introduction to curriculum research pdf

We will keep fighting for all libraries - stand with us!

Internet Archive Audio

• This Just In
• Grateful Dead
• Old Time Radio
• 78 RPMs and Cylinder Recordings
• Audio Books & Poetry
• Computers, Technology and Science
• Music, Arts & Culture
• News & Public Affairs
• Spirituality & Religion
• Radio News Archive

• Flickr Commons
• Occupy Wall Street Flickr
• NASA Images
• Solar System Collection
• Ames Research Center

• All Software
• Old School Emulation
• MS-DOS Games
• Historical Software
• Classic PC Games
• Software Library
• Kodi Archive and Support File
• Vintage Software
• CD-ROM Software
• CD-ROM Software Library
• Software Sites
• Tucows Software Library
• Shareware CD-ROMs
• Software Capsules Compilation
• CD-ROM Images
• ZX Spectrum
• DOOM Level CD

• Smithsonian Libraries
• FEDLINK (US)
• Lincoln Collection
• American Libraries
• Canadian Libraries
• Universal Library
• Project Gutenberg
• Children's Library
• Biodiversity Heritage Library
• Books by Language
• Additional Collections

• Prelinger Archives
• Democracy Now!
• Occupy Wall Street
• TV NSA Clip Library
• Animation & Cartoons
• Arts & Music
• Computers & Technology
• Cultural & Academic Films
• Ephemeral Films
• Sports Videos
• Videogame Videos
• Youth Media

Search the history of over 866 billion web pages on the Internet.

Mobile Apps

• Wayback Machine (iOS)
• Wayback Machine (Android)

Browser Extensions

Archive-it subscription.

• Explore the Collections
• Build Collections

Save Page Now

Capture a web page as it appears now for use as a trusted citation in the future.

Please enter a valid web address

• Donate Donate icon An illustration of a heart shape

An introduction to curriculum research and development

Bookreader item preview, share or embed this item, flag this item for.

• Graphic Violence
• Explicit Sexual Content
• Hate Speech
• Misinformation/Disinformation
• Marketing/Phishing/Advertising
• Misleading/Inaccurate/Missing Metadata

plus-circle Add Review comment Reviews

118 Previews

4 Favorites

Better World Books

DOWNLOAD OPTIONS

No suitable files to display here.

PDF access not available for this item.

IN COLLECTIONS

Uploaded by station18.cebu on January 14, 2022

SIMILAR ITEMS (based on metadata)

An introduction to curriculum research and development

21,163  citations

View 1 citation excerpt

Cites background from "An introduction to curriculum resea..."

... It is possible for action research to be an individualistic matter as well, relating action research to the ‘teacher-as-researcher’ movement (Stenhouse 1975). ...

1,897  citations

... A generation or so ago, Stenhouse (1975) argued that teachers ought to be school and classroom researchers and play an active part in the curriculum development process. ...

1,483  citations

1,035  citations

875  citations

Related Papers (5)

Ask Copilot

Related papers

Related topics

Understanding Curriculum in Higher Education

• First Online: 03 September 2022

Cite this chapter

• Kate O’Connor 4  

Part of the book series: Rethinking Higher Education ((RHE))

285 Accesses

Curriculum is a neglected area of attention in both higher education scholarship and policy. Despite a lot of concern and debate about university teaching practice, the curriculum effects of new teaching approaches tend to go unexamined. Although the concept of curriculum is complex and contested, foregrounding curriculum draws attention to the question of ‘what’ is taught in important ways (Deng, 2018 ; Yates, 2006 ), as well as the complex relations between curriculum and pedagogy (Bernstein, 1976 ). This chapter discusses the concept of curriculum and its importance for understanding the implications of unbundled online learning. It puts forward an interpretation of curriculum development as a contested site of struggle over the question of ‘what counts as knowledge’ and how knowledge is defined within a particular program of study. The chapter discusses the concepts and theories derived from the field of curriculum inquiry which informed this understanding, and how these were taken up to understand the case studies of unbundled online learning discussed later in the book. It highlights the concerns a focus on curriculum draws attention to, which are neglected in debates centered on learning and teaching.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Aoki, T. (1980/2005). Toward curriculum inquiry in a new key. In W. Pinar & R. Irwin (Eds.), Curriculum in a new key (pp. 81–110). Lawrence Erlbaum.

Google Scholar  

Apple, M. W. (1990). Ideology and Curriculum (2nd ed.). Routledge.

Ashwin, P. (2014). Knowledge, curriculum and student understanding in higher education. Higher Education , 67 (2), 123–126. https://doi.org/10.1007/s10734-014-9715-3

Ball, S., Hoskins, K., Maguire, M., & Braun, A. (2011). Disciplinary texts: A policy analysis of national and local behaviour policies. Critical Studies in Education , 52 (1), 1–14. https://doi.org/10.1080/17508487.2011.536509

Ball, S. J. (1997). Policy sociology and critical social research: A personal review of recent education policy and policy research. British Educational Research Journal , 23 (3), 257–274. https://doi.org/10.1080/0141192970230302

Ball, S. J. (2006). Education policy and social class: The selected works of Stephen J . Routledge.

Book   Google Scholar  

Barnett, R., & Coate, K. (2005). Engaging the curriculum in higher education . Open University Press.

Bayne, S., Evans, P., Ewins, R., Knox, J., Lamb, J., Macleod, H., O’Shea, C., Ross, J., Sheail, P., & Sinclair, C. (2020). The manifesto for teaching online . The MIT Press.

Becher, T. (1989). Academic tribes and territories: Intellectual enquiry and the cultures of disciplines . Society for Research into Higher Education & Open University Press.

Bernstein, B. (1976). Class, codes and control: Towards a theory of educational transmissions (2nd ed.). Routledge & Kegan Paul.

Biesta, G. (2010). Good education in an age of measurement: Ethics, politics, democracy . Paradigm Publishers.

Clegg, S. (2011). Cultural capital and agency: Connecting critique and curriculum in higher education. British Journal of Sociology of Education, 32 (1), 93–108. https://doi.org/10.1080/01425692.2011.527723

Article   Google Scholar  

Cliff, A., Walji, S., Mogliacci, R. J., Morris, N., & Ivancheva, M. (2020). Unbundling and higher education curriculum: A cultural-historical activity theory view of process. Teaching in Higher Education , 1–16. https://doi.org/10.1080/13562517.2019.1711050

Coate, K. (2009). Curriculum. In M. Tight, K. Mok, J. Huisman, & C. Morphew (Eds.), The Routledge international handbook of higher education (pp. 77–90). Routledge.

