. Rep. 9 , 11295 (2019).

Ren, Q. et al. The changes of microbial community and flavor compound in the fermentation process of Chinese rice wine using Fagopyrum tataricum grain as feedstock. Sci. Rep. 9 , 3365 (2019).

Hastie, T., Friedman, J. & Tibshirani, R. The Elements of Statistical Learning. (Springer, New York, NY). (2001).

Dietz, C., Cook, D., Huismann, M., Wilson, C. & Ford, R. The multisensory perception of hop essential oil: a review. J. Inst. Brew. 126 , 320–342 (2020).

CAS   Google Scholar  

Roncoroni, Miguel & Verstrepen, Kevin Joan. Belgian Beer: Tested and Tasted. (Lannoo, 2018).

Meilgaard, M. Flavor chemistry of beer: Part II: Flavor and threshold of 239 aroma volatiles. in (1975).

Bokulich, N. A. & Bamforth, C. W. The microbiology of malting and brewing. Microbiol. Mol. Biol. Rev. MMBR 77 , 157–172 (2013).

Dzialo, M. C., Park, R., Steensels, J., Lievens, B. & Verstrepen, K. J. Physiology, ecology and industrial applications of aroma formation in yeast. FEMS Microbiol. Rev. 41 , S95–S128 (2017).

Article   PubMed   PubMed Central   Google Scholar  

Datta, A. et al. Computer-aided food engineering. Nat. Food 3 , 894–904 (2022).

American Society of Brewing Chemists. Beer Methods. (American Society of Brewing Chemists, St. Paul, MN, U.S.A.).

Olaniran, A. O., Hiralal, L., Mokoena, M. P. & Pillay, B. Flavour-active volatile compounds in beer: production, regulation and control. J. Inst. Brew. 123 , 13–23 (2017).

Verstrepen, K. J. et al. Flavor-active esters: Adding fruitiness to beer. J. Biosci. Bioeng. 96 , 110–118 (2003).

Meilgaard, M. C. Flavour chemistry of beer. part I: flavour interaction between principal volatiles. Master Brew. Assoc. Am. Tech. Q 12 , 107–117 (1975).

Briggs, D. E., Boulton, C. A., Brookes, P. A. & Stevens, R. Brewing 227–254. (Woodhead Publishing). (2004).

Bossaert, S., Crauwels, S., De Rouck, G. & Lievens, B. The power of sour - A review: Old traditions, new opportunities. BrewingScience 72 , 78–88 (2019).

Google Scholar  

Verstrepen, K. J. et al. Flavor active esters: Adding fruitiness to beer. J. Biosci. Bioeng. 96 , 110–118 (2003).

Snauwaert, I. et al. Microbial diversity and metabolite composition of Belgian red-brown acidic ales. Int. J. Food Microbiol. 221 , 1–11 (2016).

Spitaels, F. et al. The microbial diversity of traditional spontaneously fermented lambic beer. PLoS ONE 9 , e95384 (2014).

Blanco, C. A., Andrés-Iglesias, C. & Montero, O. Low-alcohol Beers: Flavor Compounds, Defects, and Improvement Strategies. Crit. Rev. Food Sci. Nutr. 56 , 1379–1388 (2016).

Jackowski, M. & Trusek, A. Non-Alcohol. beer Prod. – Overv. 20 , 32–38 (2018).

Takoi, K. et al. The contribution of geraniol metabolism to the citrus flavour of beer: Synergy of geraniol and β-citronellol under coexistence with excess linalool. J. Inst. Brew. 116 , 251–260 (2010).

Kroeze, J. H. & Bartoshuk, L. M. Bitterness suppression as revealed by split-tongue taste stimulation in humans. Physiol. Behav. 35 , 779–783 (1985).

Mennella, J. A. et al. A spoonful of sugar helps the medicine go down”: Bitter masking bysucrose among children and adults. Chem. Senses 40 , 17–25 (2015).

