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Titles, Abstracts & Keywords

The title of your manuscript is usually the first introduction readers (and reviewers) have to your work. Therefore, you must select a title that grabs attention, accurately describes the contents of your manuscript, and makes people want to read further.

Additionally, Most people rely on electronic search engines to find articles. Usually they search through databases that contain only the title, author list, and abstract of articles, excluding any keywords attached to the article by its authors. It is therefore important to include in the title and/or abstract the words that potential readers of the article are likely to use during a search.

An effective title should:

• Convey the main topics of the study
• Highlight the importance of the research
• Attract readers

Writing a good title for your manuscript can be challenging. First, list the topics covered by the manuscript. Try to put all of the topics together in the title using as few words as possible. A title that is too long will seem clumsy, annoy readers, and probably not meet journal requirements.

• This title has too many unnecessary words.
• This title doesn’t give enough information about what makes the manuscript interesting.
• This is an effective title. It is short, easy to understand, and conveys the important aspects of the research.

Think about why your research will be of interest to other researchers. This should be related to the reason you decided to study the topic. If your title makes this clear, it will likely attract more readers to your manuscript.

TIP: Write down a few possible titles, and then select the best to refine further. Ask your colleagues their opinion. Spending the time needed to do this will result in a better title.

Selecting the most important information

The abstract must outline the most important aspects of the study while providing only a limited amount of detail on its background, methodology and results. Authors need to critically assess the different aspects of the manuscript and choose those that are sufficiently important to deserve inclusion in the abstract.

Once the abstract is ready it can be helpful to ask a colleague who is not involved in the research to go through it to ensure that the descriptions are clear. After you have drafted the manuscript, you should go back to the abstract to check that it agrees with the contents of the final manuscript.

Abstracts should have a structured format, serving several purposes: it helps authors summarize the different aspects of their work; it makes the abstract more immediately clear; and it helps peer reviewers and readers assess the contents of the manuscript.

The abstract structure varies between journals and between types of articles. Authors should check that the abstract of their manuscript is consistent with the requirements of the article type and journal to which the manuscript will be submitted. 

Keywords are a tool to help indexers and search engines find relevant papers. If database search engines can find your journal manuscript, readers will be able to find it too. This will increase the number of people reading your manuscript, and likely lead to more citations.

However, to be effective, keywords must be chosen carefully. They should:

• Represent the content of your manuscript
• Be specific to your field or sub-field
• Poor keywords : molecule, optics, lasers, energy lifetime
• Better keywords : single-molecule interaction, Kerr effect, carbon nanotubes, energy level structure
• Poor keywords : neuron, brain, OA (an abbreviation), regional-specific neuronal degeneration, signaling
• Better keywords : neurodegenerative diseases; CA1 region, hippocampal; okadaic acid; neurotoxins; MAP kinase signaling system; cell death
• Poor keywords : climate change, erosion, plant effects
• Better keywords : quaternary climate change, soil erosion, bioturbation

Next : Introduction, Methods & Materials

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Top 10 nature memoirs

Moving on from writing that holds the natural world at arm’s length, authors have begun using intimate life to show nature as a protagonist in itself

T he lockdowns of 2020/2021 galvanised and expanded a readership drawn to writing about the natural world. For the fortunate, the pause and hush offered space to witness the seasons unfolding, to hear voices other than our own, and to realise “our” story is deeply entangled with other lives. Undisturbed by the hum of road and shipping traffic, birdsong and the buzz of pollinators were amplified in our days’ soundtracks, and whales were recorded for the first time speaking in complex “sentences” . With the grave threat posed by the compound climate, ecological and biodiversity crises, a need and longing to repair our connection to the living world is keenly felt by many, and literature is playing a key role.

While the early nature writing canon leaned towards natural history – often at arm’s length, often written by a man out in a “wild” place – recent forms are bringing the issues of our time closer to home in memoir, making vivid the lives of others – human and not. The diversification of authors and of the places, cultures, and beings represented are lending vitality to the genre. A current fascination with the intelligences of the “‘more-than-human” world is firmly placing nature as protagonist rather than in service to a human plot.

Given the Arctic is such an active protagonist in climate change, it is perhaps surprising that the genre has seldom ventured to the far north. I was lucky enough to spend half a decade in Iceland, leaning into its genius loci. I still do, whenever I get the chance: it is a place that allows me to think differently. My debut The Raven’s Nest is an ecological memoir set in its otherworldly Westfjords. I call it ecological because, in life as on the page, it manifests everything as relational and interdependent. Entangled with the story of my marriage to an Icelander, other stories – of people, ravens, storms, the supernatural, life and death – build a weave in cycles of light and dark, into the titular nest. Amid volcanic eruptions and melting ice sheets, people and place are continuous. We are increasingly aware that the far north is not remote but central – in the regulation of climate, ocean currents, and therefore in all our futures. Treading a fine line between insider and outsider, I felt compelled to record what I witnessed and became a part of.

What happens when we listen to the voices which make a place? How might we feel our entanglements with the world to know it as home and treat it as such, even when we are unsure where we belong to? These books, many with a focus on the far north and spanning nearly a century, have inspired how I explore this interplay between place, people, living, thought and the body.

1. A Woman in the Polar Night by Christiane Ritter In the winter of 1933/4 Ritter, an Austrian painter and self-proclaimed “housewife”, makes the radical decision to join her hunter-trapper husband and a Norwegian hunter in Arctic Spitsbergen, living together in a tiny hut. Often alone for long spells, her mundane chores and her will to survive the extremes uncover marvels, both in the place and her spirit. The imagistic prose is exhilarating. Written as a journal – with long periods tellingly absent – we witness her transformation as she relinquishes herself to this place.

2. The Living Mountain by Nan Shepherd This dazzling gem written in the 1940s is an intimate journey into the Cairngorms Massif. Shepherd swims naked in clear mountain lochs, walks to be with the mountains as companions, naps on them, looks at them upside down between her legs, thrills in the glow of sparks from her hobnail boots while nightwalking. She probes at the possible, but there are no heroics here. The summit is not the point. It is rather to find new thoughts in the material – in the here and now – which is also metaphysical.

3. Arctic Dreams: Imagination and Desire in a Northern Landscape by Barry Lopez Written in the 1980s, this is a portal into the Arctic before it was synonymous with climate change. Lopez’s masterwork is the result of years of travel, delving into its histories, fauna, ice, water, stars, light and people – both his Indigenous companions and visiting scientists and workers. Scientifically rigorous, poetic and often reverent in its tone, Lopez builds a prismatic portrait. We are in the hands of a truly reliable and loving guide: an ecologist with a profound respect for knowledge of all kinds, so humble and curious that his words and thoughts seem almost prayerful.