Chapter   Google Scholar  

Connelly, F. M., & Xu, S. (2010). An overview of research in curriculum inquiry. In P. Peterson, E. Baker, & B. McGaw (Eds.), International encyclopedia of education (3rd ed., pp. 324–334). Elsevier. https://doi.org/10.1016/B978-0-08-044894-7.00067-1

Deng, Z. (2018). Contemporary curriculum theorizing: Crisis and resolution. Journal of Curriculum Studies, 50 (6), 691–710. https://doi.org/10.1080/00220272.2018.1537376

Deng, Z., & Luke, A. (2008). Subject matter: Defining and theorizing school subjects. In F. M. Connelly, M. F. He, & J. Phillion (Eds.), The SAGE handbook of curriculum and instruction (pp. 66–87). Sage Publications.

Gewitz, S., & Cribb, A. (2009). Understanding education: A sociological perspective . Polity Press.

Green, B. (2010). Knowledge, the future, and education(al) research: A new-millennial challenge. Australian Educational Researcher, 37 (4), 43–62.

Green, B. (2018). Engaging curriculum: Bridging the curriculum theory and English education divide . Routledge.

Hicks, O. (2017). Curriculum on the map! 10 years of curriculum initiatives in higher education in Australia. In R. Walker & S. Bedford (Eds.), Research and development in higher education: Curriculum transformation (pp. 164–174). Higher Education Research and Development Society of Australasia.

Karseth, B. (2006). Curriculum restructuring in higher education after the Bologna Process: A new pedagogic regime? Revista Espanola De Eduation Comprada, 12 , 255–284.

Karseth, B., & Solbrekke, T. D. (2016). Curriculum trends in European higher education: The pursuit of the Humboldtian University ideas. In S. Slaughter & B. J. Taylor (Eds.), Higher education, stratification, and workforce development: Competitive advantage in Europe, the US, and Canada (pp. 215–233). Springer.

Krause, K.-L. D. (2020). Vectors of change in higher education curricula Journal of Curriculum Studies 1–15 https://doi.org/10.1080/00220272.2020.1764627

Morgan, J., & Lambert, D. (2018). For knowledge – but what knowledge? Confronting social realism’s curriculum problem. In B. Barrett, U. Hoadley, & J. Morgan (Eds.), Knowledge, curriculum and equity: Social realist perspectives (pp. 17–31). Routledge.

Nerland, M., & Jensen, K. (2012). Epistemic practices and object relations in professional work. Journal of Education and Work , 25 (1), 101–120. https://doi.org/10.1080/13639080.2012.644909

Nespor, J. (1994). Knowledge in motion: Space, time and curriculum in undergraduate physics and management . Routledge.

Pinar, W. F. (2012). What is curriculum theory? (2nd ed.). Routledge.

Rudolph, S., Sriprakash, A., & Gerrard, J. (2018). Knowledge and racial violence: The shine and shadow of ‘powerful knowledge’. Ethics and Education , 13 (1), 22–38. https://doi.org/10.1080/17449642.2018.1428719

Shore, C., Wright, S., & Però, D. (Eds.). (2011). Policy worlds: Anthropology and the analysis of contemporary power . Berghahn Books.

Slaughter, S. (2002). The political economy of curriculum-making in American universities. In S. Brint (Ed.), The future of the city of intellect: The changing American university (pp. 260–289). Stanford University Press.

Tight, M. (2012). Researching higher education . Open University Press.

Westbury, I. (2003). Evaluating a national curriculum reform. In P. Haug & T. A. Schwandt (Eds.), Evaluating educational reforms: Scandinavian perspectives (pp. 189–208). Information Age Publishing Inc.

Yates, L. (2006). What can schools do? Knowledge, social identities and the changing world. TCI (Transnational Curriculum Inquiry) , 3 (1), 1–11. https://doi.org/10.14288/tci.v3i1.17

Yates, L. (2009). From curriculum to pedagogy and back again: Knowledge, the person and the changing world. Pedagogy, Culture & Society , 17 (1), 17–28. https://doi.org/10.1080/14681360902742837

Yates, L. (2012). My school, my university, my country, my world, my Google, myself… What is education for now? The Australian Educational Researcher , 39 , 259–274. https://doi.org/10.1007/s13384-012-0062-z

Yates, L., & Grumet, L. (2011). Curriculum in today’s world: Configuring knowledge, identities, work and politics .

Yates, L., Woelert, P., Millar, V., & O’Connor, K. (2017). Knowledge at the crossroads?: Physics and history in the changing world of schools and universities . Springer.

Young, M., & Muller, J. (2015). Curriculum and the specialization of knowledge: Studies in the sociology of education . Routledge.

Young, M. F. (1971). Knowledge and control: New directions for the sociology of education . Collier-Macmillan.

Zipin, L., Fataar, A., & Brennan, M. (2015). Can social realism do social justice? Debating the warrants for curriculum knowledge selection. Education as Change , 19 (2), 9–36. https://doi.org/10.1080/16823206.2015.1085610

Download references

Author information

Authors and affiliations.

La Trobe University, Bundoora, VIC, Australia

Kate O’Connor

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Kate O’Connor .

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

O’Connor, K. (2022). Understanding Curriculum in Higher Education. In: Unbundling the University Curriculum. Rethinking Higher Education. Springer, Singapore. https://doi.org/10.1007/978-981-19-4656-1_3

Download citation

DOI : https://doi.org/10.1007/978-981-19-4656-1_3

Published : 03 September 2022

Publisher Name : Springer, Singapore

Print ISBN : 978-981-19-4655-4

Online ISBN : 978-981-19-4656-1

eBook Packages : Education Education (R0)

Introduction to Curriculum for Early Childhood Education

(16 reviews)

Jennifer Paris, College of the Canyons

Kristin Beeve, College of the Canyons

Clint Springer, College of the Canyons

Copyright Year: 2018

Last Update: 2019

Publisher: College of the Canyons

Language: English

Formats Available

Conditions of use.

Learn more about reviews.

Reviewed by Felix Rodriguez Suero, Lecturer I, University of Texas Rio Grande Valley on 11/9/22

This book addresses a wide range of topics pertaining to curriculum design and inquiry with young children. Learning through Play is a central focus of the book. However, the authors introduce the reader to the most common approaches to early... read more

Comprehensiveness rating: 5 see less

This book addresses a wide range of topics pertaining to curriculum design and inquiry with young children. Learning through Play is a central focus of the book. However, the authors introduce the reader to the most common approaches to early childhood education.

Content Accuracy rating: 5

I think that at times the authors rely too much on secondary documents instead of citing scientific research findings directly. However, the authors draw from relevant theories and anecdotes to illustrate what the application of specific pedagogical principles may look like in practice, which I found particularly valuable for education students at the undergraduate level in general.

Relevance/Longevity rating: 5

One of the important contributions of this book is summarizing in one place, theories, principles, and concepts that often demand the use multiple books and articles. This book gives future teachers a good grasp of essential concepts such as transitions, routines, student readiness, assessment, and documentation. In addition to the solid discussion of the traditional literature, future editions could benefit from discussions on the contributions of neuroscience to how we understand young children's learning.

