research methodology and scientific writing ppt

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A COURSE IN RESEARCH METHODOLOGY 2018.pptx

This teaching paper is an introdcution to the field of research methodology as it enables beginners (students) to understand basic things about research, research techniques , research design and research procedure. The general aim behind this teaching paper is to facilitate the task of students to tackle this complicated field with confidence and ease.It covers a lot of courses and it can be taught to different levels of students: BA, MA and even PHd students.

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Wafae Barkani

Xochitl Ortiz

The authors felt during their several years of teaching experience that students fail to understand the books written on Research Methodology because generally they are written in technical language. Since this course is not taught before the Master’s degree, the students are not familiar with its vocabulary, methodology and course contents. The authors have made an attempt to write it in very non- technical language. It has been attempted that students who try to understand the research methodology through self-learning may also find it easy. The chapters are written with that approach. Even those students who intend to attain high level of knowledge of the research methodology in social sciences will find this book very helpful in understanding the basic concepts before they read any book on research methodology. This book is useful those students who offer the Research Methodology at Post Graduation and M.Phil. Level. This book is also very useful for Ph.D. Course Work examinations.

Anil Jharotia

Research is an important activity of any nation and societies for generating the information to its developments. Robust collection of qualitative information helps in the development of the any nations. Research & Development is an important tool for acquiring new knowledge in any field of human survival. Various type of problems and questions need to use research methodology depend on the rationale of researchers. How to use the research for finding answers of any research questions/problems.

https://www.ijrrjournal.com/IJRR_Vol.6_Issue.3_March2019/Abstract_IJRR0011.html

International Journal of Research & Review (IJRR)

Research methodology is a way to systematically solve the research problem. It may be understood as a science of studying how research is done scientifically. In it we study the various steps that are generally adopted by a researcher in studying his research problem along with the logic behind them. It is necessary for the researcher to know not only the research methods/techniques but also the methodology. Researchers not only need to know how to develop certain indices or tests, how to calculate the mean, the mode, the median or the standard deviation or chi-square, how to apply particular research techniques, but they also need to know which of these methods or techniques, are relevant and which are not, and what would they mean and indicate and why. Researchers also need to understand the assumptions underlying various techniques and they need to know the criteria by which they can decide that certain techniques and procedures will be applicable to certain problems and others will not. All this means that it is necessary for the researcher to design his methodology for his problem as the same may differ from problem to problem.

Scholarly Communication and the Publish or Perish Pressures of Academia A volume in the Advances in Knowledge Acquisition, Transfer, and Management (AKATM) Book Series

Dr. Naresh A . Babariya , Alka V. Gohel

The most important of research methodology in research study it is necessary for a researcher to design a methodology for the problem chosen and systematically solves the problem. Formulation of the research problem is to decide on a broad subject area on which has thorough knowledge and second important responsibility in research is to compare findings, it is literature review plays an extremely important role. The literature review is part of the research process and makes a valuable contribution to almost every operational step. A good research design provides information concerning with the selection of the sample population treatments and controls to be imposed and research work cannot be undertaken without sampling. Collecting the data and create data structure as organizing the data, analyzing the data help of different statistical method, summarizing the analysis, and using these results for making judgments, decisions and predictions. Keywords: Research Problem, Economical Plan, Developing Ideas, Research Strategy, Sampling Design, Theoretical Procedures, Experimental Studies, Numerical Schemes, Statistical Techniques.

Hafizi Saari

Dr. Moses Gweyi

This book is the outcome of more than four decades of experience of the author in teaching and research field. Research is a creative process and the topic of research methodology is complex and varied. The basic premise for writing this book is that research methods can be taught and learnt. The emphasis is on developing a research outlook and a frame of mind for carrying out research. The book presents current methodological techniques used in interdisciplinary research along with illustrated and worked out examples. This book is well equipped with fundamentals of research and research designs. All efforts have been made to present Research, its meaning, intention and usefulness. Focussed in designing of research programme, selection of variables, collection of data and their analysis to interpret the data are discussed extensively. Statistical tools are complemented with examples, making the complicated subject like statistics simplest usable form. The importance of software, like MS Excel, SPSS, for statistical analyses is included. Written in a simple language, it covers all aspects of management of data with details of statistical tools required for analysis in a research work. Complete with a glossary of key terms and guides to further reading, this book is an essential text for anyone coming to research for the first time and is widely relevant across the disciplines of sciences. This book is designed to introduce Masters, and doctoral students to the process of conducting scientific research in the life sciences, social sciences, education, public health, and related scientific disciplines. It conforms to the core syllabus of many universities and institutes. The target audience for this book includes those are going to start research as graduate students, junior researchers, and professors teaching courses on research methods. The book entitled “A guide to Research Methodology for Beginners” is succinct and compact by design focusing only on essential concepts rather than burden students with a voluminous text on top of their assigned readings. The book is structured into the following nine chapters. Chapter-1: What is Scientific Research? Chapter-2: Literature Review Chapter-3: How to develop a Research Questions & Hypotheses Chapter-4: Research Methods and the Research Design Chapter-5: Concept of Variables, Levels and Scales of Measurements for Data collection Chapter-6: Data Analysis, Management and Presentation Chapter-7: Tips for Writing Research Report Chapter-8: Glossary Related to Research Methodology Chapter-9: References It is a comprehensive and compact source for basic concepts in research and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. The target audience for this book includes those are going to start research as graduate students, junior researchers, and professors teaching courses on research methods.

Yuanita Damayanti

Khamis S Moh'd

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Education Standards

Radford university.

Learning Domain: Social Work

Standard: Basic Research Methodology

Lesson 10: Sampling in Qualitative Research

Lesson 11: qualitative measurement & rigor, lesson 12: qualitative design & data gathering, lesson 1: introduction to research, lesson 2: getting started with your research project, lesson 3: critical information literacy, lesson 4: paradigm, theory, and causality, lesson 5: research questions, lesson 6: ethics, lesson 7: measurement in quantitative research, lesson 8: sampling in quantitative research, lesson 9: quantitative research designs, powerpoint slides: sowk 621.01: research i: basic research methodology.

The twelve lessons for SOWK 621.01: Research I: Basic Research Methodology as previously taught by Dr. Matthew DeCarlo at Radford University. Dr. DeCarlo and his team developed a complete package of materials that includes a textbook, ancillary materials, and a student workbook as part of a VIVA Open Course Grant.