4. Wild: An Elemental Journey by Jay Griffiths Partly in response to a debilitating depression, Griffiths makes a seven-year journey across the Earth to which she gives everything, in search of the meaning of “wild” – in the world and in herself. Using the elements as a structural device – Earth, Water, Fire, Wind (and she adds Ice) – she travels to the Peruvian Amazon, the Indonesian Ocean, the Australian bush, the mountains of West Papua, and the Canadian Arctic. Through sharp and heartful observation of these places and a high regard for the indigenous knowledges she encounters, her philosophical inquiry takes us to far corners of our minds, using a deeply embodied prose and wild language that writhes on the tongue.

5. Braiding Sweetgrass by Robin Wall Kimmerer Kimmerer’s world is animate and abundant. She is in love with it, and moves through it as if it loves her back. She asks what might “right relationship” look like in a damaged world? What is the language of reciprocity, the “grammar of animacy”? Drawing on her native Potawatomi culture, twined with her training as a bryologist, she shows us how science and culture, myth and reality are not opposed but live within one another.

6. Land of Love and Ruins by Oddný Eir This beautiful, pioneering autofiction mainly set in Iceland is a deceptively small and easy text which covers vast swathes of philosophical terrain in a variety of landscapes and homescapes. Written as a journal marked by feast days, equinoxes and the stages of the moon, Eir’s sensuous and breezy narrative voice explores what shape of existence might allow a woman to tend well to all those relationships which make her: to her living kin, to her ancestors, to a partner and to the Earth itself, without losing herself.

7. Small Bodies of Water by Nina Mingya Powles A series of loosely connected essays, Small Bodies of Water’s luscious prose flows deftly between moments in the author’s international life, each so vividly and sensuously portrayed as to immerse us in a world, and the age at which that world was lived. We move with Powles across time and geography – from adolescence to adulthood , from Borneo to New Zealand to London – exploring the fluid (and sometimes suspended) nature of identity and home. Deeply embodied, the pleasures of swimming, food, languages, flora and fauna are keenly felt as anchors, and an almost aqueous merging of the bloodline across generations pulses through it.

8. Soundings by Doreen Cunningham A failed relationship and resulting professional and financial ruin compel former climate journalist Cunningham to make a bold move. Taking out a bank loan, she travels with her young son along the migration route of the grey whales, from Mexico to the Canadian Arctic, back to a family of Iñupiaq whale hunters who took her in as one of their own years earlier on a research trip. Cunningham’s honouring of the hunters’ culture is nuanced by this entanglement, and the endless wait of the whale hunt is made fascinating by her quiet observations. The protagonists make a deeply refreshing triad: a single mother travelling with her child, learning from the whales how to parent.

9. On Time and Water by Andri Snær Magnason A poetic and heartful treatise which argues that we lack the metaphors to carry the enormity of the ecological crisis. We do not – cannot – truly understand words such as “climate change” and “ocean acidification”, so cannot respond appropriately. Approaching them slant through myth and family history (his grandparents honeymooned as participants in one of the first Icelandic glaciological surveys) Magnason’s simple proposition elicits a shift in perspective to connect to the future “in an intimate and urgent way”. By invoking our time as “the handshake of generations” – the period inhabited by those who have loved us, ourselves, and those who we will love in the future – he brings the impacts of this unknown future close to our hearts.

10. Islands of Abandonment: Life in the Post Human Landscape by Cal Flyn In rigorously researched and poetic prose Flyn manages a rare feat: an angle on nature writing that is entirely new. Travelling to places that humans once inhabited, then destroyed and (mostly) left, she returns to see what has thrived in their wake. From the abandoned buildings of Detroit to Chernobyl to the “bings”(spoil heaps) of Scotland’s West Lothian, she finds a strange kind of abundance. Flyn proposes that rather than purely lamenting a lost vitality in the natural world, we might also reframe and cultivate our aesthetic sensibilities to see, and appreciate, the life in ruins.

The Raven’s Nest by Sarah Thomas is published by Atlantic Books. To help the Guardian and Observer, order your copy from guardianbookshop.com . Delivery charges may apply.

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Nature Writing Examples

by Lisa Hiton

From the essays of Henry David Thoreau, to the features in National Geographic , nature writing has bridged the gap between scientific articles about environmental issues and personal, poetic reflections on the natural world. This genre has grown since Walden to include nature poetry, ecopoetics, nature reporting, activism, fiction, and beyond. We now even have television shows and films that depict nature as the central figure. No matter the genre, nature writers have a shared awe and curiosity about the world around us—its trees, creatures, elements, storms, and responses to our human impact on it over time.

Whether you want to report on the weather, write poems from the point of view of flowers, or track your journey down a river in your hometown, your passion for nature can manifest in many different written forms. As the world turns and we transition between seasons, we can reflect on our home, planet Earth, with great dedication to description, awe, science, and image.

Journal Examples: Keeping Track of Your Tracks

One of the many lost arts of our modern time is that of journaling. While keeping a journal is a beneficial practice for all, it is especially crucial to nature writers. John A. Murray , author of Writing About Nature: A Creative Guide , begins his study of the nature writing practice with the importance of journaling:

Nature writers may rely on journals more consistently than novelists and poets because of the necessity of describing long-term processes of nature, such as seasonal or environmental changes, in great detail, and of carefully recording outdoor excursions for articles and essays[…] The important thing, it seems to me, is not whether you keep journals, but, rather, whether you have regular mechanisms—extended letters, telephone calls to friends, visits with confidants, daily meditation, free-writing exercises—that enable you to comprehensively process events as they occur. But let us focus in this section on journals, which provide one of the most common means of chronicling and interpreting personal history. The words journal and journey share an identical root and common history. Both came into the English language as a result of the Norman Victory at the Battle of Hastings in 1066. For the next three hundred years, French was the chief language of government, religion, and learning in England. The French word journie, which meant a day’s work or a day’s travel, was one of the many words that became incorporated into English at the time[…]The journal offers the writer a moment of rest in that journey, a sort of roadside inn along the highway. Here intellect and imagination are alone with the blank page and composition can proceed with an honesty and informality often precluded in more public forms of expression. As a result, several important benefits can accrue: First, by writing with unscrutinized candor and directness on a particular subject, a person can often find ways to write more effectively on the same theme elsewhere. Second, the journal, as a sort of unflinching mirror, can remind the author of the importance of eliminating self-deception and half-truths in thought and writing. Third, the journal can serve as a brainstorming mechanism to explore new topics, modes of thought, or types of writing that otherwise would remain undiscovered or unexamined. Fourth, the journal can provide a means for effecting a catharsis on subjects too personal for publication even among friends and family. (Murray, 1-2)

A dedicated practice of documenting your day, observing what is around you, and creating your own field guide of the world as you encounter it will help strengthen your ability to translate it all to others and help us as a culture learn how to interpret what is happening around us.

Writing About Nature: A Creative Guide by John A. Murray : Murray’s book on nature writing offers hopeful writers a look at how nature writers keeps journals, write essays, incorporate figurative language, use description, revise, research, and more.

