research report about overpopulation

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Epidemics and pandemics: Is human overpopulation the elephant in the room?

N. spernovasilis.

a School of Medicine, University of Crete, Voutes, P.C, 71003 Heraklion, Greece

b “Trifyllio” General Hospital of Kythira, Kythira, Greece

M. Papadakis

c Department of Surgery II, University Hospital Witten-Herdecke, Wuppertal, Germany

C. Tsioutis

d School of Medicine, European University Cyprus, Nicosia, Cyprus

e “Agia Sofia” Children's Hospital, Athens, Greece

Dear Editor,

On the 11th of March 2020 the World Health Organization (WHO) declared the Coronavirus disease 19 (COVID-19) outbreak a global pandemic. Since then, COVID-19 has highlighted the weaknesses of the healthcare systems worldwide as well as the lack of coordination between governments in the face of a global health crisis. It has also emphasised how reluctant citizens can be to follow guidelines and make personal sacrifices for the common good. Now, more than ever, it is evident that action is required to prevent future pandemics by targeting the roots of the problem rather than the problem itself. By analysing this and several other past health crises, it appears that human overpopulation and everything it entails may be the core issue.

Epidemics have ravaged humanity throughout its existence. However, infectious disease outbreaks have been increasing over the last decades [1] . The exponential growth of human population has led to increased urbanization which acts as an accelerant of epidemics, as was the case with COVID-19 in Wuhan. In addition, the continuously increasing pursuit for natural resources has led to the expansion of humans in wild habitats where they are more likely to come into contact with animals that act as reservoirs or vectors of previously unknown infectious agents. Approximately 1.67 million yet-to-be-discovered viral species exist in mammalian and bird hosts and many of these unknown viruses may have zoonotic potential [2] . It is not a coincidence that recent outbreaks such as Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS) and Ebola virus disease, were all zoonotic diseases. According to a report by the WHO, the Ebola outbreak was probably the result of exploitation of a densely forested area in Guinea by timber and mining companies [3] . This allowed fruit bats, which are considered the natural reservoir of the virus, to live in close proximity to humans and thus facilitated the transmission of the virus between the two different species. The loss of biodiversity owing to increased human activity is another factor that seems to contribute to the accelerated spread of infectious diseases. Studies have shown that loss of avian biodiversity in certain regions correlates with an increased incidence of West Nile encephalitis [4] . More specifically, areas with low avian diversity appear to be dominated by species that amplify the virus thus aiding human infection, as opposed to areas with a higher avian diversity that also contain species that are less competent hosts [4] . These examples clearly indicate that the degradation of natural habitats and the destabilization of ecosystems may play a determining role in the development of epidemics and pandemics.

Overpopulation has also generated a rise in food demands. The upsurge of malaria in Borneo eventuated due to the excessive deforestation in order to support the steadily increasing demands for palm oil production through the creation of palm oil plantations [1] . In another example, most of the constantly growing global meat production comes from structures that are called Concentrated Animal Feeding Operations (CAFOs) [5] . In these structures, specific species of animals are tightly packed together and kept in suboptimal conditions. Setting aside issues like animal welfare, CAFOs also constitute the perfect environment for the emergence and spread of infections, including zoonoses [5] . Moreover, the existence of overcrowded live animal markets in many parts of the developing world, where wild and domestic animals are caged in close proximity and are sold for human consumption, poses a significant risk for emerging infectious diseases. Some of these markets in Wuhan have been connected with early cases of COVID-19, although a firm conclusion cannot currently be drawn [6] . In addition, livestock trading across international borders further promotes the spread of infections such as the H5N1 influenza [7] . Finally, the use of antibiotics to compensate for unsanitary conditions and to augment livestock production is a major driving force for the development of drug-resistant bacterial zoonoses [1] .

Cross-border migration which may aid in the spread of infectious diseases is also driven by overpopulation. Most people will migrate in search for labour or better economic prospects due to the increasing stress that population growth exerts on the financial system of their home countries [8] . Others will migrate in order to evade climate change and natural disasters. Finally, disruptions and conflicts that will arise over the exploitation of the earth's finite natural resources will also result in people seeking refuge in other countries [8] .

The human population now is 7.7 billion people and the United Nations estimate that it will rise to 9.7 billion by 2050 [9] . The aforementioned issues are only expected to be exacerbated by this estimation. Perhaps it is time to start addressing the problem of human overpopulation using modest solutions. Taking into account that every year a significant proportion of pregnancies worldwide are unintended [10] , simple actions such as comprehensive sexuality education, improving the status of women in terms of human rights and education and employment opportunities in certain countries, and contraception availability, may all be useful. Also, new methods of contraception that are more easily accessible and effective ought to be sought out. In addition to the proposed measures above, the scale and the nature of the consumption in the developed countries should be modified. The sooner we address these problems and advocate for solutions, the more likely we are to avoid extreme measures like one-child policy, which has been enforced in China in the past, or worse, a catastrophic pandemic that will act as human population's equilibrator.