The PowerPoint slides associated with the twelve lessons of the course, SOWK 621.01: Research I: Basic Research Methodology, as previously taught by Dr. Matthew DeCarlo at Radford University. 

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Research Methodology and Scientific Writing

• C. George Thomas 0

Kerala Agricultural University, Thrissur, India

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• Provides tips to improve the writing skills for research students
• Deals with most interdisciplinary fields in Research such as Problems, Writing Proposals, Funding, Selecting Designs, Literature and Review, Collection of Data and Analysis, and Preparation of Thesis
• Discusses the latest on the use of information technology in retrieving and managing information

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Table of contents (24 chapters)

Front matter, research: the search for knowledge.

C. George Thomas

Philosophy of Research

Approaches to research, major research methods, experimental research, collection and analysis of data, planning and writing a research proposal, publications and the library, academic databases, the literature review, preparation of research papers and other articles, the structure of a thesis, tables and illustrations, reasoning in research, references: how to cite and list correctly, improve your writing skills, use appropriate words and phrases, punctuation marks and abbreviations, units and numbers.

• Research Problems
• Writing Proposals
• Selecting Designs
• Literature and Review
• Collection of Data and Analysis
• Preparation of Thesis

Book Title : Research Methodology and Scientific Writing

Authors : C. George Thomas

DOI : https://doi.org/10.1007/978-3-030-64865-7

Publisher : Springer Cham

eBook Packages : Education , Education (R0)

Copyright Information : The Author(s) 2021

Hardcover ISBN : 978-3-030-64864-0 Published: 25 February 2021

Softcover ISBN : 978-3-030-64867-1 Published: 25 February 2022

eBook ISBN : 978-3-030-64865-7 Published: 24 February 2021

Edition Number : 2

Number of Pages : XVII, 620

Number of Illustrations : 25 b/w illustrations

Topics : Engineering/Technology Education , Writing Skills , Thesis and Dissertation

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Lecture Notes on Research Methodology

Published by Eileen Garrison Modified over 6 years ago

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Presentation on theme: "Lecture Notes on Research Methodology"— Presentation transcript:

Introduction to Research Methodology

Sabine Mendes Lima Moura Issues in Research Methodology PUC – November 2014.

Today Concepts underlying inferential statistics

Richard M. Jacobs, OSA, Ph.D.

Research Methodology Lecture 1.

Chapter 12 Inferential Statistics Gay, Mills, and Airasian

Sample Design.

Copyright © 2008 by Pearson Education, Inc. Upper Saddle River, New Jersey All rights reserved. John W. Creswell Educational Research: Planning,

Magister of Electrical Engineering Udayana University September 2011

Chapter 1: Introduction to Statistics

RESEARCH A systematic quest for undiscovered truth A way of thinking

Research Methodology.

Educational Research: Competencies for Analysis and Application, 9 th edition. Gay, Mills, & Airasian © 2009 Pearson Education, Inc. All rights reserved.

Research Seminars in IT in Education (MIT6003) Quantitative Educational Research Design 2 Dr Jacky Pow.

PROCESSING OF DATA The collected data in research is processed and analyzed to come to some conclusions or to verify the hypothesis made. Processing of.

Academic Research Academic Research Dr Kishor Bhanushali M

Question paper 1997.

Chapter 6: Analyzing and Interpreting Quantitative Data

Module III Multivariate Analysis Techniques- Framework, Factor Analysis, Cluster Analysis and Conjoint Analysis Research Report.

Chapter 7 Measuring of data Reliability of measuring instruments The reliability* of instrument is the consistency with which it measures the target attribute.

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[Updated 2023] Top 20 PowerPoint Templates to Devise a Systematic Research Methodology

Kritika Saini

Developing a systematic research methodology is essential for conducting effective investigations. It ensures clarity, rigor, validity, replicability, ethical integrity, and efficiency in the research process. It serves as a roadmap that guides researchers through the study, enabling them to generate reliable findings and contribute to the advancement of knowledge in their respective fields.

Research Methodology Templates to Conduct Rigorous and Reliable Research

By following a well-structured approach, you can enhance the efficiency of your research and produce meaningful results. Therefore, SlideTeam brings you a collection of content-ready and custom-made PPT templates to help you save time by providing pre-designed structures and frameworks for research methodologies. You can customize these templates to fit your specific projects, eliminating the need to create a methodology from scratch. 

This time-saving aspect allows you to focus more on the actual research process. Secondly, these ready-made templates provide you with consistency and standardization in methodologies. They ensure that essential elements are included and organized in a logical manner, making it easier for readers and reviewers to understand and evaluate the research. They also serve as a helpful guide, ensuring that researchers cover all necessary components and follow best practices. They provide a clear and structured format for learning about research methodologies and help researchers develop a systematic approach to their work. Overall, research methodology templates streamline the process, enhance consistency, and serve as educational resources for researchers at various levels of expertise.

Browse the collection below and ensure that your methodology is comprehensive and well-written. 

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Accelerate your business research endeavors with our business research methodology proposal template. This comprehensive e template provides a solid framework for crafting a well-structured and persuasive research proposal. Streamline the proposal development process by leveraging our template's pre-designed sections, including problem statement, research objectives, methodology, timeline, and budget. Present your proposal with confidence, knowing that you have followed a proven format and incorporated essential elements. Take your business research to the next level with our business research methodology proposal template. 

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FAQs on Research Methodology

What are the four types of research methodology.

The four types of research methodology commonly used in academic and scientific studies are:

Descriptive Research: This type aims to describe and document the characteristics, behavior, and phenomena of a particular subject or population. It focuses on gathering information and providing an accurate portrayal of the research topic.

Experimental Research: This approach involves the manipulation and control of variables to establish cause-and-effect relationships. It often includes the use of control groups and random assignment to test hypotheses and draw conclusions.

Correlational Research: This methodology examines the statistical relationship between two or more variables without direct manipulation. It aims to identify patterns and associations between variables to understand their degree of relationship.

Qualitative Research: This approach focuses on exploring and understanding the subjective experiences, perspectives, and meanings attributed by individuals or groups. It involves methods such as interviews, observations, and analysis of textual or visual data to uncover insights and interpretations.

What are the 3 main methodological types of research?