Clarity rating: 5

The authors use a language that is clear and accessible. Some of the articles I assign in my Art Education method courses can be hard to read for some of my undergraduate students. I am considering substituting some of those articles with chapters and sections from this book.

Consistency rating: 4

The organization of the book is consistent throughout the different chapters. The pop-up windows "Vignette" and "Pause and Reflect" add content and experiences that facilitate the connection between theory and practice.

Modularity rating: 5

In my case, not all chapters are useful for the courses I currently teach, but the book structure makes it conducive to assign specific chapters that align with some of my weekly topics.

Organization/Structure/Flow rating: 5

The book is organized into five large thematic sections. Each section is divided into uneven number of chapters. However, the organization of the text is consistent throughout the different chapters. The book starts with more general theoretical and practical considerations that could be useful to students from different disciplines. Section IV, covering Chapters 7-16 provides insights into specific subject areas.

Interface rating: 2

While the book is comprehensive and useful for introduction to curriculum design and education method courses, the interface of the book could benefit from more elaborate formatting and design. Several images are low resolution, and the format and quality are not consistent. In general, a more professional design could make the book more visually appealing.

Grammatical Errors rating: 5

I did not notice significant grammatical errors.

Cultural Relevance rating: 5

The authors are attentive to how cultural and social factors affect students’ engagement. They intentionally examine learning experiences from diverse cultural settings and discuss how disadvantage students may lack access to technology.

I was expecting more specialized and in-depth discussions on the Creative Arts in Chapter 11. Nonetheless, this book is a great resource to address general curriculum design considerations with young children. I plan to use this book in the future.

Reviewed by Ilfa Zhulamanova, Associate Professor, University of Southern Indiana on 5/19/22

This text brings a comprehensive approach to curriculum making in early childhood education. I really liked the emphasis on play-centered approach to education of young children. read more

This text brings a comprehensive approach to curriculum making in early childhood education. I really liked the emphasis on play-centered approach to education of young children.

The content information is researched-based, unbiased and accurate.

The text consists of best practices experienced and grounded in research for the education of young children. The text is written and/or arranged in such a way that necessary updates will be relatively easy and straightforward to implement.

The text is clearly written, full of examples and visual graphs, charts, tables, and photos. The language is appropriate for the context.

Consistency rating: 5

There is a consistency in organization, terminology and framework of the book which makes it easy to follow. Each chapter begins with objectives and brief introductions. I really like the Pause to Reflect sections included throughout the text.

The text content is divided into sixteen chapters which can be easily and readily divisible into smaller reading sections that can be assigned at different points within the course. Instructors will find this format easy to follow to organize their course.

The content topics are presented in a logical, clear fashion.

Interface rating: 5

The text is free of significant interface issues, including navigation problems, distortion of images/charts.

The book is readable with no grammatical errors.

Cultural Relevance rating: 4

The text book content and images represent diverse population of children and families schools serve today. More information on teaching children with special needs and ESL/ELL students would make this book more appealing to instructors.

Reviewed by Robert Bryant, Adjunct Professor, Dominican University on 4/25/22

The text is readable and complete in scope and Early Childhood responsibilities. read more

The text is readable and complete in scope and Early Childhood responsibilities.

The text shares accurate information that is correct and timely based on current research.

The text is relevant in today's education environment.

The book is readable and compete.

The book focuses on nurturing care and attachment as paths for early learning.

The book has a preface and a table of contents.

The book is organized as a readable book, but also by chapter subject for easy reference on many topics.

The PDF download makes it convenient to use as a reference anywhere.

The book is relevant when compared to current early learning research.

Reviewed by Kimberly McFall, Associate Professor, Marshall University on 1/3/22

The book is arranged in a logical order and includes relevant up-to-date topics. read more

The book is arranged in a logical order and includes relevant up-to-date topics.

The book reflects accurate information

This book includes relevant topics that are arranged in a logical way. One thing that might be helpful is to make sure that terms/topics that are intertwined (technology and culture for example) are also noted in a chapter summary or hyperlinked from the Chapter Objectives sections for easy access/talking points for users.

The book is well written, grounded in research, and easy to read.

The book is consistent with current research, accuracy in data/tables and laid out in a way that the friendly to the user.

Modularity rating: 4

The text features a clearly laid out chapter objective section and chapter headers. I think that it would be made even stronger if the objectives were aligned to the headers or hyperlinked to the sections they support.

This book is well laid out and is scaffolded nicely throughout.

Interface rating: 4

An opportunity for strengthening this book, even more, is to provide hyperlinks from the chapter objects to the sections of the book where each objective is addressed. Also, since the author does such a nice job of supporting the content with cultural and technology references, keywords that are hyperlinked from the table of contents to these topics that are not stand-alone chapters might help users if they want to use this book in part/section.

Professional and accurate without grammatical error

As noted above, easier access to clickable links or noted where to find culturally relevant content would strengthen this area.

This is well thought out book that gives an in-depth look at early childhood education in a practical approach. More information about culturally responsive teaching would make this book a home run. The authors have done an outstanding job providing useful, researched-based information. One glaring issue is the lack of glossary or index and clickable links from Table of Contents and Chapter Objects would be helpful.

Reviewed by Robin Folkerts, Assistant Teacher Educator, Wichita State University on 10/30/21

There is a table of contents which is very helpful and transparent about the contents. I found the information in this text to be very comprehensive and thorough in regards to an introduction to Early Childhood Education. There is no index or... read more

Comprehensiveness rating: 4 see less

There is a table of contents which is very helpful and transparent about the contents. I found the information in this text to be very comprehensive and thorough in regards to an introduction to Early Childhood Education. There is no index or glossary to accompany this text.

I have taught this course with another book, and I find that the information in this resource is accurate and up to date. I did not find errors in my review, and I did not find anything that was biased in my opinion.

The topics are relevant, and the vignettes that are added in each chapter are valuable for deeper understanding of real life experiences. It is helpful for Early Childhood Education teacher candidates to have practical and specific examples of how theories are applied. I found the topics covered in this book to be important and well-represented.

The flow of the text is easy to follow. It is helpful to have tables and charts to help clarify information in the text. The objectives at the beginning are also helpful to clarify what is contained in each chapter. I appreciated the multiple lists that were included in the tables. The readability was easy and engaging. I found the tables and charts in the fourth section to be helpful in understanding developmentally what skills students have at certain ages and stages. I particularly enjoyed the chapter on teaching science where it was divided into sub-categories of earth science, life science, and physical science.

I see consistency throughout the resource in regard to text structure and text features. Each chapter begins with the objectives, and is laid out in an easy to read format with headings, tables, and vignettes (in green boxes) and research highlights (in purple boxes). The consistency made it easier to read and follow.