Botanical Shakespeare: An Illustrated Compendium of All the Flowers, Fruits, Herbs, Trees, Seeds, and Grasses Cited by the World’s Greatest Playwright by Gerit Quealy and Sumie Hasegawa Collins: Helen Mirren’s foreword to the book describes it as “the marriage of Shakespeare’s words about plants and the plants themselves.” This project combines the language of Shakespeare with the details of the botanicals found throughout his works—Quealy and Hasegawa bring us a literary garden ripe with flora and fauna puns and intellectual snark.

• What new vision of Shakespeare is provided by approaching his works through the lens of nature writing and botanicals?
• Latin and Greek terms and roots continue to be very important in the world of botanicals. What do you learn from that etymology throughout the book? How does it impact symbolism in Shakespeare’s works?
• Annotate the book using different colored highlighters. Seek out description in one color, interpretation in another, and you might even look for literary echoes using a third. How do these threads braid together?

The Living Mountain: A Celebration of the Cairngorm Mountains of Scotland by Nan Shepherd : The Living Mountain is Shepherd’s account of exploring the Cairngorm Mountains of Scotland. Part of Britain’s Arctic, Shepherd encounters ravenous storms, clear views of the aurora borealis, and deep snows during the summer. She spent hundreds of days exploring the mountains by foot.

• These pages were written during the last years of WWII and its aftermath. How does that backdrop inform Shepherd’s interpretation of the landscape?
• The book is separated into twelve chapters, each dedicated to a specific part of life in the Cairngorms. How do these divisions guide the writing? Is she able to keep these elements separate from each other? In writing? In experiencing the land?
• Many parts of the landscape Shepherd observes would be expected in nature writing—mountains, weather, elements, animals, etc. How does Shepherd use language and tone to write about these things without using stock phrasing or clichéd interpretations?

Birds Art Life: A Year of Observation by Kyo Maclear : Even memoir can be delivered through nature writing as we see in Kyo Maclear’s poetic book, Birds Art Life . The book is an account of a year in her life after her father has passed away. And just as Murray and Thoreau would advise, journaling those days and the symbols in them led to a whole book—one that delicately and profoundly weaves together the nature of life—of living after death—and how art can collide with that nature to get us through the hours.

• How does time pass throughout the book? What techniques does Maclear employ to move the reader in and out of time?
• How does grief lead Maclear into art? Philosophy? Nature? Objects?
• The book is divided into the months of the year. Why does Maclear divide the book this way?
• What do you make of the subtitles?

Is time natural? Describe the relationship between humans and time in nature.

So dear writers, take to these pages and take to the trails in nature around you. Journal your way through your days. Use all of your senses to take a journey in nature. Then, journal to make a memory of your time in the world. And give it all away to the rest of us, in words.

Lisa Hiton is an editorial associate at Write the World . She writes two series on our blog: The Write Place where she comments on life as a writer, and Reading like a Writer where she recommends books about writing in different genres. She’s also the interviews editor of Cosmonauts Avenue and the poetry editor of the Adroit Journal .

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What is nature writing?

What we talk about when we talk about nature writing.

“Nature writing can be defined as non-fiction or fiction prose or poetry about the natural environment.” This is actually its definition on Wikipedia.

For the purposes of this prize, we're accepting only non-fiction prose submissions (see last week's resources on breaking down the brief ), but in general, nature writing can mean many more things and cover lots of different ideas. As such, there’s a whole variety of approaches to writing a book in this genre. Different types of nature writing books can include: factual books such as field guides, natural history told through essays, poetry about the natural world, literary memoir and personal reflections.

Typically, nature writing is writing about the natural environment. Your book might take a look at the natural world and examine what it means to you or what you’ve encountered in the environment. You could frame this idea through a personal lens.

Perhaps you want to take a more focused or factual approach and look at individual flora and fauna in detail. Recent books that we’ve enjoyed have looked at topics such as beekeeping, owls, social and cultural history, trees, swimming, cows and have offered personal observation and reflection on their chosen topics.

You might be writing about the landscape, from farming to remote islands or city life. You may want to write about the fauna and flora of a whole region, or just one animal or a single tree. You don’t need to go out into the wilderness to write about nature and you don’t need to be hiking for three months in a remote area either. Most importantly, we believe the best books on nature writing convey a clear sense of place and mainly focus on the natural world and our human relationship with it.

The Nan Shepherd Prize aims to find the next big voice in nature writing from emerging writers, and we can’t wait to read about what nature means to you.

• Read an academic paper on New Nature Writing here .
• ‘Land Lines’ was a two-year project funded by the Arts and Humanities Research Council, and is a collaboration between researchers at the Universities of Leeds, Sussex and St Andrews. The project carried out a sustained study on modern British nature writing, beginning in 1789 with Gilbert White’s seminal study, The Natural History of Selborne, and ending in 2014 with Helen Macdonald’s prize-winning memoir, H is for Hawk. You can look at their website here .
• Read about nature writing throughout history (this is a US perspective) here .
• Read about which nature books have inspired today’s contemporary nature writers here .
• Read this guide to nature writing from Sharmaine Lovegrove, publisher of Dialogue Books, who teamed up with the Forestry Commission to find undiscovered nature writers here .

Over @NanPrize we’ve been sharing examples of our favourite nature writing books, so if you want to see some specific examples of recent favourites, that might be a good place to start. We’ve also got a collection here which will give you an idea as to what books we like to publish in the nature writing genre.

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Nature Writing is Survival Writing: On Rethinking a Genre

Michelle nijhuis thinks it’s time for some new perspectives.

If there were a contest for Most Hated Genre, nature writing would surely take top honors. Other candidates—romance, say—have their detractors, but are stoutly defended by both practitioners and fans. When it comes to nature writing, though, no one seems to hate container and contents more than nature writers themselves.

“‘Nature writing’ has become a cant phrase, branded and bandied out of any useful existence, and I would be glad to see its deletion from the current discourse,” the essayist Robert Macfarlane wrote in 2015. When David Gessner, in his book Sick of Nature , imagined a party attended by his fellow nature writers, he described a thoroughgoing dud: “As usual with this crowd, there’s a whole lot of listening and observing going on, not a lot of merriment.”

Critics, for their part, have dismissed the genre as a “solidly bourgeois form of escapism,” with nature writers indulging in a “literature of consolation” and “fiddling while the agrochemicals burn.” Nature writers and their work are variously portrayed, fairly and not, as misanthropic, condescending, and plain embarrassing. Joyce Carol Oates, in her essay “Against Nature,” enumerated nature writing’s “painfully limited set of responses” to its subject in scathing all caps: “REVERENCE, AWE, PIETY, MYSTICAL ONENESS.”

Oates, apparently, was not consoled.

The persistence of nature writing as a genre has more to do with publishers than with writers. Labels can usefully lash books together, giving each a better chance of staying afloat in a flooded marketplace, but they can also reinforce established stereotypes, limiting those who work within a genre and excluding those who fall outside its definition. As Oates suggested, there are countless ways to think and write about what we call “nature,” many of them urgent. But nature writing, as defined by publishers and historical precedent, ignores all but a few.