The three main methodological types of research are:

Quantitative Research: This approach involves the collection and analysis of numerical data to uncover patterns, relationships, and statistical trends. It focuses on objective measurements, often utilizing surveys, experiments, and statistical analysis to quantify and generalize findings.

Qualitative Research: This methodology aims to understand the subjective experiences, meanings, and social contexts associated with a research topic. It relies on non-numerical data, such as interviews, observations, and textual analysis, to explore in-depth perspectives, motivations, and behavior.

Mixed-Methods Research: This type of research integrates both quantitative and qualitative approaches, combining the strengths of both methodologies. It involves collecting and analyzing both numerical and non-numerical data to gain a comprehensive understanding of the research problem. Mixed-methods research can provide a more nuanced picture by capturing both statistical trends and rich contextual information.

What are the 7 basic research methods?

There are several research methods commonly used in academic and scientific studies. While the specific categorization may vary, here are seven basic research methods:

Experimental Research: Involves controlled manipulation of variables to establish cause-and-effect relationships.

Survey Research: Utilizes questionnaires or interviews to collect data from a sample population to gather insights and opinions.

Observational Research: Involves systematic observation of subjects in their natural environment to gather qualitative or quantitative data.

Case Study Research: In-depth analysis of a particular individual, group, or phenomenon to gain insights and generate detailed descriptions.

Correlational Research: Examines the statistical relationship between variables to identify patterns and associations.

Qualitative Research: Focuses on understanding subjective experiences, meanings, and social contexts through interviews, observations, and textual analysis.

Action Research: Involves collaboration between researchers and participants to address real-world problems and generate practical solutions.

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Research Methodology: Writing a Scientific Research Proposal

Oct 14, 2014

450 likes | 1.42k Views

Research Methodology: Writing a Scientific Research Proposal. Dean Sherzai MD, MAS Ayesha Z. Sherzai, MD, MAS. Writing a research proposal. Formulating a research problem. Conceptualize research design. Selecting a sample. Collecting and processing data. Introduction. A Quick Glance.

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Research Methodology: Writing a Scientific Research Proposal • Dean Sherzai MD, MAS • Ayesha Z. Sherzai, MD, MAS

Writing a research proposal Formulating a research problem Conceptualize research design Selecting a sample Collecting and processing data Introduction A Quick Glance

A research proposal has three main points: • Explanation of proposed research (what will be done) • Methods and techniques to be employed (how it will be done) • Novelty and importance of the study (why it should be done)

Organization • Title • Abstract • Introduction and Review • Research Hypothesis • Material and Methods • Conclusion and Justification • Bibliography

Title • Provide a specific summary of the proposed work • Minimal words, clear language

Abstract • One paragraph • Description of the hypothesis and the goals of the experiment • Readers can quickly assess the basic premise of your proposal

Introduction • Say it in the first paragraph! Readers can be impatient • “In the proposed study, we seek to examine...”

Review of the literature • Lengthiest part of proposal • Begin with basics, narrow the focus to pertinent proposed work • Use plenty of primary sources of information: Textbooks, journal articles, website (with caution!) • Cite appropriately

Identification of the knowledge gap • State what we do not know from reading the existing fund of knowledge • Justification for starting the project

Research Hypothesis • What is the hypothesis that you are testing? • What are the questions that you seek to answer? • Based on what is known in this field, explain what you expect to see and hope to show through your result

Material and Methods • Dictated by the nature of your research • Describe your data source, process of collection in detail • Describe the statistical analysis to be used, limitations

Conclusion and Justification • Explicitly state how your proposed research will advance knowledge • What are the far-reaching effects? Will your study potentially change practices or policies? Why is it that your research deserves funding?

Bibliography • Include all the resources that were used in the writing of the paper • Follow your guidelines for formatting

Bibliography • Example: • Agosti C, Borroni B, Akkawi NM, Bordonali T, Padovani A. Acute myocardial infarction presenting with transient global amnesia. J Am Geriatr Soc. 2006; 6: 1004. • Jain S, Ton TG, Boudreau RM, et al. The Risk of Parkinson Disease Associated with Urate in a Community-Based Cohort of Older Adults. Neuroepidemiology. 2011; 4: 223-229.

A note on sources, paraphrasing and citations • Terse, clearcut, no artistic enhancement • Avoid quoting directly • Read the article, put it down, write it in your own words • Citations: Author, year

A note on voice • Active voice (“I” or “We”) • Switch between active and passive voice to avoid repetition

Important Points • Organized, well-written, concise, complete proposal = easier to conduct experiment • Good writing when paired with a thorough understanding of the subject matter is a valuable skill to possess

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Writing a Research Proposal

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WRITING THE RESEARCH PROPOSAL

WRITING THE RESEARCH PROPOSAL. Research process Writing the proposal Session1. Write Research Proposal. Research process. Topic should be: Appealing Interesting Manageable Beneficial BUT not controversial or theory-generating PHRASING

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Writing the Research Proposal

CHAPTER SIX. Writing the Research Proposal. Thought Experiment. You are a VT researcher and are pondering a new research project. What are the consequences if: You DO the project. You DO NOT do the project. Thought Experiment on Research. Capital Assets:

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Tips for Scientific Writing & Presentation

The following resources will assist students with scientific writing and presentation skills including how to write an abstract and create a poster for a conference.

Abstract Guidelines

• Abstract Guidelines for the Medical Student Research Symposium

BU Authorship Guidelines

• What does it mean to be an author?

Manuscript Tips

• Me write pretty one day: how to write a scientific paper
• Improving your scientific writing: a short guide
• Online writing lab
• Bibliography software (Zotero has a lot of advantages (including that it is free).

Poster Tips

• Tips on Poster Design & Presentation
• Presentation Skills Toolkit for Medical Students (resources on developing and delivering formal lectures and presentations, poster and oral abstract presentations, patient presentations, and leading small group sessions)
• Conference presentations: Lead the poster parade
• Posters and Presentations (BU Alumni Medical Library—template here)
• Poster Template 2023 (template to create your poster; includes BMC and BU logos)

Printing Your Poster

• Fedex (700 Albany St., Boston) will print posters at a discount (tell them you are from BU). Most posters are printed on photo satin paper and can be emailed as a PDF to this Fedex location using the following email: [email protected] .
• PhD Posters
• Poster Presentations
• Posters should be created as a PowerPoint file at final size (check your meeting for dimensions; 4′ wide x 3′ tall is common).