Wietstock, P., Kunz, T., Perreira, F. & Methner, F.-J. Metal chelation behavior of hop acids in buffered model systems. BrewingScience 69 , 56–63 (2016).

Sancho, D., Blanco, C. A., Caballero, I. & Pascual, A. Free iron in pale, dark and alcohol-free commercial lager beers. J. Sci. Food Agric. 91 , 1142–1147 (2011).

Rodrigues, H. & Parr, W. V. Contribution of cross-cultural studies to understanding wine appreciation: A review. Food Res. Int. 115 , 251–258 (2019).

Korneva, E. & Blockeel, H. Towards better evaluation of multi-target regression models. in ECML PKDD 2020 Workshops (eds. Koprinska, I. et al.) 353–362 (Springer International Publishing, Cham, 2020). .

Gastón Ares. Mathematical and Statistical Methods in Food Science and Technology. (Wiley, 2013).

Grinsztajn, L., Oyallon, E. & Varoquaux, G. Why do tree-based models still outperform deep learning on tabular data? Preprint at (2022).

Gries, S. T. Statistics for Linguistics with R: A Practical Introduction. in Statistics for Linguistics with R (De Gruyter Mouton, 2021). .

Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2 , 56–67 (2020).

Ickes, C. M. & Cadwallader, K. R. Effects of ethanol on flavor perception in alcoholic beverages. Chemosens. Percept. 10 , 119–134 (2017).

Kato, M. et al. Influence of high molecular weight polypeptides on the mouthfeel of commercial beer. J. Inst. Brew. 127 , 27–40 (2021).

Wauters, R. et al. Novel Saccharomyces cerevisiae variants slow down the accumulation of staling aldehydes and improve beer shelf-life. Food Chem. 398 , 1–11 (2023).

Li, H., Jia, S. & Zhang, W. Rapid determination of low-level sulfur compounds in beer by headspace gas chromatography with a pulsed flame photometric detector. J. Am. Soc. Brew. Chem. 66 , 188–191 (2008).

Dercksen, A., Laurens, J., Torline, P., Axcell, B. C. & Rohwer, E. Quantitative analysis of volatile sulfur compounds in beer using a membrane extraction interface. J. Am. Soc. Brew. Chem. 54 , 228–233 (1996).

Molnar, C. Interpretable Machine Learning: A Guide for Making Black-Box Models Interpretable. (2020).

Zhao, Q. & Hastie, T. Causal interpretations of black-box models. J. Bus. Econ. Stat. Publ. Am. Stat. Assoc. 39 , 272–281 (2019).

Article   MathSciNet   Google Scholar  

Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning. (Springer, 2019).

Labrado, D. et al. Identification by NMR of key compounds present in beer distillates and residual phases after dealcoholization by vacuum distillation. J. Sci. Food Agric. 100 , 3971–3978 (2020).

Lusk, L. T., Kay, S. B., Porubcan, A. & Ryder, D. S. Key olfactory cues for beer oxidation. J. Am. Soc. Brew. Chem. 70 , 257–261 (2012).

Gonzalez Viejo, C., Torrico, D. D., Dunshea, F. R. & Fuentes, S. Development of artificial neural network models to assess beer acceptability based on sensory properties using a robotic pourer: A comparative model approach to achieve an artificial intelligence system. Beverages 5 , 33 (2019).

Gonzalez Viejo, C., Fuentes, S., Torrico, D. D., Godbole, A. & Dunshea, F. R. Chemical characterization of aromas in beer and their effect on consumers liking. Food Chem. 293 , 479–485 (2019).

Gilbert, J. L. et al. Identifying breeding priorities for blueberry flavor using biochemical, sensory, and genotype by environment analyses. PLOS ONE 10 , 1–21 (2015).