The nature-writing genre emerged in the late 1700s, during the peculiar moment when nature, as Europeans and North American intellectuals saw it, was no longer fearfully mysterious but not yet endangered. The scientific classification of species had brought some apparent order to undomesticated landscapes, allowing writers such as William Bartram, a botanist who traveled through the American South shortly before the Revolutionary War, to perceive not a tangle of flora and fauna but “an infinite variety of animated scenes, inexpressibly beautiful and pleasing.”

Such “appreciative aesthetic responses to a scientific view of nature,” as the writer and naturalist David Rains Wallace once described them, were products not only of their time and place but their culture and class. Scientific views of nature are not the only possible views, of course, and as many anthropologists and linguists have pointed out, the concept of “nature” as a collection of objects, separate from but subservient to humans, is also far from universal.

In the 19th century, many of the thinkers we now call nature writers took some exception to the genre’s original project. While Ralph Waldo Emerson famously saw human transcendence as the primary purpose of the non-human world, his rebellious protégé Henry David Thoreau was more interested in other forms of life for their own sake, and more willing to get his literal and metaphorical boots muddy. John Muir, though notoriously dismissive of the human history of the Sierra Nevada , had unusually egalitarian ideas about other species, considering even lizards, squirrels, and gnats to be fellow occupants of the planet.

As I learned while researching my book Beloved Beasts , a history of the modern conservation movement, the rise of the science of ecology in the early 20th century made it ever clearer that the boundaries between humans and “nature” were more linguistic and cultural than physical. Rachel Carson, who cited Thoreau as one of her primary influences, further expanded the nature-writing genre by tying the fate of other species to the fate of human bodies.

Any genre can only stretch so far, though, and the limitations of nature writing are inscribed in its very name. Nature writing still tends to treat its subject as “an infinite variety of animated scenes,” and while the genre’s membership and approaches have diversified somewhat in recent years, its prizewinners resemble its founders : mostly white, mostly male, and mostly from wealthy countries. The poet and essayist Kathleen Jamie calls them Lone Enraptured Males .

Meanwhile, writers in every genre and discipline are wrestling with the relationship between humans and the rest of life, recognizing that while writing about other species is often about wonder and uplift, it is also, inevitably, about survival—the survival of all species, including our own. Amitav Ghosh, whose novels often follow the connections among species and habitats—humans and snakes, tigers and dolphins, land and sea—recently published The Nutmeg’s Curse , his second book-length essay about the literature, history, and politics of climate change. (The first was The Great Derangement , published in 2016.)

Science-fiction writer Jeff VanderMeer returns again and again to the unstable boundaries between humans and other species, most recently in his novel Hummingbird Salamander . Margaret Atwood, a dedicated birdwatcher, wrote that the sight of red-necked crakes “scuttling about in the underbrush” in northern Australia inspired her dystopian MaddAddam trilogy . Historians such as Dina Gilio-Whitaker, the author of As Long as Grass Grows , and Nick Estes, the author of Our History Is The Future , document the damage done to Indigenous cultures and all species by centuries of capitalism and colonialism. These and many other works acknowledge that humans are both observers of and participants in the network of life on earth—and that our roles, while often destructive, can be constructive, too.

Today, the nature-writing genre reminds me of the climate-change beat in journalism: the stakes and scope of the job have magnified to the point that the label is arguably worse than useless, misrepresenting the work as narrower than it is and restricting its potential audience. The state of “nature,” like the state of the global climate, can no longer be appreciated from a distance, and its literature can no longer be confined to a single shelf. If we must give it a label, I say we call it survival writing. Or, better yet, writing.

 __________________________________

Michelle Nijhuis’s book Beloved Beasts is available through W.W. Norton & Company. Copyright © 2022.

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A Nature Research Service

• On-demand Courses
• Write & publish

Writing a Research Paper: 2nd Edition

For researchers in the natural sciences looking to write effective research papers

With insights from 12 experts in scientific writing, including Nature Portfolio Editors

14.5 hours of learning

10-50-minute lessons

5-module course with certificate

• About this course

Writing research papers allows you to contribute to the scientific record, and is critical for advancing your career. To ensure that the findings you have invested so much effort in have an impact on your scientific community, it is pivotal that the paper you write is effective. In other words, it needs to be informative, concise, well structured and engaging . An effective research paper makes it easier to convey to editors and reviewers the significance of presented outcomes. It also provides researchers with the information they need, boosting dissemination of your work.

This course will introduce you to powerful narrative tools and principles of scientific writing. It will also teach you how to apply them to your next manuscript. Don’t miss out and start learning today!

This is the second edition of our highly popular ‘Writing a Research Paper’ course. We recommend you take this updated version even if you have already started the first edition . However, the previous version of the course is still available, if you wish to continue it to obtain your certificate. If you have already completed the first edition of the course you will still be able to access your certificate on the progress page under “My dashboard”. 

The benefits of this new 2nd edition

• Restructured content for a better learning experience
• Enriched content with extensive real-world examples
• Bite-size lessons on each topic to fit busy schedules
• Strategies to apply narrative tools when writing research papers
• Detailed examples for explaining concepts, taken from real papers where possible
• Step-by-step strategies and how to apply them to your research paper
• Portfolio activities that empower you to transfer knowledge into practice.

What you'll learn

• Understand what makes an effective research paper
• Gain insights on the preferred structure of a Nature published paper
• Leverage narrative tools and their application to scientific writing
• Master the principles of scientific writing style
• Write a research paper section by section
• Develop effective titles and abstracts
• Finalise your research paper for submission.

Free Sample Understanding the elements of an effective research paper

9 lessons 2h

Free Sample Applying narrative tools to your research paper

7 lessons 3h

Free Sample Using the principles of scientific writing style for your research paper

22 lessons 2h

Free Sample Writing your research paper section by section

32 lessons 5h 30m

Free Sample Finalising your research paper for submission

6 lessons 2h

Free Sample Writing a Research Paper: 2nd Edition: Free Sample

No subscription yet? Try this free sample to preview lessons from the course

8 lessons 2h

Start this module

Select the dropdown to explore an overview of the content for each module

Module 1: Understanding the elements of an effective research paper

• Welcome to the course
• The structure of a research paper
• What makes an effective research paper
• Strategies to write an effective research paper
• Overview of the strategies for writing an effective research paper
• Narrative tools and research papers – how they work together
• Principles of scientific writing style
• Key points about the strategies for writing an effective research paper
• Module summary

Module 2: Applying narrative tools to your research paper

• Introduction
• The key message
• The audience
• The story arc
• Steps to develop your story arc
• The evidence

Module 3: Using the principles of scientific writing style for your research paper

Informative writing

• Introduction to informative writing
• Pitfalls that can undermine the informativeness of your research paper
• Master the basics of informative writing
• Take informative writing to the next level
• Apply the key points of informative writing to your research paper