I found this text to be well-organized with text features used to keep information in manageable chunks. Illustrations and tables are used to help clarify information and it flows well for the reader. I especially like the vignettes that were consistently in green boxes. They are well-written and relevant.

I found this resource to be well-organized and easy to follow. It is divided into sections and chapters where the first three sections are more of an introduction, and the fourth section is the real meat and potatoes of curriculum and lesson planning. The final section gives an extension for other age groups.

The interface was exceptional. I downloaded it as a PDF and it was easy to navigate. I had no issues at all with any of the displays or features, and it would not be confusing or distracting to readers. This resource is easy to navigate and consistent in its format.

I did not identify any grammatical errors in my review of this resource. A link was included at the beginning to report any such findings.

While I did not see anything blatant, in comparison to the book that I currently use, there is not an entire chapter dedicated to cultural sensitivity. Rather, it is intertwined within the chapters. There was a piece in an early chapter that talked about including books and materials that are culturally diverse, There was a reference in the Infants and Toddlers chapter about cultural sensitivity as well as working together with families. English Language Learners is also not included as a chapter on its own, but is interwoven into the contents of this resource.

I enjoyed this resource very much, and I will plan to use it with my ECU: Foundations course that I teach. Well Done!

Reviewed by Jennifer Forker, Professor, Hutchinson Community College on 10/18/21

This textbook covers all of the major topics for developing a curriculum in a preschool setting. read more

This textbook covers all of the major topics for developing a curriculum in a preschool setting.

The book breaks down each developmental level expectations in a way that is easy to read and understand.

Relevance/Longevity rating: 4

The textbook focuses on the California Early Learning Standards, but can easily be adapted to your state framework.

The textbook uses phrases that should be common knowledge to all early childhood education professionals.

The chapters are laid out similarly so the student always knows what to expect.

Each subtopic is in its own chapter and can be easily skipped (or added to) if needed.

The book flowed easily from basic theorist knowledge to more in depth procedures on how to incorporate curriculum into your classroom.

The links provided in the footnotes are live and easy to access.

I did not see any grammatical errors.

The photos used were inclusive and representative.

The vignettes provided real life examples of best practices in early childhood education.

Reviewed by Diane Lewis, Adjunct Professor, Northern Essex Community College on 4/6/21

I really like how the book is broken down into sections and works from the understanding how children learn to developing curriculum. I like seeing how to set stage for learning and guiding behavior in classroom. I like how the book wraps up the... read more

I really like how the book is broken down into sections and works from the understanding how children learn to developing curriculum. I like seeing how to set stage for learning and guiding behavior in classroom. I like how the book wraps up the last 9 chapters with what the curriculum looks like. Concluding with documentation and assessment is a great way to end the students learning. This book will be very helpful in many classes in ECE.

As I was reading I found the information to be accurate and error-free. The author is unbiased.

It will be pretty easy to update what would need to be updated as years go by.

I really liked the clarity and the examples in the chapters. Not much jargon/technical terminology to confuse the reader. Easy to read.

I liked how the book started in the understanding and ended with examples. The text was consistent in how it was written.

Different chapters can be spaced out over the course and also through other courses as well. It can easily be broken down into different sections for easier digestion of the reading. Lots of charts and pictures break up the blocks of text.

Definitely written in a logical and clear fashion. It shows someone how to educate young children.

Although there are images and charts, it is easy to navigate around them or through them. There seems to be a nice lead into the charts or images that makes it so that they are not distracting or confusing to the reader.

I did not find the text to be culturally insensitive or offensive in any way.

I would use this book in a couple of my classes. It has information that I cover in in 3 different classes.

Reviewed by Mary Ellen McGuire-Schwartz, Professor, Rhode Island College on 12/9/20

The text is comprehensive in covering areas of early childhood curriculum. I like the straightforward nature of chapter content with photos, charts, webs, links and other resources. I like the links that are available in each chapter. Some of the... read more

The text is comprehensive in covering areas of early childhood curriculum. I like the straightforward nature of chapter content with photos, charts, webs, links and other resources. I like the links that are available in each chapter. Some of the links are related to California State Standards and California Child Care Licensing Regulations. It would be good to have links from other states. I would also like more in the text on kindergarten - grade 2 curriculum, cultural competence, equity, Universal Design for Learning, and inclusion .

Content Accuracy rating: 4

The content appears to be very accurate with references and links provided in each chapter. I was not able to check all sources and documentation.

The content is very relevant and straightforward with links that can be updated.

I found the text very clear and to the point.

The text is internally consistent with terminology and framework. My only concern relates to limits of California regulations and standards. Is it possible to add regulations and standards of other states to the text?

There is good organization in text. The text is broken down into small organized sections with headings, subheadings, charts, and webs.

Organization/Structure/Flow rating: 4

The topics in the text are in general presented in a logical, clear fashion. I would like to see infant-toddler and kindergarten - second grade curriculum connected with the preschool curriculum in a logical order. More focus on both infant-toddler, Birth - 3, and kindergarten through primary curriculum, age 5-8, would add to the flow of the book. Chapters 15 and 16 cover the areas of infants and toddlers and school age curriculum at the end of the book.

The text is very clear and direct. I found no distortions of images or charts. I liked the displays of charts and webs in the book. The appendices were particularly clear and helpful.

Grammatical Errors rating: 4

I found the text grammatically correct but I did not review it with a fine tooth comb.

Cultural Relevance rating: 3

A greater focus on social justice, equity, and cultural competence throughout the book would add to its cultural relevance. More culturally diverse images would also add to the text.

In general, I like the book. It is comprehensive. I would like to see a greater focus on cultural competence, social justice, and equity.

Reviewed by Yolanda Buenafe, Early Childhood Education Faculty and Program Coordinator, Mt. Hood Community College on 8/17/20

The textbook is quite comprehensive as an overall introduction to early childhood curriculum. Content covers theories, curriculum models, developmental span from infancy to school age, and specific curriculum areas. The textbook provides a good... read more

The textbook is quite comprehensive as an overall introduction to early childhood curriculum. Content covers theories, curriculum models, developmental span from infancy to school age, and specific curriculum areas. The textbook provides a good foundation for many of our other early childhood courses, where we delve into specific topics and issues. Expansion in a few areas could be further incorporated, as indicated in other criteria sections of this review. There is an appendix section which provides useful supplemental resources. Incorporating an index and glossary would be helpful to have for future revisions of this textbook.

The content is accurate and free of bias, citing theories, research, and child development milestones and principles.

Information presented is relevant to curriculum approaches and content areas which students will need to be knowledgeable of, when considering how to apply and integrate these into their developing teaching practices. Expanding on the topics of DAP, anti-bias curriculum, and intentional teaching would increase the textbook's relevance to current and future competencies for early childhood practitioners.