Presentation Tips

• Three tips for giving a great research talk
• How to Speak (Patrick Winston’s famous MIT lecture)

If you have found other great resources please let us know .

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• Open access
• Published: 03 April 2024

Perception, practice, and barriers toward research among pediatric undergraduates: a cross-sectional questionnaire-based survey

• Canyang Zhan 1 &
• Yuanyuan Zhang 2  

BMC Medical Education volume  24 , Article number:  364 ( 2024 ) Cite this article

160 Accesses

Metrics details

Scientific research activities are crucial for the development of clinician-scientists. However, few people pay attention to the current situation of medical research in pediatric medical students in China. This study aims to assess the perceptions, practices and barriers toward medical research of pediatric undergraduates.

This cross-sectional study was conducted among third-year, fourth-year and fifth-year pediatric students from Zhejiang University School of Medicine in China via an anonymous online questionnaire. The questionnaires were also received from fifth-year students majoring in other medicine programs [clinical medicine (“5 + 3”) and clinical medicine (5-year)].

The response rate of pediatric undergraduates was 88.3% (68/77). The total sample of students enrolled in the study was 124, including 36 students majoring in clinical medicine (“5 + 3”) and 20 students majoring in clinical medicine (5-year). Most students from pediatrics (“5 + 3”) recognized that research was important. Practices in scientific research activities are not satisfactory. A total of 51.5%, 35.3% and 36.8% of the pediatric students participated in research training, research projects and scientific article writing, respectively. Only 4.4% of the pediatric students contributed to publishing a scientific article, and 14.7% had attended medical congresses. None of them had given a presentation at a congress. When compared with fifth-year students in the other medicine program, the frequency of practices toward research projects and training was lower in the pediatric fifth-year students. Lack of time, lack of guidance and lack of training were perceived as the main barriers to scientific work. Limited English was another obvious barrier for pediatric undergraduates. Pediatric undergraduates preferred to participate in clinical research (80.9%) rather than basic research.

Conclusions

Although pediatric undergraduates recognized the importance of medical research, interest and practices in research still require improvement. Lack of time, lack of guidance, lack of training and limited English were the common barriers to scientific work. Therefore, research training and English improvement were recommended for pediatric undergraduates.

Peer Review reports

Medical education includes the learning of basic clinical medical knowledge and the cultivation of scientific research abilities. Scientific research, an essential part of medical education, is increasingly important, as it can greatly improve medical care [ 1 , 2 ]. Scientific research activities are crucial for the development of clinician-scientists, who have key roles in clinical research and translational medicine. Therefore, medical education is increasingly emphasizing the cultivation of scientific research abilities. Strengthening scientific research training helps students to develop independent critical thinking, improve the ability of observation, and foster the problem-solving skills. It is suggested that developing undergraduate research benefits the students, the faculty mentors, the university or institution, and eventually society [ 2 , 3 ]. As a result, there is a growing trend to integrate scientific research training into undergraduate medical education. Early exposure to scientific research was recommended in undergraduate medical students [ 4 , 5 ]. In fact, an international questionnaire study showed that among 1625 responses collected from 38 countries, less than half (42.7%) agree/strongly agree that their medical schools provided “sufficient training in medical research” [ 6 ]. The training or practices about medical research in undergraduates is not universal. In China, few people pay attention to the current situation of medical research in undergraduates, especially for pediatric medical students.

Due to changes in China’s birth policy (two-child policy in 2016 and the three-child policy in 2021), child health needs are increasing [ 7 ]. The shortage of pediatricians is alarming in China. Therefore, numerous policies have been implemented to meet the challenges of the shortage of pediatricians, including reinstating pediatrics as an independent discipline in medical school enrollment and increasing the enrollment of pediatrics. The number of pediatricians has increased year by year. The number of pediatricians in China increased from 118,500 in 2015 (0.52 pediatricians per 1000 children under the age of 14) to 206,000 in 2021 (0.78 pediatricians per 1000 children under the age of 14). With the increase in pediatric enrollment, pediatric medical education is facing new challenges. It is urgent to study the current situation of cultivation of pediatric medical students, one of which is the scientific research abilities [ 8 , 9 ]. However, as the particular background of pediatrics, very little is known about the perception, practice and barriers toward medical research in pediatric undergraduates. The purpose of this study was to address the gap by assessing the practices, perceptions and barriers toward medical research of pediatric undergraduates at Zhejiang University. The results can help to improve the mode of cultivating scientific research abilities among pediatric medical students.

The study was conducted from March to April 2023. The study was approved by the Ethics Review Committee of the Children’s Hospital of Zhejiang University School of Medicine and was undertaken according to the Helsinki declaration. Participants provided written informed consent upon applying to participate in the study.

Study design and setting

This is a cross-sectional study conducted via an online questionnaire and the questionnaire was done simultaneously in all students. The study aimed to investigate the perception, practices and barriers toward research in pediatric undergraduates from Zhejiang University School of Medicine, and to investigate the differences in research among undergraduate students from clinical medicine (“5 + 3” integrated program, pediatrics) [pediatrics (“5 + 3”)], clinical medicine (“5 + 3” integrated program) [clinical medicine (“5 + 3”)] and clinical medicine (5-year).

The clinical medicine of Zhejiang University School of Medicine (ZUSM) includes a 5-year program, a “5 + 3” integrated program, and a 8-year MD. Program. The clinical medicine (5-year) program is the basis of clinical medicine education.Graduates need to complete 3 years of standardized residency training to become doctors. The clinical medicine (“5 + 3”) model combines the 5-year medical undergraduate education, 3-year standardized residency training and postgraduate education. Since 2015, 20 to 30 students who are interested in pediatrics were selected from second-year undergraduate students of clinical medicine (“5 + 3”) to continue studies as pediatrics (“5 + 3”) every year. Since 2019, ZUSM established pediatrics (“5 + 3”) program. 20–30 students have been enrolled independently every year.

Participants

All of the third-, fourth-, and fifth-year undergraduate students in pediatrics (“5 + 3”) and some of the fifth-year undergraduate students from clinical medicine (“5 + 3”) and clinical medicine (5-year) who expressed an interest in participating in the study were enrolled.