Concise writing

• Introduction to concise writing
• Pitfalls that can undermine the conciseness of your research paper
• Master the basics of concise writing
• Take concise writing to the next level
• Apply the key points of concise writing to your research paper

Well-structured writing

• Introduction to well-structured writing 
• Pitfalls that can undermine the structure of your research paper
• Master the basics of well-structured writing
• Take well-structured writing to the next level
• Key takeaways for ensuring well-structured writing

Engaging writing

• Introduction to engaging writing
• Pitfalls that can undermine the engaging of your research paper
• Master the basics of engaging writing
• Take engaging writing to the next level
• Key takeaways for writing engagingly

Module 4: Writing your paper section by section

• Tools to help you plan and write the sections of your paper

The methods section

• The purpose of the methods section
• What to include in the methods section
• How to structure the methods section
• The specific writing style of the methods section
• Common pitfalls in the methods section
• Key points about writing the methods section

The results section

• The purpose of the results section
• What to include in the results section
• How to structure the results section
• The specific writing style of the results section
• Common pitfalls in the results section
• Key points about writing the results section

The discussion section

• The purpose of the discussion section
• What to include in the discussion section
• How to structure the discussion section
• The specific writing style of the discussion section
• Common pitfalls in the discussion section
• Key points about writing the discussion section

The conclusion section

• The purpose of the conclusion section
• How to structure the conclusion section
• The specific writing style of the conclusion section
• Common pitfalls in the conclusion section
• Key points about writing the conclusion section

The introduction section

• The purpose of the introduction section
• What to include in the introduction section
• How to structure the introduction section
• The specific writing style of the introduction section
• Common pitfalls in the introduction section
• Key points about writing the introduction section

Module 5: Finalising your research paper for submission

• Assemble an appealing title
• Compose an effective abstract
• Revise your paper before submission
• Course summary

Developed with experts in scientific writing

The course was developed and refined by an international panel of academics, editors and experts in writing research papers.

Meet the expert panel that have helped shape and refine the content of the course:

Davide Esposito

Chief Editor, Nature Catalysis

Peter Gorsuch

Linguistic Design Manager, Springer Nature

Joshua Schimel

Professor of Ecology, Evolution and Marine Biology, University of California Santa Barbara and author of Writing Science

Sadaf Shadan

Biological Sciences Team Manager and Senior Editor, Nature

Advice from experienced science editors and researchers

The course contains additional insights from other experts in scientific writing:

Magdalena Skipper

Editor-in-Chief, Nature   and Chief Editorial Advisor,  Nature Portfolio

Malena Rice

Assistant Professor, Department of Astronomy, Yale University and named to the Forbes 30 under 30 list in science

Alexia-Ileana Zaromytidou

Chief Editor, Nature Cancer

Xiaodong Zou

Professor, Department of Materials and Environmental Chemistry, Stockholm University

Anna Ploszajski

Freelance materials scientist and storyteller

Tamara Goldin

Chief Editor, Nature Geoscience

Zoë Doubleday

Australian Research Council (ARC) Future Fellow, University of South Australia

Senior Editor, Nature Nanotechnology

Why should you take this course?

Discover related courses, research integrity: publication ethics.

Examine ways you can handle ethical arises that can arise as you publish your research

Publishing a Research Paper

Learn about the publication process and the things you need to consider

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Disabled Poets Prize

Deptford literature festival, nature nurtures, early career bursaries, criptic x spread the word, lewisham, borough of literature, a pocket guide to nature writing.

In this glorious Pocket Guide, Kerri ní Dochartaigh highlights the value of Nature writing, whilst sharing her personal tips, resources and opportunities on how you can get inspired to write. 

What do we really mean when we talk about ‘nature writing’? And what do we even mean when we talk about ‘nature’?

Nature writing , like pockets , is a politicised thing – embroidered with different threads; depending on your race , class , gender , (dis)ability, wealth or place in this world. Is there space here for you? Do you feel safe? There has never been a more important time to make safe space: for every single thing on this earth. The writing, then, will just do its own thing, you see. It will come and go as it pleases, like a moth to a big aul’ light.

How about a wee browse through these background reads , and then we might, in the words of Edwyn Collins , (the most inspiring nature punk on earth): ‘Rip it up and start again’?… (What is nature writing if not the constant riotous act of starting again? Of learning, again, to listen and to look, to draw close and keep our distance, to break and to weep; to get back up and love the world afresh?) In this NY Times piece three and a half decades ago, David Rains Wallace wrote ‘NATURE writing is a historically recent literary genre, and, in a quiet way, one of the most revolutionary.’

We’re ready for this revolution but who is going to lead it?

For far too long we have allowed a very particular voice, from a very particular background, with a very particular outlook – dominate bookshop displays, library shelves, reading lists, bestseller rankings and our own homes. This, the idea that there has only ever been one nature story, is wildly incorrect. Other standpoints, other views, other stories, other voices: have always been there. In ‘Heart Berries’ Terese Marie Mailhot summarises: ‘So, where are we? Where we have always been. Where are you?’

To write about nature with truth and integrity means to ask questions about the past and the future – who, where and what have been mistreated – and how do we make that stop, through how we approach this genre? I only want to be a part of any gathering where every single one of us is there as an equal.

So, who is doing the important work in this area? Where should you go to read more? Where should you send your fledgling words?

Let’s start with The Willowherb Review because I think they are incredible. Their aim is ‘to provide a digital platform to celebrate and bolster nature writing by emerging and established writers of colour’, and already their writers have seen prize nominations and awards (all links on the site). Most importantly of all the writing is cracking; beautiful, raw and necessary. Jessica J Lee, the editor, has a no nonsense approach to the genre that I deeply admire. If you are a nature writer of colour, check out their website for submission dates.

Jessica has also organised a reading group, Allies in the Landscape , a fantastic support for nature writers and anyone wanting to widen their reading in the genre.

The folks at Caught by the River do stellar work for those who love the natural world across a plethora of genres. If you are in need of inspiration, or events to go to when we can, start here. You will not be let down. They read everything they’re sent but are a busy crew so – as with submitting anywhere, patience is kindness.

More folks with big hearts and brilliant writing are The Clearing .

The art of nature writing itself can be a children’s story, a poem, a list, a eulogy, a translation – it can be fiction or non – written or other – short or long; it is anything that takes our world and makes it sing. The best nature writing, for me, speaks of transformation – anything from a fiercely hungry caterpillar, through to strong women swimming themselves to safe places – making lists of yellow things for their sick fathers – moulding grief through sowing seeds: the best nature writers might not even call themselves that at all. Some books I have recently loved are: ‘ Trace’ by Lauret Savoy, ‘Braiding Sweet Grass’ by Robin Wall Kimmerer Elizabeth J Burnett’s ‘ The Grassling’ , ‘ Bulbul Calling’ by Pratyusha, Seán Hewitt’s ‘ Tongues of Fire’ , Jessica J Lee’s ‘ Two Trees Make a Forest’ , ‘The Promise’ by Nicola Davies and ‘ The Diary of a Young Naturalist’ by Dara McAnulty. I return over and over to writers like Amy Liptrot, Kathleen Jamie, Annie Dillard, Robert McFarlane and others but I am constantly trying to find new voices, approaches and stories – new to me, not new in their existence, of course: it’s important to make that distinction in a genre such as this.