The text is written in a clear manner, utilizing terminology that is pertinent and essential to those in the early childhood field. The theoretical aspects were described in a straightforward and understandable way, and further enhanced with diagrams, tables, and other visuals.

The textbook is consistent with its format of outlining objectives, followed by a concise and clear introduction of the chapter/topic, and providing vignettes and reflection questions in each chapter to connect theory with practical application. Terminology is consistent throughout the text.

The chapters cover the content areas well without being overly lengthy and include essential and relevant subtopics. In the chapters that address the various subject/curriculum areas, developmental milestones and skills for preschool ages four to five are nicely outlined in tables. For a more integrated approach emphasizing the continuum from the infant stage to school age, I would suggest that the tables include skills/milestones from infancy to school age in each of these curriculum areas, which would require revision or renaming of the ‘preschool’ planning section to include a broader scope. An additional recommendation would be to place the chapter on infant and toddler curriculum before the section/chapters on preschool curriculum. This would emphasize the adult-child interactions as central to infant and toddler curriculum, and then proceed to the more specific curriculum content chapters, providing information on how math, science, literacy, social science concepts can be identified and supported in everyday routines and explorations with infants and toddlers.

Overall the topics in each chapter are organized in a clear and systematic manner with guiding principles presented at the beginning of the chapters, followed by vignettes that bring these concepts 'to life.' The chapters end with reflection questions, providing the reader/student with a personal connection to the chapter content. Adding a more defined chapter on anti-bias curriculum in section 1 would highlight the importance of our awareness of incorporating anti-bias curriculum throughout formal and informal planning. One additional recommendation is to include a subtopic or section on managing group times in the chapter for 'Guiding Behavior and Managing the Classroom, as this is a vital skill that all teachers will need for guiding children's learning and self regulation.

The interface presented well. The visual diagrams and tables were displayed well, and enhances the written text on the respective topics. Navigation was smooth, with only one broken link at the time of this review (to the Australian Government Department of Education).

No grammatical errors were detected.

There are several vignettes presented throughout the textbook which reflect the growing diversity in our early childhood classrooms, which I found to be culturally sensitive and relevant to the experiences of our current practitioners. As mentioned earlier in this review, I suggest adding an additional chapter specific to anti-bias curriculum and cultural responsiveness.

Of all the OER textbooks written for early childhood education, I have found this Introduction to Curriculum for ECE textbook to be the best thus far. It is a compilation of all the essential information we would want to impart to our early childhood education students about what curriculum encompasses. There is a balance of theory presented in a clear and understandable manner, blended with numerous vignettes and reflection questions to support our students in their emerging teaching practices. The content provides a good foundation for knowledge of curriculum, along with many opportunities for rich discussion based on real life scenarios.

Reviewed by Maryam Sharifian, Assistant Professor, James Madison University on 7/31/20

The chapters are very well developed with achievable and comprehensive objectives. The content of each chapter unfolds each objective and provides opportunities to reflect with examples and thoughtful scenarios. Chapters are matching one another... read more

The chapters are very well developed with achievable and comprehensive objectives. The content of each chapter unfolds each objective and provides opportunities to reflect with examples and thoughtful scenarios. Chapters are matching one another in thorough order. However, the text does not have an effective index/glossary.

The content is accurate, error free and unbiased.

Content is up to-date but not referring to the most recent studies to make it more relevant. The technology section is not comprehensive and needs more relevant and up to date strategies to provide a better understanding of the importance of utilizing appropriate technology and developing required skills in ECE. In addition, building family school community relationship is a critical factor in ECE that should be more highlighted and extended throughout the content.

The content is explicit and understandable. It is easy to follow each section and build connection between chapters.

The book is developed based on a strong consistent framework. This framework creates clarity and prevents unexpected expectations from the reader.

The authors used objectives as overall outline to create clear subheads for each chapter. Their method helps readers in understanding the content and instructors in planning teaching content.

The authors developed a great organizational layout to break down each section and keep it consistent.

There is no major interface issue. The images are not distracting, however, they do not add any significant values to the text.

The text contains no grammatical errors.

This book has a universal approach in presenting the content. The examples and scenarios are inclusive. Authors are intentional in emphasizing the importance of culturally responsive teaching. The content is developed based on children who are culturally diverse, linguistically diverse, diverse in ability, and from diverse socioeconomic backgrounds.

Introduction to Curriculum for Early Childhood Education is a great vehicle to prepare future early childhood teachers through a clear and consistent content.

Reviewed by Adkins Vernita, Associate Professor, California State University, Dominguez Hills on 7/25/20

This is a very comprehensive text covering pertinent topics in early childhood education from understanding how children learn to the appropriate setting for their learning to the curriculum topics that cover their complete educational development. read more

This is a very comprehensive text covering pertinent topics in early childhood education from understanding how children learn to the appropriate setting for their learning to the curriculum topics that cover their complete educational development.

The content is relevant, accurate and unbiased.

The text is a compilation of current best practices experienced and grounded in research for the education of children.

It is easily read and does provide an appropriate context for use of educational terminology.

The text is consistent in use of its terminology and framework.

Each chapter presents objectives, frameworks, theories, reflections/vignettes and examples of practical applications on the chapter topic.

The topics in the text and in each chapter are presented with a comprehensive overview to specific applications.

There are no features that are distorted that may distract or confuse the reader.

There are no grammatical errors evident in the text.

Examples and pictures within the text are inclusive of a variety of races, ethnicities, and backgrounds.

This early childhood education text is clearly and beautifully written and presented with research based, comprehensive and practical information on the development and instruction of children addressing their early education environment with appropriate learning strategies.

Reviewed by John Cipora, Adjunct Instructor, Holyoke Community College on 6/30/20

I found this text to be supremely comprehensive in scope, as well as fully current and progressive in tone and intent. To my mind, it would make an ideal foundational work for undergraduate programs in early childhood education. It is... read more

I found this text to be supremely comprehensive in scope, as well as fully current and progressive in tone and intent. To my mind, it would make an ideal foundational work for undergraduate programs in early childhood education. It is sufficiently broad in topical coverage as to have utility across multiple courses, from Foundations or Early Childhood Development through Ethical and Professional Standards or Children with Special Needs. At the same time, specific sections or chapters provide sufficient depth to serve as excellent pathways of entry into dynamic and evolving topical arenas such as Diversity and Multiculturalism or Infants and Toddlers: Learning through Relationships. An index would be a useful addition to this excellent work.

I found the content to be entirely accurate, bias-free, and appropriately current in the selection of supporting resources incorporated throughout. A salient attribute of the authors' approach is the presentation of nuanced advocacy (toward full inclusion, for instance, or the need to engage families) in very matter-of-fact fashion. Rather than taking a prescriptive tone, so typical in overview college texts, the sensibility is collegial, engaging, and welcoming: content is introduced and consistently reinforced in a manner that invites readers who may be new to the field to participate in optimal, dynamic, and creative ways.