Data collection

The questionnaire was self-designed after reviewing the literature and consulting senior faculty. For the purpose of testing its clarity and reliability, the questionnaire was pilot tested among 36 undergraduate students. Their feedback was mainly related to the structure of the questionnaire. To address these comments, the questionnaire was modified to reach the final draft, which was distributed to the student sample included in the study. The reliability coefficient was assessed by Cronbach’s alpha, and the validity was evaluated by Kaiser-Meyer-Olkin (KMO).

There are four sections of the questionnaire used in this study:

The first part covered 3 statements (gender, grade and major).

The second part examined the participants’ perceptions of medical research, including 5 statements (importance, enhancement of competitiveness, practising thinking ability, solving clinical problems, and being interesting).

The third part examined practices in medical research, including 6 statements (project, training, write paper, publish paper, attend academic conference and conference communication).

The barriers to medical research were assessed in the last part, including 7 statements.

Perception and barriers toward medical research were evaluated using a five-point Likert scale ranging from 1 to 5 (1 = strongly disagree; 2 = disagree, 3 = uncertain, 4 = agree, 5 = strongly agree).

Statistical analysis

Categorical data are represented as numbers and frequencies. For ease of reporting and analyzing data, the responses of “agree” and “strongly agree” were grouped and reported as agreements, and “disagree” and “strongly disagree” were grouped as disagreements. The chi-square test was used to test the difference in the frequency of participation in research practices. The student’s perception score based on grades was analyzed using Fisher’s exact test, and attitude between the year of study was analyzed by ANOVA or a nonparametric test (Kruskal-Wallis H test). The statistical analysis was performed using IBM SPSS version 26. P  < 0.05 was considered significant.

The reliability coefficient of the questionnaire was assessed by Cronbach’s alpha; it was 0.73 for perception and 0.78 for barriers. KMO was 0.80 for perception (Bartlett’s sphericity test: χ2 = 200.4, p  < 0.001) and 0.73 for barriers (Bartlett’s sphericity test: χ2 = 278.4, p  < 0.001), indicating the appropriateness of the factor analysis. The factor analysis was carried out using the principal component analysis with varimax rotation. For perception, one factor explains 58.2% of the variance. For barriers, two-factor solution explains 60.2% of the variance.

The response rate was 79.2% (19/24) in the third year, 88% (22/25) in the fourth year and 96.4% (27/28) in the fifth year students in pediatrics (“5 + 3”), and the total response rate was 88.3% (68/77). The number of fifth-year students majoring in clinical medicine (“5 + 3”) and clinical medicine (5-year) was 36 and 20, respectively. Thus, a total of 124 students participated in the questionnaire. Among the participants, approximately 46% were male and 54% were female.

Perception regarding scientific research among the students majoring in pediatrics (“5 + 3”)

The majority of students in pediatrics (“5 + 3”) recognized that research was important (92.6%), such as increasing competitiveness, solving clinical problems and improving thinking (Fig.  1 ). Approximately half of the students in pediatrics (“5 + 3”) were interested in the research.

Perception regarding scientific research among the students majoring in pediatrics

Among the third-, fourth-, and fifth-year students in pediatrics (“5 + 3”), there was a significant difference in the effect of research on thinking ability (Table  1 ). A stronger understanding of the importance of research for thinking abilities was found in students from the fifth year.

Comparing the perception of medical research among the fifth-year students from the different medicine programs, there was a significant difference in the interest in research (Table  2 ). The fifth-year undergraduates from clinical medicine (5-year) received the highest score for interest in scientific research, followed by pediatrics (“5 + 3”).

Practices regarding scientific research among students majoring in pediatrics (“5 + 3”)

More than half of the students in pediatrics (“5 + 3”) participated in research training. Approximately 36.8% of them were involved in writing scientific articles, and 35.3% participated in research projects (Table  3 ). Only 4.4% of the students in pediatrics (“5 + 3”) contributed to publishing a scientific article, and 14.7% of the students in pediatrics (“5 + 3”) had attended medical congresses. However, none of the students had made a presentation at congresses.

A statistically significant difference was observed among different grades in the pediatrics (“5 + 3”) program, with fifth-year students having a much higher rate of participation in conferences. However, no significant differences were observed in other forms of medical research practices.

When compared with fifth-year students from other programs (clinical medicine “5 + 3” or 5-year), the students in pediatrics (“5 + 3”) had a lower rate of participation in the projects (Table  4 ). The rate of participation in the research training of the pediatric students was lower than that of clinical medicine (5-year) (44.44% vs. 75%). There were no significant differences in other research practices, such as writing articles and attending congress.

Barriers regarding scientific research among the students majoring in pediatrics (“5 + 3”)

The most common barriers to research work for pediatric students were lack of training (85.3%), lack of time (83.9%), and lack of mentorship (82.4%).

However, the top three barriers to research work in fifth-year pediatric students were lack of training (96.3%), limited English (88.89%) and lack of time (88.89%). We found that the barrier of “lack of training” became increasingly apparent with grade, which was significantly obvious in fifth-year pediatric students compared with other grades (Table  5 ). The other barriers had no significant differences among the three grades from the pediatrics (“5 + 3”) program.

When compared with fifth-year students from other programs (clinical medicine “5 + 3” or 5-year), the rate of agreement about the barrier of “limited English” was significantly higher in fifth-year students from the pediatrics (“5 + 3”) program. There were no significant differences in other barriers among fifth-year students from different majors (Table  6 ).

The type of research activities willing to involve in the future among the students majoring in pediatrics (“5 + 3”)

A total of 88.2% of students in pediatrics (“5 + 3”) wanted to participate in the training of scientific research activities. Furthermore, when asked about the type of future scientific research activities, 80.9% of students wanted to participate in clinical research, and only 19.1% of students wanted to be involved in basic research. There was no significant difference in the different grades of the students from the pediatrics (“5 + 3”) program (Fig.  2 A).

Types of research activities that students majoring in pediatrics are willing to be involved with in the future ( A ). Types of research activities that the students from different programs are willing to be involved with in the future ( B ). When compared with students in clinical medicine (“5 + 3”), fifth-year students in pediatrics (“5 + 3”) were significantly less likely to participate in basic research (* P  = 0.001)

Compared with students in clinical medicine (“5 + 3”), fifth-year students in pediatrics (“5 + 3”) were significantly less likely to participate in basic research (Fig.  2 B).