The important thing, needed now more than ever, is that they each take their place in this symphony of hope.

There is room, here, on these mountains and beaches, in these gardens and fields, in these bodies of water – in ASDA parking lots and unsafe spaces – on the streets, and in every place both ‘wild’  and not (both outer and inner) – for you and your story.

From me to you, here a few exercises I return to over and over as a means to get started…

Choose something – a moth, the colour blue, a tree, a wren, a pebble, the waves on the beach – and write about it as if the reader will have never before seen or heard of it. Really stay with the description for as long as you can, and try to get down to what it really is: its thingness. Make your description almost like a love letter in how much care you take with it, and the depth of your words. Another interesting take on this is to write yourself as the thing – to really imagine, say, going through all the stages of the cycle from caterpillar to moth – or the ebb and flow you would experience as a particular body of water etc.

Journal – at least three free-flow pages without thinking about them or rereading – every single day. This one really helps to get me out of my normal flow of thought, and does something to my brain that welcomes experiences, creatures and thoughts that are conducive to nature writing. It really doesn’t matter if I am not writing about nature in these pages, really that is not the point, I think it’s in the act of carving out space and time – bringing awareness to the act. The space in which I write these can be a cafe, on a train, or at home, and still I find myself in a wild place, one that is on the inside not the outside.

The thing that most helps me to write about the natural world is actually being in it – walking, swimming, running, laying, laughing, crying – just allowing myself to be outside as much as I can seems to be the best way for me to try to write about the world we share.

Once you feel more confident, you might be interested in entering your writing into a prize or sharing it online (an incredible amount of links can also be found in the hyperlinked pages too) and I can share only a fraction but here are a few that sing to me:

https://nanshepherdprize.com   This prize is changing the landscape of this genre. Every single section on the site is invaluable.

https://www.thenaturelibrary.com

Christina Riley has put such a wonderful thing together here. Have a browse / follow.

https://www.lonewomeninflashesofwilderness.com/about

Clare Archibald’s inspiring, inclusive site is really making ripples in this area.

https://beachbooks.blog/about/ A gorgeous, generous sea library full of joy.

https://www.elementumjournal.com  Submissions are closed for this journal but there is lots of fine work to peruse.

https://www.elsewhere-journal.com   This is a superb journal of place, and submission are open.

The Moth Nature Writing prize , The Rialto Nature Poetry Competition and others are great to look at too. There are courses, schemes and more online but I think the most important place to start is by looking and listening, reading and caring; by loving the world and by writing it down in any way you can.

For me, any time any of us looks and listens to the non-human beings we share this earth with – when we pause in humility to acknowledge the interconnectedness of us all – the threads tying us to each other; invisible often, but so strong – we are playing a part in making this a safer, fairer earth. To go one step further, and to write about this connection, to name, explore, celebrate and honour – whether we choose a swan or a stone, a moth or a lough, the wild sea or our gut flora; things nearby or faraway, the known or unknown –   we are shining light on one of the most important truths of this earth: our need to be alive, and to remain connected to every other living thing. There is power in trying to find traces of ourselves in the nonhuman, as well as acknowledging our difference. In searching for the beat of something unnameable;  the simple act of being alive, at the same time, as each other, and in the same way as even the smallest insect.

Nature Writing holds the hope, for me, of reminding us how to treat everyone and everything on the earth. The best nature writing shines light on places we need to see; on beings we need to learn to accept as our equal. It is only a proper telling of the earth if we can tread gently on the land and the non-human as well as human while we do it. If we can speak honestly of the places and the past – if we can find a way to write it where every single one of us is heard; where each one of us, and our stories, are kept safe.

Kerri ní Dochartaigh is from the North West of Ireland but now lives in the very middle. She writes about nature, literature and place for The Irish Times, The Dublin Review of Books, Caught By The River and others. Her first book, Thin Places ,  is out with Canongate in January 2021. @kerri_ni @whooperswan

Published 7 July 2020

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What is Nature Writing?

Definition and Examples

• An Introduction to Punctuation
• Ph.D., Rhetoric and English, University of Georgia
• M.A., Modern English and American Literature, University of Leicester
• B.A., English, State University of New York

Nature writing is a form of creative nonfiction in which the natural environment (or a narrator 's encounter with the natural environment) serves as the dominant subject.

"In critical practice," says Michael P. Branch, "the term 'nature writing' has usually been reserved for a brand of nature representation that is deemed literary, written in the speculative personal voice , and presented in the form of the nonfiction essay . Such nature writing is frequently pastoral or romantic in its philosophical assumptions, tends to be modern or even ecological in its sensibility, and is often in service to an explicit or implicit preservationist agenda" ("Before Nature Writing," in Beyond Nature Writing: Expanding the Boundaries of Ecocriticism , ed. by K. Armbruster and K.R. Wallace, 2001).

Examples of Nature Writing:

• At the Turn of the Year, by William Sharp
• The Battle of the Ants, by Henry David Thoreau
• Hours of Spring, by Richard Jefferies
• The House-Martin, by Gilbert White
• In Mammoth Cave, by John Burroughs
• An Island Garden, by Celia Thaxter
• January in the Sussex Woods, by Richard Jefferies
• The Land of Little Rain, by Mary Austin
• Migration, by Barry Lopez
• The Passenger Pigeon, by John James Audubon
• Rural Hours, by Susan Fenimore Cooper
• Where I Lived, and What I Lived For, by Henry David Thoreau

Observations:

• "Gilbert White established the pastoral dimension of nature writing in the late 18th century and remains the patron saint of English nature writing. Henry David Thoreau was an equally crucial figure in mid-19th century America . . .. "The second half of the 19th century saw the origins of what we today call the environmental movement. Two of its most influential American voices were John Muir and John Burroughs , literary sons of Thoreau, though hardly twins. . . . "In the early 20th century the activist voice and prophetic anger of nature writers who saw, in Muir's words, that 'the money changers were in the temple' continued to grow. Building upon the principles of scientific ecology that were being developed in the 1930s and 1940s, Rachel Carson and Aldo Leopold sought to create a literature in which appreciation of nature's wholeness would lead to ethical principles and social programs. "Today, nature writing in America flourishes as never before. Nonfiction may well be the most vital form of current American literature, and a notable proportion of the best writers of nonfiction practice nature writing." (J. Elder and R. Finch, Introduction, The Norton Book of Nature Writing . Norton, 2002)