Goulet, C. et al. Role of an esterase in flavor volatile variation within the tomato clade. Proc. Natl. Acad. Sci. 109 , 19009–19014 (2012).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Borisov, V. et al. Deep Neural Networks and Tabular Data: A Survey. IEEE Trans. Neural Netw. Learn. Syst. 1–21 (2022).

Statista. Statista Consumer Market Outlook: Beer - Worldwide.

Seitz, H. K. & Stickel, F. Molecular mechanisms of alcoholmediated carcinogenesis. Nat. Rev. Cancer 7 , 599–612 (2007).

Voordeckers, K. et al. Ethanol exposure increases mutation rate through error-prone polymerases. Nat. Commun. 11 , 3664 (2020).

Goelen, T. et al. Bacterial phylogeny predicts volatile organic compound composition and olfactory response of an aphid parasitoid. Oikos 129 , 1415–1428 (2020).

Article   ADS   Google Scholar  

Reher, T. et al. Evaluation of hop (Humulus lupulus) as a repellent for the management of Drosophila suzukii. Crop Prot. 124 , 104839 (2019).

Stein, S. E. An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data. J. Am. Soc. Mass Spectrom. 10 , 770–781 (1999).

American Society of Brewing Chemists. Sensory Analysis Methods. (American Society of Brewing Chemists, St. Paul, MN, U.S.A., 1992).

McAuley, J., Leskovec, J. & Jurafsky, D. Learning Attitudes and Attributes from Multi-Aspect Reviews. Preprint at (2012).

Meilgaard, M. C., Carr, B. T. & Carr, B. T. Sensory Evaluation Techniques. (CRC Press, Boca Raton). (2014).

Schreurs, M. et al. Data from: Predicting and improving complex beer flavor through machine learning. Zenodo (2024).

Download references


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

Author information

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

Authors and Affiliations

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

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

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

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

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

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

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

Łukasz Kreft & Alexander Botzki

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

Philippe Malcorps & Luk Daenen

You can also search for this author in PubMed   Google Scholar


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

Corresponding author

Correspondence to Kevin J. Verstrepen .

Ethics declarations

Competing interests.

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

Peer review

Peer review information.

Nature Communications thanks Florian Bauer, Andrew John Macintosh and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, peer review file, description of additional supplementary files, supplementary data 1, supplementary data 2, supplementary data 3, supplementary data 4, supplementary data 5, supplementary data 6, supplementary data 7, reporting summary, source data, source data, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit .

Reprints and permissions

About this article

Cite this article.

Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024).

Download citation

Received : 30 October 2023

Accepted : 21 February 2024

Published : 26 March 2024


Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

  • Explore articles by subject
  • Guide to authors
  • Editorial policies

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

how to write a chemistry research paper

2024 Enhancing Lives Showcase: Meet the Makers

Admin options.

UCF’s researchers don’t just produce research papers, posters, and conference presentations: they also write poetry and stories, draw, paint, build, film, design, code, sew, shape, and more! The first annual Creative Scholarship Showcase will highlight some of the creative scholarship, artistic pieces, and new production of UCF students from a variety of disciplines.  

Meet the Makers and Mentors  

Come and meet the artists at the Morgridge International Reading Center! This event will feature a brief introduction. Then each student will stand by their piece and discuss their creation with the members of the audience as they come by.  Event Details below


  1. How To Write A Chemistry Research Paper? All Details

    how to write a chemistry research paper

  2. 😍 Chemistry green and clean essay. Green Chemistry Research Paper. 2019

    how to write a chemistry research paper

  3. How To Write A Chemistry Research Paper? All Details

    how to write a chemistry research paper

  4. Ace How To Write A Good Lab Report Chemistry Writing Format According Cbse

    how to write a chemistry research paper

  5. Biographical research paper sample, format for writing a chemistry l

    how to write a chemistry research paper

  6. 😂 Chemistry research papers. Chemistry Research Paper. 2019-02-01

    how to write a chemistry research paper



  2. How to Write Chemistry Board Exam

  3. how to write chemistry practical record for intermediate students 2024

  4. How to write chemistry project work


  6. How to write Chemistry practical Record and Project work


  1. Guide for Writing in Chemistry

    Research Paper A research paper is the most important type of writing in chemistry and comprises the bulk of primary literature in the discipline. Research papers afford the author the opportunity to communicate original research conducted in the laboratory, rigorously documenting the results. Most laboratory reports are shortened