In China, to solve the shortage of pediatricians, pediatric programs have resumed in some medical schools, including Zhejiang University, in recent years. In this study, we focused on the perceptions, practices and barriers to scientific research in pediatric undergraduates from Zhejiang University.

With global progress, more research is required to advance knowledge and innovation in all fields. Likewise, at the present time, research activities are a highly important skill for medical practitioner. Medical students were encouraged to take active part in scientific research and prepare for today’s knowledge-driven world [ 2 ]. In the current study, we found an overall positive perception of scientific research in pediatric undergraduates. More than 90% of pediatric students agreed (“strongly agree” and “agree”) that scientific research was important, which could make them more competitive and improve their thinking.

Although the students had a positive perception of medical research, their practice of conducting research remained unsatisfactory. When compared with the fifth-year undergraduates from clinical medicine (“5 + 3”) (66.67%) and clinical medicine (5-year) (75%), only 33.33% of the fifth-year undergraduates in pediatrics (“5 + 3”) have participated in scientific research projects. The number of paper publications was very small (third-year of Pediatric (“5 + 3”) 0, fourth-year 4.5% and fifth-year 7.4%). It was significantly less than the publication rate of final-year students in the United States (46.5%) and Australia (roughly one-third) [ 10 , 11 ]. In another study in Romania, 31% of fifth-year students declared that they had prepared a scientific presentation for a medical congress at least once [ 12 ]. Moreover, none of the students in the study presented their paper in the scientific forum. A study in India also found that the undergraduate students’ experience of presenting paper in scientific forums was only 5% and publication 5.6% [ 13 ]. As part of the curriculum, some Indian universities require postgraduates to present papers and submit manuscripts for publication. Nevertheless, the practices regarding scientific research of undergraduates is still relatively poor. Lack of time, lack of guidance and lack of training for research careers were found to be the major obstacles in medical research for both pediatric students and others, which is consistent with previous reports [ 5 , 14 , 15 ]. The questionnaire in residents also found that lack of time was a critical problem for scientific research [ 16 ]. There is no common practice about how to solve this difficulty. In the literature, it was usually recommended that integration of scientific research training into the curricular requirements for undergraduates or residency programs for residents should be implemented [ 7 , 14 , 17 , 18 ]. An increasing number of medical schools have individual projects as a component of their curriculum or mandatory medical research projects to develop research competencies [ 19 , 20 ].

Interestingly, in fifth-year pediatric undergraduates (“5 + 3”), English limitations were found to be one of the most common barriers. The barrier of the limitation of English was increasingly better as the grades increased in pediatric students. We speculated that this was related to the increasing awareness of the importance of scientific research and participation in scientific research activities, increasing demand for reading English literature and writing English articles. Furthermore, the English limitation barrier for pediatric students was more obvious than that for students from clinical medicine (“5 + 3”) and clinical medicine (5-year). They are worried about academic English. Horwitz et al. first proposed “foreign language anxiety” [ 21 ]. Deng and Zhou explored medical students’ medical English anxiety in Sichuan, China. They found that 85.2% of the students surveyed suffered moderate above medical English anxiety [ 22 ]. In the questionnaire, 88.89% of the fifth-year pediatric students believed that limited English was one of the most important barriers for scientific research. Currently, English is the chief language of communication in the field of medical science, including correspondence, conferences, writing scientific articles, and reading literature. Ma Y noted that medical English should be the most important component of college English teaching for medical students [ 23 ]. At Zhejiang University, all of the students, including those majoring in pediatrics (“5 + 3”), clinical medicine (“5 + 3”) and clinical medicine (5-year), had a medical English course during the undergraduate period. Thus, the course could not satisfy the demands for scientific research, such as reading English literature, writing English paper and oral presentation in English. To solve this barrier, it was suggested to understand the requirements of pediatric students for medical English learning and offer more courses about medical English or English writing training for pediatric students. Furthermore, undergraduates should be encouraged to participate in local, regional or national conferences that are not in English but in Chinese language, which can increase the interest in participating in scientific research.

Most of the pediatric students tended to choose clinical research, while only 19.1% wanted to attend basic research. The proportion of fifth-year students in pediatrics (“5 + 3”) choosing basic research was much lower than the students from the clinical medicine (“5 + 3”) program. It is speculated that pediatrics usually have heavier clinical work with relative poor scientific practice in China, compare with doctors from other clinical department. They are likely to concern the clinical research. The students in pediatrics might not obtain sufficient scientific guidance from their clinician teachers compared with those from other medicine program. According to the data, the Pediatric College could conduct more scientific research training directed at clinical research, such as the design, conduct and administration of clinical trials. The simulation-based clinical research curriculum is considered to be a better approach training of clinician-scientists compared with traditional clinical research teaching [ 24 ]. On the other hand, we might need to do more to improve the interest in basic research for pediatric undergraduates.

The major limitation of the present study is the small sample size. Only 20 to 30 students have been enrolled in pediatrics (“5 + 3”) of ZUSM every year. Therefore, multicenter studies (multiple medical schools) might be better to understand the perception, practice, and barriers of medical research among pediatric undergraduates. Even so, the findings in this study indicate that lack of time, lack of guidance, lack of training and limited English might be the common barriers to scientific work for pediatric undergraduates. Furthermore, the questionnaire for teachers and administrators would be performed to offer some concrete solutions in future.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

Zhejiang University School of Medicine

Kaiser-Meyer-Olkin

Hanney SR, González-Block MA. Health research improves healthcare: now we have the evidence and the chance to help the WHO spread such benefits globally. Health Res Policy Syst. 2015;13:12.

Article   Google Scholar  

Adebisi YA. Undergraduate students’ involvement in research: values, benefits, barriers and recommendations. Ann Med Surg (Lond). 2022;81:104384.

Google Scholar  

Petrella JK, Jung AP. Undergraduate research: importance, benefits, and challenges. Int J Exerc Sci. 2008;1(3):91–5.

Stone C, Dogbey GY, Klenzak S, Van Fossen K, Tan B, Brannan GD. Contemporary global perspectives of medical students on research during undergraduate medical education: a systematic literature review. Med Educ Online. 2018;23(1):1537430.

El Achi D, Al Hakim L, Makki M, Mokaddem M, Khalil PA, Kaafarani BR, et al. Perception, attitude, practice and barriers towards medical research among undergraduate students. BMC Med Educ. 2020;17(1):195.