I found that the early chapters provide exemplary grounding of fundamental educational frameworks in such engaging, expansive, and globally relevant fashion that everything that follows flows logically and consistently from those introductory passages. Such clarity of concept and logic of sequencing affords a seamless structure to which future essential changes can be made in organic, authentic fashion as core professional concepts get refined or added. In the vernacular of the moment, the authors have created a 'living document' which captures central current best practices while being open to creative amplification going forward.

In my view, the writing throughout is accessible while appropriately academic, and richly informative while never being pedantic or turgid. The enthusiasm and expertise of the authors shines through in lucid prose and evocative, relevant, often inspired selection of supporting photographs and figures. The tone is inviting along with being professional; the always-implicit, often-explicit expectation is that optimal professionalism is a given, at all times and in all contexts. The reader is guided to the fundamental recognition that, while every practitioner can and should enhance their competencies, there is a baseline of excellence to which each person who enters an early childhood education center as a professional needs to adhere: a most appropriate metaview, in short.

The authors have deftly managed to frame the entire work in such a way as to be infused with a single authorial voice--no small accomplishment for a work with multiple contributors. The clarity of the format, recursive but never simply repetitive, serves as an intuitively-navigable sequence of guideposts. Consequently, the reader is provided an opportunity to construct their own incrementally-enriched, coherently guided, and pedagogically interconnecting gestalt. Whether a student works through this text in sequence or in a more complex, topically-guided manner, the thematic underpinnings of the content are consistently made evident.

This is one of the most appealing attributes of the text: while the authors have rendered a field-wide overview in clear and comprehensible fashion, they have also managed to produce individual segments, whether sections or chapters, that are fully self-contained. To my mind, a dedicated practitioner--faculty member or student--could choose any such item with which to begin a unit of study, with equally substantive results. Thus, the work affords marvelously wide pathways via which to access desired content, whatever the particular curriculum of the institution choosing to use its exemplary range of opportunities.

This could be my favorite attribute of this textbook: after working through the first fifty pages or so, I realized that the organization of material was so lucid that it was perfectly seamless. It simply makes exquisite sense, providing an exemplary compendium of essential information while remaining transparent as to overall goals and intent of the overall document. The term 'reflective practitioners' kept surfacing for me: the creators understand the field, are confident as to the depth and range of their insights, and convey their expertise and enthusiasm in an entirely appropriate, coherent, and connected fashion.

This aspect of the work is particularly noteworthy, perhaps because, in its clarity, simplicity, and comprehensive nature, it is virtually invisible if one isn't specifically focused on it. Essential guiding items such as 'Pause to Reflect,' 'Vignettes,' 'Teacher Tips,' or 'Research Highlights' are emphasized without being intrusive: they flow easily into the rest of the content, welcome amplifications without being unduly distracting from the overall forward direction of the passage. The choice of placing a significant bank of relevant but secondary supporting content into an appendix is an example of a navigational decision that makes great sense. Figures available here include such items as classroom floor plans, charts of developmental metrics across domains or of salient developmental sequences, and CSEFELS tables, all of which are valuable but which would have been distracting had they been embedded in their entirety in the text proper.

As suggested above, the prose style is vivid, dynamic, and highly effective. I found no instances of content presentation that were anything less than lucid, direct, and exemplary: all that is essential is included, while nothing extraneous has been retained.

Again, the authors have been exemplars of presenters in this regard. Concepts of diversity or multiculturalism have been interwoven in every section of the text, in smooth, seamless fashion that makes such respect and inclusion perfectly matter of fact--as of course they should be. I so appreciated the full range of topics and concepts that this integrative approach subsumed, across dimensions of race and ethnicity, countries of origin, home languages, socioeconomic status, and religious beliefs as well as those less typically incorporated such as differently-abled individuals or those presenting with the full range of gender identifications or sexual orientation preferences. These presentations of equity and equality emerged consistently both in text and images.

I plan to begin using this text as soon as possible in my upcoming courses in the field, whether in blended or online modalities. My students will benefit both conceptually and economically.

Reviewed by Caitlin Malloy, Associate Lecturer, University of Massachusetts Boston on 6/29/20

This textbook provides a comprehensive summary of curriculum planning for preschool-aged (3-to 5-year old) children. With only a chapter truly dedicated to infant/toddler and early elementary-aged children, instructors who are teaching student... read more

This textbook provides a comprehensive summary of curriculum planning for preschool-aged (3-to 5-year old) children. With only a chapter truly dedicated to infant/toddler and early elementary-aged children, instructors who are teaching student teachers seeking a broader license (e.g., PreK-2 or Birth-5) will need to supplement the text in these areas. The book assumes a basic knowledge of child development (though a summary of developmental milestones is provided in the Appendices), and would be most useful to students who have yet had little exposure to early childhood classrooms.

The book does not have a glossary or a ‘References’ section.

The content is accurately presented, and examples illustrate the diverse demographics of students that may be encountered in a United States preschool context. The authors cite recent work from prominent scholars in the field, or research that is considered to be ‘seminal’ – together, these provide a sound summary of relevant knowledge.

One concern is that diversity/anti-bias curriculum is treated as a separate curricular area; for example, in the Preface, it is listed as one of the specific domains to plan for (separate from literacy or science). Current best practices in anti-bias curriculum planning emphasize how considerations of diversity should be embedded across all curriculum areas (in other words, as part of language and math), not as a separate domain of its own. Anti-bias curriculum is discussed, but is presented as a way to support History and Social Sciences, instead of as something that should be included in all areas of curriculum planning.

The information presented is generally relevant, given the quality and recency of the works cited. However, as pointed out earlier, the approach to discussing anti-bias work detracts from the relevance, as well-integrated anti-bias work is central to high quality early education in our current society.

The language is clear and accessible. Summary tables and charts were particularly helpful for aiding comprehension of text.

The terminology is used consistently throughout the text, and the presentation of the material is structured similarly in all chapters, making it easy to navigate.

The text is broken down into logical and manageable sections that could be divided if relevant for the course or instructor. The subheadings are very helpful in orienting the reader to the goals of each section.

Generally, the organization of the book is logical and easy to follow. The only suggestion would be to add a section about diversity/anti-bias in Chapter 1 to emphasize how these topics are relevant across all of the curricular areas (i.e., in the same way that the authors discuss technology and media in the first chapter, to describe how it pervades various developmental domains).

The text is easy to read on a screen, and the photos, tables, etc. are clearly displayed. It would have been helpful to add a ‘landmark’ on each page naming the chapter title/topic, to facilitate browsing the resources provided in the book. For example, if a reader references Appendix C following its mention in the text, the reader may then have difficulty finding their way back up to the chapter to continue reading.