"Human Writing . . . in Nature"

• "By cordoning nature off as something separate from ourselves and by writing about it that way, we kill both the  genre and a part of ourselves. The best writing in this genre is not really 'nature writing' anyway but human writing that just happens to take place in nature. And the reason we are still talking about [Thoreau's] Walden 150 years later is as much for the personal story as the pastoral one: a single human being, wrestling mightily with himself, trying to figure out how best to live during his brief time on earth, and, not least of all, a human being who has the nerve, talent, and raw ambition to put that wrestling match on display on the printed page. The human spilling over into the wild, the wild informing the human; the two always intermingling. There's something to celebrate." (David Gessner, "Sick of Nature." The Boston Globe , Aug. 1, 2004)

Confessions of a Nature Writer

• "I do not believe that the solution to the world's ills is a return to some previous age of mankind. But I do doubt that any solution is possible unless we think of ourselves in the context of living nature "Perhaps that suggests an answer to the question what a 'nature writer' is. He is not a sentimentalist who says that 'nature never did betray the heart that loved her.' Neither is he simply a scientist classifying animals or reporting on the behavior of birds just because certain facts can be ascertained. He is a writer whose subject is the natural context of human life, a man who tries to communicate his observations and his thoughts in the presence of nature as part of his attempt to make himself more aware of that context. 'Nature writing' is nothing really new. It has always existed in literature. But it has tended in the course of the last century to become specialized partly because so much writing that is not specifically 'nature writing' does not present the natural context at all; because so many novels and so many treatises describe man as an economic unit, a political unit, or as a member of some social class but not as a living creature surrounded by other living things." (Joseph Wood Krutch, "Some Unsentimental Confessions of a Nature Writer." New York Herald Tribune Book Review , 1952)
• Creative Nonfiction
• Defining Nonfiction Writing
• A Guide to All Types of Narration, With Examples
• What You Should Know About Travel Writing
• Notable Authors of the 19th Century
• Genres in Literature
• Top 5 Books about Social Protest
• Must Reads If You Like 'Walden'
• Great Summer Creative Writing Programs for High School Students
• Point of View in Grammar and Composition
• Ways of Defining Art
• Ralph Waldo Emerson: American Transcendentalist Writer and Speaker
• What Is a Synopsis and How Do You Write One?
• The Power and Pleasure of Metaphor
• What Literature Can Teach Us

  Current Winners | Past Winners by Year | Application Procedures | Press Releases & Media Information | Master Lists | Schedule | Contact | Website Index

Current Winners

Past Winners by Year

Application Information

Press Releases & Media Information Page

High Resolution Images of Winning Books

Master Lists

National Coverage of NOBA

Judging Process

NOBA Medallion

Webmaster Information

Website Index

The National Outdoor Book Awards

The National Outdoor Book Awards (NOBA) is the outdoor world's largest and most prestigious book award program.  It is a non-profit, educational program, sponsored by the National Outdoor Book Awards Foundation, Association of Outdoor Recreation and Education , and Idaho State University. 

The purpose of the awards is to recognize and encourage outstanding writing and publishing.  Each fall in early November, the NOBA Foundation announces the winners of the ten categories making up the program, including History, Literature, Children, Nature, Natural History, Journeys, Adventure Guides, Nature Guides, Design, and Outdoor Classic. 

The program has very high standards of fairness and objectivity and has no connection whatsoever to any publisher or publishing business interest.  The winners are chosen by a panel of judges consisting of educators, academics, book reviewers, authors, editors, and outdoor columnists from throughout the country.  Once selected, the books are publicized through social media, wire service stories, press releases, and announcements at related websites.

On this website, you'll find lists of winning books, reviews of past and present winners, cover scans, and links to sources with additional information.  It's our hope that you'll find the website educational and useful.

For members of the media, we have included a press information page with the latest press releases and high resolution scans of winning books.  A special service is also available to website developers .  In return for a link to the NOBA site, web masters are welcome to use our reviews and cover scans on their own sites. 

Publishers and authors will also find application forms, entry rules, and information on how to nominate books for the annual awards.

Frequently Used NOBA Website Links

National Outdoor Book Award Links: Past Winners by Year | Master List | NOBA Medallion | Application Procedures   |  Media Information Page |  National Coverage of NOBA | Website Index

National Outdoor Book Award Sponsors: Association of Outdoor Recreation and Education &  Idaho State University

Mountain & River Photo: Alsek River and Mt. Blackadar, St. Elias Range. Photo by Jim Brock

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Poor Nations Are Writing a New Handbook for Getting Rich

Economies focused on exports have lifted millions out of poverty, but epochal changes in trade, supply chains and technology are making it a lot harder.

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By Patricia Cohen

Reporting from London

For more than half a century, the handbook for how developing countries can grow rich hasn’t changed much: Move subsistence farmers into manufacturing jobs, and then sell what they produce to the rest of the world.

The recipe — customized in varying ways by Hong Kong, Singapore, South Korea, Taiwan and China — has produced the most potent engine the world has ever known for generating economic growth. It has helped lift hundreds of millions of people out of poverty, create jobs and raise standards of living.

The Asian Tigers and China succeeded by combining vast pools of cheap labor with access to international know-how and financing, and buyers that reached from Kalamazoo to Kuala Lumpur. Governments provided the scaffolding: They built up roads and schools, offered business-friendly rules and incentives, developed capable administrative institutions and nurtured incipient industries.

But technology is advancing, supply chains are shifting, and political tensions are reshaping trade patterns. And with that, doubts are growing about whether industrialization can still deliver the miracle growth it once did. For developing countries, which contain 85 percent of the globe’s population — 6.8 billion people — the implications are profound.

Today, manufacturing accounts for a smaller share of the world’s output, and China already does more than a third of it . At the same time, more emerging countries are selling inexpensive goods abroad, increasing competition. There are not as many gains to be squeezed out: Not everyone can be a net exporter or offer the world’s lowest wages and overhead.

There are doubts that industrialization can create the game-changing benefits it did in the past. Factories today tend to rely more on automated technology and less on cheapworkers who have little training.

“You cannot generate enough jobs for the vast majority of workers who are not very educated,” said Dani Rodrik, a leading development economist at Harvard.

The process can be seen in Bangladesh, which the World Bank’s managing director called “one of the world’s greatest development stories” last year. The country built its success on turning farmers into textile workers.

Last year, though, Rubana Huq, chair of Mohammadi Group, a family-owned conglomerate, replaced 3,000 employees with automated jacquard machines to do complex weaving patterns.

The women found similar jobs elsewhere in the company. “But what follows when this happens on a large scale?” asked Ms. Huq, who is also president of the Bangladesh Garment Manufacturers and Exporters Association.

These workers don’t have training, she said. “They’re not going to turn into coders overnight.”

Recent global developments have accelerated the transition.