In conclusion, epidemics and pandemics seem to be fuelled by human overpopulation and the elicited disruption of the balance between humankind and nature. If action is not taken to address the various factors that affect this balance, the COVID-19 pandemic will probably be just the beginning of many more to come.

Human and animal rights

The authors declare that the work described has not involved experimentation on humans or animals.

Informed consent and patient details

The authors declare that the work described does not involve patients or volunteers.

Disclosure of interest

The authors declare that they have no competing interest.

This work did not receive any grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author contributions

All authors attest that they meet the current International Committee of Medical Journal Editors (ICMJE) criteria for Authorship.

Public Health and Overpopulation: The United Nations Takes Action

With the world’s population rising faster than ever before, will our population growth outpace our resource reserves? How can the dangerous effects of overpopulation be managed without diminishing the major improvements in our quality of life that come about thanks to population growth?

The UN projects that over half of the Earth’s population growth in the next three decades will occur in the continent of Africa. This is due to the fact that, from 2010 to 2015, Africa’s population grew at a rate of 2.55 percent annually, with the continent still maintaining the highest pace of population growth among other continents. The UN predicts that, behind Africa, Asia will be the second greatest donor to future international population growth, with an expected addition of approximately one billion people by 2050. In contrast, within every European nation, fertility rates are currently below the population replacement level, which is approximately two children per woman. In most of Europe, fertility rates have remained beneath replacement level for decades. The global population grew fourfold in the past 100 years, so what impact could increased population growth have in the future? Will there be mass-migration? Overcrowding in already densely populated or resource-rich areas? Poor living conditions and sanitation similar to Industrial Revolution era slums?

The global population is currently rising at a steady rate. The number of humans existing on Earth has never been as high as it is now. In 1800, Earth had approximately 1 billion inhabitants, which rose to 2.3 billion in 1940, then 3.7 billion in 1970, and approximately 7.5 billion today. In the last five decades, Earth has experienced an extreme population boom. This phenomenon is known as overpopulation, where the condition in which the amount of humans currently existing on Earth outstrips future resource availability and earth’s carrying capacity. Throughout human history, birth and death rates have always counterbalanced each other, which ensured that Earth had a maintainable population growth level. However, in the 1960s, the global population increased at an unparalleled rate. This brought about a variety of apocalyptic predictions, most prominently, a revival of the Malthusian trap panic.

Paul R. Ehrlich’s 1968 novel, The Population Bomb , eerily echoes Thomas R. Malthus’s landmark 1798 Essay on the Principle of Population . Ehrlich’s novel proposes theories regarding potential outcomes for when agricultural growth does not keep pace with population growth. Ultimately his theories say that the world’s food supply will inevitably become inadequate for feeding the general population, whose numbers would continue to swell until famine, disease epidemics, war, or other calamities took root. These Malthusian predictions about out-of-control population growth have resulted in a variety of detrimental global impacts, particularly the emergence of extreme reproductive control measures, which have taken center stage on an international scale. Today, despite the fact that population scientists mostly agree that Malthus’s forecasts were overblown, the lingering prevalence of these fears have contributed to millions of forced sterilizations in Mexico, Bolivia, Peru, Indonesia, Bangladesh and India, as well as China’s two-child policy . Overall, this has left many wondering whether extreme population growth projections are legitimate or merely groundless panic perpetuated by alarmists.

The Demographic Transition

In reality, rising birth rates and population booms are components of a four-step process called the demographic transition, which the Earth is currently undergoing. Most developed nations have already made this transition, but other countries are currently experiencing this change. In the 1700s, the entire world was undergoing the first stage of the demographic transition. During this time, the continent of Europe was in even poorer condition than the modern-day definition of a developing region, and was afflicted with inferior public health, sustenance, and medical facilities. Birth rates were higher; however, death rates were also higher. For this reason, population growth remained largely stagnant.

Statistically, in the 1700s, women birthed four to six children. However, on average, only two survived to adulthood. When the Industrial Revolution began in Great Britain in the mid-18th century, the Earth experienced the most significant shift in human lifestyles since the Agricultural Revolution. The Industrial Revolution altered every aspect of society, and fostered a greater sense of global interconnectedness. For example, many peasants became factory workers, manufactured products became widely available due to mass production, and countless scientific advancements improved existing methods of transportation, communication, and medicine.