The textbook is well-written, with no noticeable grammatical errors.

Some forms of diversity are quite visible throughout the textbook; for example, there are examples, anecdotes, and photos of children who are linguistically-, culturally-, racially- and neuro-diverse. However, the approach to explaining anti-bias curricular approaches is limited (which seems particularly problematic in light of the racism-related uprisings occurring at the time that this review was completed).

Throughout the text, the authors reference licensing requirements, curriculum frameworks, etc. for the state of California. Instructors planning to use this text with students working towards licensure in other states will need to be prepared to clarify, adapt, or supplement with their own state guidelines, requirements, standards, etc.

Reviewed by Maureen Provost, Associate Professor of Early Childhood and Elementary Education, Mount Wachusett Community College on 6/23/20

The text covers most areas and ideas of the subject appropriately. Although NAEYC was referenced throughout, they have a new position statement on equity. Race, poverty, social inequities, and the importance of teaching these topics in early... read more

The text covers most areas and ideas of the subject appropriately. Although NAEYC was referenced throughout, they have a new position statement on equity. Race, poverty, social inequities, and the importance of teaching these topics in early childhood needs to be integrated in the text. The text does not have an effective index/glossary. Additionally, at the bottom of each page it would be helpful to write which content area is being covered. For example, in chapter 10 which covers Science add Ch. 10 and the word Science at the bottom of the page.

The content presented in the text is error free, unbiased, cited,and backed with solid research.

Some of the information is and will be important to the field of early education forever, such as theories, theorists, and child development. Brain research, AAP recommendations for media usage, ways to embed diversity, and trauma informed care were not adequately covered nor up to date. It is essential especially during this moment in history that we adhere to what we know is best for children. Although our students will be learning remotely, and are reaching out to families virtually, they need to be sure that parents/families understand the harm of too much media exposure.Although family involvement was mentioned at the end of each chapter in section IV, knowing that parents are children's first teachers and the importance of community involvement in early childhood education there should be a chapter dedicated to this topic.

Clarity rating: 4

The writing was clear, full of examples both with graphics, webs, charts, and photos. The language was appropriate for the context. Again, for any student that may struggle, such as an English Language Learner, a glossary of terms may be useful.

The framework for each section is consistent. Students will enjoy this easy to follow format. A strength of the text is that each section and chapter began with objectives and an introduction. This format was followed throughout.

For the most part the text could be easily and readily divisible into smaller reading sections. The vignettes and reflection boxes could be used as an assignment within themselves. The questions and scenarios posed would lead to further reflection by students.

Section IV: Infants should be discussed before toddlers, then preschoolers, and finally school age children. As a reader, and instructor I struggled with the order of this section in the text. The remaining topics are presented in a logical, clear fashion.

The interface was issue free. The charts, photos, and other display features are excellent.

The text is well written and with no noticeable grammatical errors.

Early educators set the foundation for human's life. It is imperative that we teach and address topics of anti-racism, anti-bias, multicultural education, equality, social justice, and celebrating differences in our classrooms with our students so that they can teach the children in their classrooms. This cannot be an add-on to what we are teaching at any level, rather we must integrate these messages in all that we do.

First and foremost, thank you to the authors for creating and making your text available for our students. I have been teaching early childhood and elementary education courses for more than 25 years and I will be using your text in the coming year; supplementing it with the important topics, and new information and research from our field as discussed in my review and aligning with state and national standards.

Reviewed by Jacquelynne Chase, Assistant Professor of Elementary and Early Childhood Education, Bridgewater State University on 5/27/20

Comprehensiveness was overall strong, but there were some areas that I felt should have been explored with more depth. For example, approaching social justice topics and those that are deemed “uncomfortable” that small children often times ask... read more

Comprehensiveness was overall strong, but there were some areas that I felt should have been explored with more depth. For example, approaching social justice topics and those that are deemed “uncomfortable” that small children often times ask were not fully addressed. I would recommend supplementing this book with "Black Ants and Buddhists: Thinking Critically and Teaching Differently in the Primary Grades" by Mary Cowhey to fully address social justice education in the early childhood grades. I think that more about home-school partnerships would have benefitted this text as well. What about the role of home visits?

After reviewing this text, it was clear to me that the information presented was accurate. I did not disagree with any of the statements that were made. In addition, the citations that were throughout the text substantiated the claims satisfactorily. I greatly appreciated the balanced perspective the authors provided by including the work of many different development and education theorists. From Piaget, to Dewey, there was satisfactory breadth. One point for consideration is while intentional teaching methods is highlighted, I think that unintentional teaching should also be highlighted. There are a myriad of implicitly learned skills that children learn while participating in their explicit learning experiences. Implicit learning could be a great way to then discuss inquiry-based learning.

Relevance was achieved in this text as the citations were well-connected. Also, the sources used to compile the information presented were all fairly recent. I appreciate that when citations that were not within the past few years were seminal pieces that have not been recreated due to their high regard in the field. With the increase of educational research on the importance of social justice education and multicultural understandings, I saw this as an area that hinders its relevance. In addition, as I am writing this review in the middle of the Covid-19 pandemic, I would be remiss if I did not also mention that technology is not emphasized more with specific resource options and parental recommendations to continue the learning at home. If a future teacher is reading this text and they need to teach remotely, I have to wonder if this text helps with the remote teaching mind-set. This isn't to say that the author could have anticipated the widespread need to teach remotely, but future in-class usage should pose these questions to think beyond the text.

I was impressed by the writing style in this textbook because I found it incredibly approachable and clear. The complex ideas pertaining to cognitive development were delineated and I was able to read through dense topics with ease. I think students would benefit from this writing style.

I would consider this text to be consistent in how it presents information. The writing did not show any biases and provided balanced perspective throughout. The language used throughout was academic and did not include colloquial phrasing consistently throughout it. With an introductory text like this, it is essential to offer consistent terminology usage to reinforce students' understandings of such terms to increase their comfort and familiarity with using the terms correctly.

Modularity was an area of improvement for this text. Some topics needed more detail and others needed less to be more even. This would have, in turn, supported the organizational structure of the text. For example, section 2 that was about setting the stage for play did not integrate the routine and expectations practice that is a major portion of "the first six weeks of school" that many schools use as a standard. Behavioral expectations and routine should be further highlighted. One area that there was a great deal of information that may have offered too much depth was health and safety. Arguably, this could be integrated thought the book as health and safety need to be considered in all aspects of early child hood education. The order of the topics covered built upon one another appropriately, however.