Funston G, Piper RJ, Connell C, Foden P, Young AM, O’Neill P. Medical student perceptions of research and research-orientated careers: an international questionnaire study. Med Teach. 2016;38(10):1041–8.

Tatum M. China’s three-child policy. Lancet. 2021;397:2238.

Rivkees SA, Kelly M, Lodish M, Weiner D. The Pediatric Medical Student Research Forum: fostering interest in Pediatric Research. J Pediatr. 2017;188:3–4.

Barrett KJ, Cooley TM, Schwartz AL, Hostetter MK, Clapp DW, Permar SR. Addressing gaps in Pediatric Scientist Development: the Department Chair View of 2 AMSPDC-Sponsored Programs. J Pediatr. 2020;222:7–e124.

Jacobs CD, Cross PC. The value of medical student research: the experience at Stanford University School of Medicine. Med Educ. 1995;29(5):342–6.

Muhandiramge J, Vu T, Wallace MJ, Segelov E. The experiences, attitudes and understanding of research amongst medical students at an Australian medical school. BMC Med Educ. 2021;21(1):267.

Pop AI, Lotrean LM, Buzoianu AD, Suciu SM, Florea M. Attitudes and practices regarding Research among Romanian Medical Undergraduate Students. Int J Environ Res Public Health. 2022;19(3):1872.

Pallamparthy S, Basavareddy A. Knowledge, attitude, practice, and barriers toward research among medical students: a cross-sectional questionnaire-based survey. Perspect Clin Res. 2019;10:73–8.

Assar A, Matar SG, Hasabo EA, Elsayed SM, Zaazouee MS, Hamdallah A, et al. Knowledge, attitudes, practices and perceived barriers towards research in undergraduate medical students of six arab countries. BMC Med Educ. 2022;22(1):44.

Kharraz R, Hamadah R, AlFawaz D, Attasi J, Obeidat AS, Alkattan W, et al. Perceived barriers towards participation in undergraduate research activities among medical students at Alfaisal University-College of Medicine: a Saudi Arabian perspective. Med Teach. 2016;38(Suppl 1):S12–8.

Fournier I, Stephenson K, Fakhry N, Jia H, Sampathkumar R, Lechien JR, et al. Barriers to research among residents in Otolaryngology - Head & Neck surgery around the world. Eur Ann Otorhinolaryngol Head Neck Dis. 2019;136(3S):S3–7.

Abu-Zaid A, Alkattan K. Integration of scientific research training into undergraduate medical education: a reminder call. Med Educ Online. 2013;18:22832.

Eyigör H, Kara CO. Otolaryngology residents’ attitudes, experiences, and barriers regarding the Medical Research. Turk Arch Otorhinolaryngol. 2021;59(3):215–22.

Möller R, Shoshan M. Medical students’ research productivity and career preferences; a 2-year prospective follow-up study. BMC Med Educ. 2017;17(1):51.

Laidlaw A, Aiton J, Struthers J, Guild S. Developing research skills in medical students: AMEE Guide 69. Med Teach. 2012;34(9):e754–71.

Horwitz EK, Horwitz MBH, Cope J. Foreign Language Classroom anxiety. Mod Lang J. 1986;70(2):125–32.

Deng J, Zhou K, Al-Shaibani GKS. Medical English anxiety patterns among medical students in Sichuan, China. Front Psychol. 2022;13:895117.

Ma Y. Exploring medical English curriculum and teaching from the perspective of ESP-A case study of a medical English teaching. Technol Enhan Lang Educ. 2009;125(1):60–3.

Yan S, Huang Q, Huang J, Wang Y, Li X, Wang Y, et al. Clinical research capability enhanced for medical undergraduates: an innovative simulation-based clinical research curriculum development. BMC Med Educ. 2022;22(1):543.

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Acknowledgements

The authors thank all the students who participated as volunteers for their contribution to the study.

This work was supported by grants from the “14th Five-Year Plan” teaching reform project of an ordinary undergraduate university in Zhejiang Province (jg20220041) and project of graduate education research in Zhejiang University (20210317).

Author information

Authors and affiliations.

Department of Neonatology, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China

Canyang Zhan

Department of Pulmonology, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China

Yuanyuan Zhang

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CZ designed and supervised the study progress. CZ and YZ wrote the manuscript and collected and analyzed the questionnaire data. All the authors have read and approved the manuscript prior to submission.

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Zhan, C., Zhang, Y. Perception, practice, and barriers toward research among pediatric undergraduates: a cross-sectional questionnaire-based survey. BMC Med Educ 24 , 364 (2024). https://doi.org/10.1186/s12909-024-05361-x

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• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

• Michiel Schreurs   ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3   na1 ,
• Supinya Piampongsant 1 , 2 , 3   na1 ,
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The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

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Introduction

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

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

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

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

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

Extensive analysis identifies relationships between chemical compounds in beer

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

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

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

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

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

Tasting panel assessments reveal sensorial relationships in beer

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

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

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

Tasting panel assessments of specific flavors correlate with chemical composition

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

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

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

Public consumer reviews complement expert panel data

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

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

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

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

Models can predict beer sensory profiles from chemical data

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Model validation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Beer selection

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

Chemical dataset

Sample preparation.

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

HS-GC-FID/FPD

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

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

HS-SPME-GC-MS

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

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

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

Discrete photometric and enzymatic analysis

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

NIR analyses

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

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

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

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

Online reviews from a public database

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

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

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

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

Beer price collection and processing

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

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

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

Model dissection

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

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

Validation of causal chemical properties

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

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

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

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

Reporting summary

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

Data availability

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

Code availability

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

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). https://doi.org/10.1007/978-1-4419-6488-5 (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). https://doi.org/10.1007/978-0-387-21606-5 (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). https://doi.org/10.1533/9781855739062.227 (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). https://doi.org/10.1007/978-3-030-65965-3_23 .

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 http://arxiv.org/abs/2207.08815 (2022).

Gries, S. T. Statistics for Linguistics with R: A Practical Introduction. in Statistics for Linguistics with R (De Gruyter Mouton, 2021). https://doi.org/10.1515/9783110718256 .

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 https://doi.org/10.1109/TNNLS.2022.3229161 (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 https://doi.org/10.48550/arXiv.1210.3926 (2012).