  2. A Brief Guide to Writing in Chemistry

    order to appreciate the formatting and writing style of research reports in the field of chemistry. Formatting a Report Layout Use 12-point Times New Roman font and double spacing to allow space for comments and corrections. Number all pages, including those in appendices. Organization A standard lab report or research paper should be ...

  3. How to write your article

    For help structuring and formatting your whole manuscript, choose one of these article templates. For detailed information on acceptable formats for your figures, visit our section on Figures, graphics, images & cover artwork. For a quick reference checklist to help you prepare a high quality article, download our 'How to publish' guide.

  4. 4 Keys to Writing a Highly-Read Chemistry Research Paper

    A Highly-Read Chemistry Research Paper Tells a Story. Your paper should be about more than data points and methods if you want to attract more readers. Your research needs to tell a story that will grab and hold readers' attention. "A paper should have a gripping narrative about why you undertook this piece of research.

  5. Your chemical science thesis: an introductory guide to writing up your

    This guide aims to give you guidance on how to write your thesis so. that your research is showcased at its best. It includes suggestions on how to prepare for writing up and things to consider during the final stages. Whether you're researching a new synthetic route to a natural product or applying computational methods to a chemical problem ...

  6. A guide to research question writing for undergraduate chemistry

    Welcome to chemistry education research Many chemistry degree programmes offer the opportunity for students to undertake a chemistry education research project as part of their final year degree, and inclusion of chemistry education as a specialism has long been part of, for example, the Royal Society of Chemistry Accreditation of Degree Programmes guidance ().

  7. PDF Preparing a Research Report

    This book addresses all aspects of scientific writing. The book provides a structured approach to writing a journal article, conference abstract, scientific poster and research proposal. The approach is designed to turn the complex process of writing into graduated, achievable tasks. Last revised in August 2015.

  8. How to Write Better Papers and Get Published in Chemistry Europe Journals

    Place essential findings and keywords in the first two sentences of your abstract. Only the first two sentences normally display in search engine results. Repeat your keywords 3-6 times. Don't forget the purpose of your abstract is to clearly and concisely express the key points of your research.

  9. Scientific Writing

    It is critical to understand the important elements of a scientific paper and how to most effectively describe research results in the context of your manuscript. While writing your paper can seem like an afterthought compared to years of work in the lab, the way you convey your findings can have a profound impact on editors, reviewers, and ...

  10. How to Write an Effective Chemistry Research Paper (Part 2)

    Example. I filtered the solution and noticed production of a yellow powder. ︎ Filtration of the solution, yielded a yellow powder. However, when referring to your own results or conclusions, it is better to use the first or second person. Example. While AB et al. report X value, the authors' data indicates Y value.

  11. PDF Writing the research proposal: Chemistry 419/519

    Invention ideas: originality. Writing is social: talk to others 1. Extrapolate from existing papers 2. Combine ideas from two existing papers in the area 3. Build on existing techniques—improve them 4. Apply a technique from one area to another area 5. Switch techniques while examining the same biological system.

  12. Research paper Writing a scientific article: A step-by-step guide for

    We describe here the basic steps to follow in writing a scientific article. We outline the main sections that an average article should contain; the elements that should appear in these sections, and some pointers for making the overall result attractive and acceptable for publication. 1.

  13. Writing a Research Paper or Lab Report

    Adapted from information found in Chapter 2 of the ACS Style Guide. Additional resources and information on each sections are also provided from the journal Clinical Chemistry from the section of their journal "Guide To Scientific Writing." Click on the title for a direct link to the PDF or use the corresponding citation for each article to view the online version.