Supply chain meltdowns related to the Covid-19 pandemic and to sanctions prompted by Russia’s invasion of Ukraine drove up the price of essentials like food and fuel, biting into incomes. High interest rates, imposed by central banks to quell inflation, set off another series of crises: Developing nations’ debts ballooned , and investment capital dried up.

Last week, the International Monetary Fund warned of the noxious combination of lower growth and higher debt.

The supercharged globalization that had encouraged companies to buy and sell in every spot around the planet has also been shifting. Rising political tensions, especially between China and the United States, are affecting where businesses and governments invest and trade.

Companies want supply chains to be secure as well as cheap, and they are looking at neighbors or political allies to provide them.

In this new era, Mr. Rodrik said, “the industrialization model — which practically every country that has become rich has relied on — is no longer capable of generating rapid and sustained economic growth.”

Nor is it clear what might replace it.

There’s a future in service jobs.

One alternative might be found in Bengaluru, a high-tech center in the Indian state of Karnataka.

Multinationals like Goldman Sachs, Victoria’s Secret and the Economist magazine have flocked to the city and set up hundreds of operational hubs — known as global capability centers — to handle accounting, design products, develop cybersecurity systems and artificial intelligence, and more.

Such centers are expected to generate 500,000 jobs nationwide in the next two to three years, according to the consulting firm Deloitte .

They are joining hundreds of biotech, engineering and information technology companies including homegrown giants like Tata Consultancy Services, Wipro and Infosys Limited. Four months ago, the American chip company AMD unveiled its largest global design center there.

“We have to move away from the idea of classic development stages, that you go from the farm to the factory and then from the factory to offices,” said Richard Baldwin , an economist at the IMD in Lausanne. “That whole development model is wrong.”

Two-thirds of the world’s output now comes from the service sector — a mishmash that includes dog walkers, manicurists, food preparers, cleaners and drivers, as well as highly trained chip designers, graphic artists, nurses, engineers and accountants.

It is possible to leapfrog to the service sector and grow by selling to businesses around the world, Mr. Baldwin argued. That is what helped India become the world’s fifth-largest economy .

In Bengaluru, formerly known as Bangalore, a general rise in middle-class living attracted more people and more businesses that, in turn, attracted more people and businesses, continuing the cycle, Mr. Baldwin explained.

Covid sped this transition, by forcing people to work remotely — from a different part of town, a different city or a different country.

In the new model, countries can focus growth around cities rather than a particular industry. “That creates economic activities which are fairly diverse,” Mr. Baldwin said.

“Think Bangalore, not South China,” he said.

Free markets are not enough.

Many developing nations remain focused on building export-oriented industries as the path to prosperity. And that’s how it should be, said Justin Yifu Lin , dean of the Institute of New Structural Economics at Peking University.

Pessimism about the classic development formula, he said, has been fueled by a misguided belief that the growth process was automatic: Just clear the way for the free market and the rest will take care of itself.

Countries were often pressured by the United States and the international institutions to embrace open markets and hands-off governance.

Export-led growth in Africa and Latin America stumbled because governments failed to protect and subsidize infant industries, said Mr. Lin, a former chief economist at the World Bank.

“Industrial policy was taboo for a long time,” he said, and many of those who tried failed. But there were also success stories like China and South Korea.

“You need the state to help the private sector overcome market failures,” he said. “You cannot do it without industrial policy.”

It won’t work without education.

The overriding question is whether anything — services or manufacturing — can generate the type of growth that is desperately needed: broad based, large scale and sustainable.

Service jobs for businesses are multiplying, but many offering middle and high incomes are in areas like finance and tech, which tend to require advanced skills and education levels far above what most people in developing nations have.

In India, nearly half of college graduates don’t have the skills they need for these jobs, according to Wheebox , an educational testing service.

The mismatch is everywhere. The Future of Jobs report , published last year by the World Economic Forum, found that six in 10 workers will need retraining in the next three years, but the overwhelming majority won’t have access to it.

Other kinds of service jobs are proliferating, too, but many are neither well paid nor exportable. A barber in Bengaluru can’t cut your hair if you’re in Brooklyn.

That could mean smaller — and more uneven — growth.

Researchers at Yale University found that in India and several countries in sub-Saharan Africa, agricultural workers jumped into consumer service jobs and raised their productivity and incomes.

But there was a catch: The gains were “strikingly unequal” and disproportionately benefited the rich .

With a weakening global economy , developing countries will need to wring every bit of growth they can from every corner of their economies. Industrial policy is essential, Mr. Rodrik of Harvard said, but it should focus on smaller service firms and households because that is going to be the source of most future growth.

He and others caution that even so, gains are likely to be modest and hard won.

“The envelope has shrunk,” he said. “How much growth we can get is definitely less than in the past.”

An earlier version of this article misidentified the location of IMD. It is in Lausanne, not Geneva.

How we handle corrections

Patricia Cohen writes about global economics and is based in London. More about Patricia Cohen

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Missy elliott kicks off first headlining tour this summer, billie eilish announces release date for third album ‘hit me hard and soft’.

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Billie Eilish has announced her third full-length album, a record that’s already stirred up some controversy due to its title Hit Me Hard And Soft .

The multiple Grammy and Oscar winner wrote on Instagram today that the album comes out May 17.

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A press release following her post explained the title. “The album does exactly as the title suggests: hits you hard and soft both lyrically and sonically while bending genres and defying trends along the way.” The new music “journeys through a vast and expansive audio landscape, immersing listeners into a full spectrum of emotions. It’s what the multiple Grammy and Academy Award winner does best, continuing to affirm Billie Eilish as the most exciting songwriter of her time.” 

The album will be available on all digital platforms, and physical formats will utilize recyclable materials.

See Eilish’s Instagram post below.

View this post on Instagram A post shared by BILLIE EILISH (@billieeilish)

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• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

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

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

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• Chemical engineering
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• Taste receptors

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

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Introduction

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

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

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

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

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

Extensive analysis identifies relationships between chemical compounds in beer

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

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

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

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

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

Tasting panel assessments reveal sensorial relationships in beer

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

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

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

Tasting panel assessments of specific flavors correlate with chemical composition

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

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

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

Public consumer reviews complement expert panel data

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

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

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

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

Models can predict beer sensory profiles from chemical data

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Model validation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Beer selection

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

Chemical dataset

Sample preparation.

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

HS-GC-FID/FPD

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

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

HS-SPME-GC-MS

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

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

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

Discrete photometric and enzymatic analysis

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

NIR analyses

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

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

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

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

Online reviews from a public database

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

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

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

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

Beer price collection and processing

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

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

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

Model dissection

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

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

Validation of causal chemical properties

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

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

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

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

Reporting summary

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

Data availability

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

Code availability

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

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Acknowledgements

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

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

Authors and Affiliations

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

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

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

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

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

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

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

Łukasz Kreft & Alexander Botzki

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

Philippe Malcorps & Luk Daenen

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Contributions

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

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Correspondence to Kevin J. Verstrepen .

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

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

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

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