Meilgaard, M. C., Carr, B. T. & Carr, B. T. Sensory Evaluation Techniques. (CRC Press, Boca Raton). https://doi.org/10.1201/b16452 (2014).

Schreurs, M. et al. Data from: Predicting and improving complex beer flavor through machine learning. Zenodo https://doi.org/10.5281/zenodo.10653704 (2024).

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Acknowledgements

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

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

Authors and Affiliations

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

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

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

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

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

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

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

Łukasz Kreft & Alexander Botzki

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

Philippe Malcorps & Luk Daenen

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Contributions

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

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Correspondence to Kevin J. Verstrepen .

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

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

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

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Write it down, then throw it away: Research confirms a simple method for reducing anger

by Nagoya University

A research group in Japan has discovered that writing down one's reaction to a negative incident on a piece of paper and then shredding it or throwing it away reduces feelings of anger.

"We expected that our method would suppress anger to some extent," lead researcher Nobuyuki Kawai said. "However, we were amazed that anger was eliminated almost entirely."

This research is important because controlling anger at home and in the workplace can reduce negative consequences in our jobs and personal lives. Unfortunately, many anger management techniques proposed by specialists lack empirical research support. They can also be difficult to recall when angry.

The results of this study, published in Scientific Reports , are the culmination of years of previous research on the association between the written word and anger reduction. It builds on work showing how interactions with physical objects can control a person's mood.

For their project, Kawai and his graduate student Yuta Kanaya, both at the Graduate School of Informatics, Nagoya University, asked participants to write brief opinions about important social problems, such as whether smoking in public should be outlawed. They then told them that a doctoral student at Nagoya University would evaluate their writing.

However, the doctoral students doing the evaluation were plants. Regardless of what the participants wrote, the evaluators scored them low on intelligence, interest, friendliness, logic, and rationality. To really drive home the point, the doctoral students also wrote the same insulting comment: "I cannot believe an educated person would think like this. I hope this person learns something while at the university."

After handing out these negative comments , the researchers asked the participants to write their thoughts on the feedback, focusing on what triggered their emotions. Finally, one group of participants was told to either dispose of the paper they wrote in a trash can or keep it in a file on their desk. A second group was told to destroy the document in a shredder or put it in a plastic box.

The students were then asked to rate their anger after the insult and after either disposing of or keeping the paper. As expected, all participants reported a higher level of anger after receiving insulting comments. However, the anger levels of the individuals who discarded their paper in the trash can or shredded it returned to their initial state after disposing of the paper. Meanwhile, the participants who held on to a hard copy of the insult experienced only a small decrease in their overall anger.

Kawai imagines using his research to help businesspeople who find themselves in stressful situations. "This technique could be applied in the moment by writing down the source of anger as if taking a memo and then throwing it away when one feels angry in a business situation," he explained.

Along with its practical benefits, this discovery may shed light on the origins of the Japanese cultural tradition known as "hakidashisara" ("hakidashi" refers to the purging or spitting out of something, and "sara" refers to a dish or plate) at the Hiyoshi shrine in Kiyosu, Aichi Prefecture, just outside of Nagoya. Hakidashisara is an annual festival where people smash small disks representing things that make them angry. Their findings may explain the feeling of relief that participants report after leaving the festival.

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After being insulted, writing down your feelings on paper then getting rid of it reduces anger

A research group in Japan has discovered that writing down one's reaction to a negative incident on a piece of paper and then shredding it or throwing it away reduces feelings of anger.

"We expected that our method would suppress anger to some extent," lead researcher Nobuyuki Kawai said. "However, we were amazed that anger was eliminated almost entirely."

This research is important because controlling anger at home and in the workplace can reduce negative consequences in our jobs and personal lives. Unfortunately, many anger management techniques proposed by specialists lack empirical research support. They can also be difficult to recall when angry.

The results of this study, published in Scientific Reports , are the culmination of years of previous research on the association between the written word and anger reduction. It builds on work showing how interactions with physical objects can control a person's mood.

For their project, Kawai and his graduate student Yuta Kanaya, both at the Graduate School of Informatics, Nagoya University, asked participants to write brief opinions about important social problems, such as whether smoking in public should be outlawed. They then told them that a doctoral student at Nagoya University would evaluate their writing.

However, the doctoral students doing the evaluation were plants. Regardless of what the participants wrote, the evaluators scored them low on intelligence, interest, friendliness, logic, and rationality. To really drive home the point, the doctoral students also wrote the same insulting comment: "I cannot believe an educated person would think like this. I hope this person learns something while at the university."

After handing out these negative comments, the researchers asked the participants to write their thoughts on the feedback, focusing on what triggered their emotions. Finally, one group of participants was told to either dispose of the paper they wrote in a trash can or keep it in a file on their desk. A second group was told to destroy the document in a shredder or put it in a plastic box.

The students were then asked to rate their anger after the insult and after either disposing of or keeping the paper. As expected, all participants reported a higher level of anger after receiving insulting comments. However, the anger levels of the individuals who discarded their paper in the trash can or shredded it returned to their initial state after disposing of the paper. Meanwhile, the participants who held on to a hard copy of the insult experienced only a small decrease in their overall anger.

Kawai imagines using his research to help businesspeople who find themselves in stressful situations. "This technique could be applied in the moment by writing down the source of anger as if taking a memo and then throwing it away when one feels angry in a business situation," he explained.

Along with its practical benefits, this discovery may shed light on the origins of the Japanese cultural tradition known as hakidashisara ( hakidashi refers to the purging or spitting out of something, and sara refers to a dish or plate) at the Hiyoshi shrine in Kiyosu, Aichi Prefecture, just outside of Nagoya. Hakidashisara is an annual festival where people smash small discs representing things that make them angry. Their findings may explain the feeling of relief that participants report after leaving the festival.

• Anger Management
• Social Psychology
• Disorders and Syndromes
• Educational Psychology
• Consumer Behavior
• Anger management
• Social psychology
• Cognitive dissonance
• Self-awareness
• Obsessive-compulsive disorder
• Collaboration

Story Source:

Materials provided by Nagoya University . Note: Content may be edited for style and length.

Journal Reference :

• Yuta Kanaya, Nobuyuki Kawai. Anger is eliminated with the disposal of a paper written because of provocation . Scientific Reports , 2024; 14 (1) DOI: 10.1038/s41598-024-57916-z

Cite This Page :

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