  14. Chemistry Writing Guide

    Chemistry papers should be written in passive voice (unless you receive other instructions from your professor). Abbreviations or acronyms must be explained the first time they are used. Figures, graphs, and tables must be titled and referenced in the text. References (including textbooks and lab manuals) must be cited and numbered ...

  15. Research Guides: Chemistry :: Peppers: APA style

    If your'e wondering how to insert a page header, adjust margins, and indent paragraphs, the following videos, created by Purdue Online Writing Lab (OWL), may be helpful: MATC Health: Inserting Headers & Page Numbers with Word 2013 (2:23) Purdue OWL: APA Formatting - The Basics (4:47) Purdue OWL: APA Formatting - Reference List (3:18)

  16. The Art of Writing the Title of Your Paper

    With respect to the title, however, there is no such luxury: We can have only one title. A title should be composed of a maximum of 20 words, and these 20 words need to convey enough information to encourage a reader to click on your paper, download it, and read it. We strongly believe that the science should speak for itself and that the use ...

  17. How to Write a Research Paper

    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.

  18. CSI Library: Chemistry: Writing a Research Paper

    Scientific Writing by Jean Luc Lebrun The book helps scientists write papers for scientific journals. Using the key parts of typical scientific papers (Title, Abstract, Introduction, Visuals, Structure, and Conclusions), it shows through numerous examples, how to achieve the essential qualities required in scientific writing, namely being clear, concise, convincing, fluid, interesting, and ...

  19. Writing Effective Review Articles

    Originality is defined by the scope of the review article and commonly takes the form of a topic or a research question. For example, one may write a topical review on capping agents in nanocrystal syntheses or a question-based review on how shape-control is achieved in nanocrystal syntheses. ... Our Most Downloaded Papers Published in 2021 ...

  20. How to Review a Paper

    A thorough reviewer would check for the relevant recent publications. Use your judgment to evaluate if authors did not overstate the importance of their work. It is better to approach the claims of importance critically, especially if they are not directly related to the topic of the paper.

  21. Citing Sources in Chemistry

    College Policy on Citing Sources & Plagiarism. It is necessary for you to give proper credit to all of the resources you use in your research papers. Plagiarism is a violation of Dickinson's Student Code of Conduct, and is a specific form of cheating defined in the code as follows: 1) To plagiarize is to use without proper citation or ...

  22. How to Write a Chemistry Research Paper

    How to Start a Chemistry Research Paper. Starting a chemistry thesis may seem complicated, but if you carefully plan and schedule the entire process, you will have an easy time. After topic selection, you need to conduct a brainstorming session to reflect on your topic question. Identify the objective of the analysis and the issues you want to ...

  23. How to Write a Research Paper Introduction (with Examples)

    Define your specific research problem and problem statement. Highlight the novelty and contributions of the study. Give an overview of the paper's structure. The research paper introduction can vary in size and structure depending on whether your paper presents the results of original empirical research or is a review paper.

  24. Developing a Research Question

    When brainstorming your research question, let your mind veer toward connections or associations that you might have already considered or that seem to make sense and consider if new research terms, language or concepts come to mind that may be interesting or exciting for you as a researcher. Sometimes testing out a research question while ...

  25. Predicting and improving complex beer flavor through machine ...

    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 ...

  26. Get Best Research Paper Writing Assistance. Contact us ...

    1 likes, 3 comments - on January 29, 2024: "Get Best Research Paper Writing Assistance. Contact us: 6268991983 Mail us: [email protected] Visit ...

  27. 2024 Enhancing Lives Showcase: Meet the Makers

    UCF's researchers don't just produce research papers, posters, and conference presentations: they also write poetry and stories, draw, paint, build, film, design, code, sew, shape, and more! The first annual Creative Scholarship Showcase will highlight some of the creative scholarship, artistic pieces, and new production of UCF students ...