Gradually, this economic development created a middle class and, after the work of union activists, ultimately raised the standard of living and health care for the impoverished labor demographic. Thus began the second transition stage. The increased availability of better foodstuffs, sanitation, and medicine directly contributed to lower death rates, causing a population explosion that doubled Great Britain’s population from 1750 to 1850. In the past, families tended to have more children because not all were expected to survive, but when child mortality rates decreased, the third transition stage was launched. This stage involves reduced conception rates and slowing population growth. Ultimately, a balance was established, with fewer deaths and births, creating a stable population growth rate and signifying the attainment of the fourth and final stage of the demographic transition.

Even as birth rates have decreased dramatically, Earth’s population is still rising at an alarming rate because the humans conceived during the population boom of the 1970s and 1980s are currently having more children; however, the current average number of children per family remains two and a half, while it was five during the late 1970s. As this generation ages and its fertility diminishes, the rate of population growth will likely continue to decrease in every nation. Most of the world’s countries have reached the fourth stage of the demographic transition. In approximately 80 years, developed countries will experience a reduction in fertility from over six children to fewer than three children. Malaysia and South Africa reached this point in 34 years, Bangladesh in 20 years,  and Iran in 10 years. If developing countries are afforded more support, they will reach this point much faster.

Overall, most scientists postulate that human population growth will eventually come to an end, and the UN predicts that Earth’s population will not exceed twelve billion. Some of the major causes of population growth are reduced infant mortality rates, increased lifespans, higher fertility rates, advances in science and technology, and improved access to proper medical care. With the UN’s continued assistance, concurrent with overpopulation, the development level of the global community will increase, and the number of people living in poverty will decrease. Nonetheless, an ever-expanding human population is an immense social and economic challenge that necessitates the alignment of different national interests, especially with regards to reproductive rights, resource availability, and environmental concerns.

The United Nations Takes Action

In 1969, the United Nations Population Fund (UNFPA) was established in order to lead the UN in implementing population programs fundamentally based on the notion of family planning, or the “human right of individuals and couples to freely determine the size of their families” without governmental interference or legislation. In 1994, at the International Conference on Population and Development in Cairo, Egypt, the designated objectives of the UNFPA were determined in greater depth. It was decided that the UNFPA would specifically focus on the gender and human rights elements of population issues; consequently, the UN Population Fund was granted the lead role in aiding nations in fulfilling the Conference’s Programme of Action.

The three most significant sections of the UN Population Fund mandate are “Reproductive Health,” “Gender Equality,” and “Population and Development.” The United Nations Population Division (UNPD) works to confront the interconnected global issues posed by population growth, which is primarily fueled by rising fertility rates, increased longevity, and greater international migration. The UN produces the official demographic approximations and predictions for every country and all regions of the world. The UNFPA specifically addresses global population by compiling data and statistics regarding migration, fertility, marriage, regional development, urbanization, world population projections, and national population policies.

In November 2012, the UNFPA declared family planning a global human right; however, approximately 12 percent of 15 to 49-year-old women internationally are not afforded access to family planning. This is considered an egregious modern-day human rights infringement. The UNFPA aids various UN bodies like the Commission on Population and Development, and endorses the implementation of the Programme of Action undertaken by the International Conference on Population and Development (IPCD) in 1994. The UNFPA has been successful in urging international cooperation on the issue of securing family planning as a human right, pushing the UN to hold three conferences concerning the issue of population, along with two special sessions of the General Assembly and a summit in 2019 .

The Way Forward

Ultimately, apocalyptic population growth fears are overblown, and as such, draconian population control regulations are unnecessary. We have witnessed progress on an international scale in this area, perhaps most notably with China revoking its infamous, longstanding one-child policy just seven years ago. However, a broader global focus on guaranteeing family planning as a human right remains essential. In the words of economist Julian Simon, “Whatever the rate of population growth is, historically it has been that the food supply increases at least as fast, if not faster.” Since Ehrlich’s initial fear-mongering regarding an overpopulation-​induced Armageddon, the planet’s population has more than doubled . However, annually, famine deaths have dropped by millions. Today’s famines are war-induced, not caused by natural resource consumption. As production rose, prices fell and calorie consumption increased, which decreased malnutrition worldwide. In Simon’s words, human ingenuity is the “ ultimate resource .” Therefore, the enactment of heavy-handed population-​control regulations is not only abhorrent, but is also irrational and unsupported by scientific evidence.

Sophia Scott

Sophia Scott is a staff writer for the Harvard International Review. She is interested in global health & health equity, along with the intersections between science and policy.