The organization of this book, as mentioned in regard to the modularity, is appropriate. The ideas build upon one another from chapter to chapter. Th chapters also nicely refer to ideas presented early in the text to further reinforce understanding. For example, to fully understand the importance of the learning environment and play-based learning, as discussed in sections 2 and 3, the reader has to have a full understanding of theoretical implications, as outlined in part 1.

Interface was approachable and eye-catching. It was not overwhelming. The amount of images was appropriate. They supported the information and each served an ample purpose.

Grammatical errors were not present to me. As previously mentioned, the writing style of clear and cohesive.

Cultural Relevance rating: 2

Cultural relevance was a final area that I think warrants revision. I think that bringing in cultural implications may add to the well-roundedness of this text. As previously mentioned, the text would benefit from multicultural education and social justice education recommendations. Since Dewey is mentioned as a seminal theorist, his work is an ideal connection to helping shape future citizens through the democratizing of education. Future citizens need to foster multicultural understandings and it is integral that the process begins in their most influential years: early childhood.

I think that this text would make a great course text for an introductory-level course on early childhood education. If the students have taken a course in development of educational psychology, they may find the theoretical portion repetitive, but it serves as a helpful refresher. This text could be used as a foundational text for a course, but to provide ample insight into early childhood education, I would recommend the instructor use supplementary readings to fill in the lacking areas outlined in my review, like multicultural education and social justice education.

Reviewed by Holly McCartney, Professor, James Madison University on 4/4/20

The text covers all areas and ideas of the subject appropriately and provides an effective index and/or glossary. Response: The book does cover the subject well, however there is no index or glossary. read more

The text covers all areas and ideas of the subject appropriately and provides an effective index and/or glossary. Response: The book does cover the subject well, however there is no index or glossary.

Content is accurate, error-free and unbiased.

Content is up-to-date, but not in a way that will quickly make the text obsolete within a short period of time. The text is written and/or arranged in such a way that necessary updates will be relatively easy and straightforward to implement. Some sources are from 2000 but still relevant today.

The text is written in lucid, accessible prose, and provides adequate context for any jargon/technical terminology used. I found the text easy to read with no jargon un explained.

The text is internally consistent in terms of terminology and framework. This text is very consistent in layout and framework – very easy to navigate

The text is easily and readily divisible into smaller reading sections that can be assigned at different points within the course (i.e., enormous blocks of text without subheadings should be avoided). The text should not be overly self-referential, and should be easily reorganized and realigned with various subunits of a course without presenting much disruption to the reader. Response: All of the above were noted in this text. Photos provide additional breaks in readings and there are “pause and reflect” questions for the reader to consider. Vignettes also offer readers opportunities to apply and clarify what is in the chapter.

Well organized by chapters & headings.

The text is free of significant interface issues, including navigation problems, distortion of images/charts, and any other display features that may distract or confuse the reader. I did not find any interface issues or concerns.

The text contains no grammatical errors, at least none that I could find in my reading.

The text is not culturally insensitive or offensive in any way. It should make use of examples that are inclusive of a variety of races, ethnicities, and backgrounds. All examples, photos and vignettes were diverse in the text.

One major omission: I could not find a glossary or index anywhere in the text. In text citations had no references to refer to either.

Table of Contents

Section I: Understanding How Children Learn

• Chapter 1: Foundations in Early Childhood Curriculum: Connecting Theory & Practice
• Chapter 2: The Importance of Play and Intentional Teaching

Section II: Developing Curriculum to Support Children's Learning

• Chapter 3: The Cycle of Curriculum Planning
• Chapter 4: Developing Curriculum for a Play Centered Approach

Section III: Setting the Stage for Children's Learning

• Chapter 5: Setting the Stage for Play: Environments
• Chapter 6: Guiding Behavior and Managing the Classroom

Section IV: Planning for Children's Learning

• Introduction to Planning for Preschoolers
• Chapter 7: Social and Emotional Development
• Chapter 8: Language and Literacy
• Chapter 9: Mathematics
• Chapter 10: Science
• Chapter 11: Creative Arts
• Chapter 12: History & Social Science
• Chapter 13: Physical Development
• Chapter 14: Health and Safety
• Introduction to Planning for Other Ages
• Chapter 15: What Curriculum Looks Like for Infants and Toddlers
• Chapter 16: What Curriculum Looks Like for School-Age Children

Section V: Making Children's Learning Visible

• Chapter 17: Documentation and Assessment

Ancillary Material

About the book.

Welcome to learning about how to effectively plan curriculum for young children. This textbook will address:

• Developing curriculum through the planning cycle
• Theories that inform what we know about how children learn and the best ways for teachers to support learning
• The three components of developmentally appropriate practice
• Importance and value of play and intentional teaching
• Different models of curriculum
• Process of lesson planning (documenting planned experiences for children)
• Physical, temporal, and social environments that set the stage for children’s learning
• Appropriate guidance techniques to support children’s behaviors as the self-regulation abilities mature.
• Physical development
• Language and literacy
• Creative (the visual and performing arts)
• Diversity (social science and history)
• Health and safety
• How curriculum planning for infants and toddlers is different from planning for older children
• Supporting school-aged children’s learning and development in out-of-school time through curriculum planning
• Making children’s learning visible through documentation and assessment

About the Contributors

Jennifer Paris

Kristin Beeve

Clint Springer

Contribute to this Page

(Stanford users can avoid this Captcha by logging in.)

• Send to text email RefWorks EndNote printer

An introduction to curriculum research and development

Available online, at the library.

SAL3 (off-campus storage)

More options.

• Find it at other libraries via WorldCat
• Contributors

Description

Creators/contributors, contents/summary, bibliographic information, browse related items.

• Stanford Home
• Maps & Directions
• Search Stanford
• Emergency Info
• Terms of Use
• Non-Discrimination
• Accessibility

© Stanford University , Stanford , California 94305 .

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

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

Predicting and improving complex beer flavor through machine learning

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

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

41k Accesses

778 Altmetric

Metrics details

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

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

Similar content being viewed by others

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

Rudraksh Tuwani, Somin Wadhwa & Ganesh Bagler

Sensory lexicon and aroma volatiles analysis of brewing malt

Xiaoxia Su, Miao Yu, … Tianyi Du

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

Kushagra Saini & Venkatnarayan Ramanathan

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).

Download references

Acknowledgements

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

Author information

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

Authors and Affiliations

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

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

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

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

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

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

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

Łukasz Kreft & Alexander Botzki

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

Philippe Malcorps & Luk Daenen

You can also search for this author in PubMed   Google Scholar

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.

Corresponding author

Correspondence to Kevin J. Verstrepen .

Ethics declarations

Competing interests.

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

Peer review

Peer review information.

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

Additional information

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

Supplementary information

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

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

Reprints and permissions

About this article

Cite this article.

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

Download citation

Received : 30 October 2023

Accepted : 21 February 2024

Published : 26 March 2024

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

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

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

Quick links

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

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