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Population Growth

Population growth is one of the most important topics we cover at Our World in Data .

For most of human history, the global population was a tiny fraction of what it is today. Over the last few centuries, the human population has gone through an extraordinary change. In 1800, there were one billion people. Today there are more than 8 billion of us.

But after a period of very fast population growth, demographers expect the world population to peak by the end of this century.

On this page, you will find all of our data, charts, and writing on changes in population growth. This includes how populations are distributed worldwide, how this has changed, and what demographers expect for the future.

Related topics

• Child Mortality
• Fertility Rate
• Life Expectancy
• Age Structure

Key insights on Population Growth

Population cartograms show us where the world’s people are.

Geographical maps show us where the world’s landmasses are; not where people are. That means they don’t always give us an accurate picture of how global living standards are changing.

One way to understand the distribution of people worldwide is to redraw the world map – not based on the area but according to population.

This is shown here as a population cartogram : a geographical presentation of the world where the size of countries is not drawn according to the distribution of land but by the distribution of people. It’s shown for the year 2018.

As the population size rather than the territory is shown in this map, you can see some significant differences when you compare it to the standard geographical map we’re most familiar with. 

Small countries with a high population density increase in size in this cartogram relative to the world maps we are used to – look at Bangladesh, Taiwan, or the Netherlands. Large countries with a small population shrink in size – look for Canada, Mongolia, Australia, or Russia.

You can find more details on this cartogram in our article about it:

What you should know about this data

• This map is based on the United Nation’s 2017 World Population Prospects report. Our interactive charts show population data from the most recent UN revision. This means there may be minor differences between the figures shown on the map and the latest estimates in our other charts.

The world population has increased rapidly over the last few centuries

The speed of global population growth over the last few centuries has been staggering. For most of human history, the world population was well under one million. 1

As recently as 12,000 years ago, there were only 4 million people worldwide.

The chart shows the rapid increase in the global population since 1700. 

The one-billion mark wasn’t broken until the early 1800s. It was only a century ago that there were 2 billion people.

Since then, the global population has quadrupled to eight billion.

Around 108 billion people have ever lived on our planet. This means that today’s population size makes up 6.5% of the total number of people ever born. 2

This increase has been the result of advances in living conditions and health that reduced death rates – especially in children – and increases in life expectancy.

• This data comes from a combination of sources, all detailed in our sources article for our long-term population dataset.

Population growth is no longer exponential – it peaked decades ago

There’s a popular misconception that the global population is growing exponentially. But it’s not.

While the global population is still increasing in absolute numbers, population growth peaked decades ago.

In the chart, we see the global population growth rate per year. This is based on historical UN estimates and its medium projection to 2100.

Global population growth peaked in the 1960s at over 2% per year. Since then, rates have more than halved, falling to less than 1%. 

The UN expects rates to continue to fall until the end of the century. In fact, towards the end of the century, it projects negative growth, meaning the global population will shrink instead of grow.

Global population growth, in absolute terms – which is the number of births minus the number of deaths – has also peaked. You can see this in our interactive chart:

The world has passed “peak child”

Hans Rosling famously coined the term “ peak child ” for the moment in global demographic history when the number of children stopped increasing.

According to the UN data, the world has passed “peak child”, which is defined as the number of children under the age of five.

The chart shows the UN’s historical estimates and projections of the number of children under five.

It estimates that the number of children in the world peaked in 2017. For the coming decades, demographers expect a decades-long plateau before the number will decline more rapidly in the second half of the century.

• These projections are sensitive to the assumptions made about future fertility rates worldwide. Find out more from the UN World Population Division .
• Other sources and scenarios in the UN’s projections suggest that the peak was reached slightly earlier or later. However, most indicate that the world is close to “peak child” and the number of children will not increase in the coming decades.
• The ‘ups and downs’ in this chart reflect generational effects and ‘baby booms’ when there are large cohorts of women of reproductive age, and high fertility rates. The timing of these transitions varies across the world.

The UN expects the global population to peak by the end of the century

When will population growth come to an end?

The UN’s historical estimates and latest projections for the global population are shown in the chart.

The UN projects that the global population will peak before the end of the century – in 2086, at just over 10.4 billion people.

• These projections are sensitive to the assumptions made about future fertility and mortality rates worldwide. Find out more from the UN World Population Division .
• Other sources and scenarios in the UN’s projections can produce a slightly earlier or later peak. Most demographers, however, expect that by the end of the century, the global population will have peaked or slowed so much that population growth will be small.

Explore data on Population Growth

Research & writing.

What would the work look like if each country’s area was in proportion to its population?

The world population has increased rapidly in recent centuries. But this is slowing.

Max Roser and Hannah Ritchie

More Key articles on Population Growth

How many people die and how many are born each year.

Hannah Ritchie and Edouard Mathieu

Five key findings from the 2022 UN Population Prospects

Hannah Ritchie, Edouard Mathieu and Lucas Rodés-Guirao

Which countries are most densely populated?

Demographic change.

Hannah Ritchie

Definitions and sources

Edouard Mathieu and Lucas Rodés-Guirao

Other articles related to population growth

Interactive charts on Population Growth

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World population to reach 8 billion this year, as growth rate slows

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15 November 2022 is predicted to be the day that the global population reaches eight billion. The projection is revealed in the UN’s World Population Prospects 2022 report, which also shows that India is on course to surpass China as the world’s most populous country in 2023.

The latest UN projections suggest that the world’s population could grow to around 8.5 billion in 2030 and 9.7 billion in 2050, before reaching a peak of around 10.4 billion people during the 2080s . The population is expected to remain at that level until 2100. 

Slowest growth rate since 1950s

However, the annual World Population Prospect report, released on Monday to coincide with World Population Day , also notes that the global population is growing at its slowest rate since 1950 , having fallen to less that one per cent in 2020.

Fertility, the report declares, has fallen markedly in recent decades for many countries: today, two-thirds of the global population lives in a country or area where lifetime fertility is below 2.1 births per woman, roughly the level required for zero growth in the long run, for a population with low mortality.

In 61 countries or areas, the population is expected to decrease by at least one per cent over the next three decades, as a result of sustained low levels of fertility and, in some cases, elevated rates of emigration.

The COVID-19 pandemic has had an effect on population change: global life expectancy at birth fell to 71 years in 2021 (down from 72.9 in 2019) and, in some countries, successive waves of the pandemic may have produced short-term reductions in numbers of pregnancies and births.

“Further actions by Governments aimed at reducing fertility would have little impact on the pace of population growth between now and mid-century, because of the youthful age structure of today’s global population,” said John Wilmoth, Director of the Population Division of the UN Department of Economic and Social Affairs (DESA).

“Nevertheless, the cumulative effect of lower fertility, if maintained over several decades, could be a more substantial deceleration of global population growth in the second half of the century ”. 

Growth concentrated in eight countries

More than half of the projected increase in the global population up to 2050 will be concentrated in eight countries: the Democratic Republic of the Congo, Egypt, Ethiopia, India, Nigeria, Pakistan, the Philippines and the United Republic of Tanzania.

Countries of sub-Saharan Africa are expected to contribute more than half of the increase anticipated through 2050. Liu Zhenmin, UN Under-Secretary-General for Economic and Social Affairs, warned that rapid population growth makes eradicating poverty, combatting hunger and malnutrition, and increasing the coverage of health and education systems more difficult. 

The ‘demographic dividend’

In most countries of sub-Saharan Africa, as well as in parts of Asia and Latin America and the Caribbean, recent reductions in fertility have led to a “demographic dividend”, with a rise in the share of the working age population (25 to 64 years), providing an opportunity for accelerated economic growth per capita.

The report argues that, to make the most of this opportunity, countries should invest in the further development of their human capital, by ensuring access to health care and quality education at all ages, and by promoting opportunities for productive employment and decent work.

Achieving the UN Sustainable Development Goals , especially those related to health, education and gender equality, will contribute to reducing fertility levels and slowing global population growth.

More older people, living longer

The world should expect to see far more grey hairs by 2050: by then, it is expected that the number of persons aged 65 years or over worldwide will be more than twice the number of children under the age of five,  and about the same as the number under age 12.

Further reductions in mortality are projected to result in an average global longevity of around 77.2 years in 2050. Yet in 2021, life expectancy for the least developed countries lagged seven years behind the global average.

The report recommends that countries with ageing populations should take steps to adapt public programmes to the growing numbers of older persons, establishing universal health care and long-term care systems, and by improving the sustainability of social security and pension systems.

“This year’s World Population Day falls during a milestone year, when we anticipate the birth of the Earth’s eight billionth inhabitant”, said UN Secretary-General António Guterres , reacting to the report’s findings.

🌟NEW! Don’t miss the latest on the #PeopleOfTomorrow!#UNPopulation will release the latest on the world’s population trends.In what parts of the world will we see more people being born? Where can we see a decline?Find out by tuning into https://t.co/qDYNaEqjH5 at 12 noon ET! pic.twitter.com/Pshf66RBjm UN DESA UNDESA

This is an occasion to celebrate our diversity, recognize our common humanity, and marvel at advancements in health that have extended lifespans and dramatically reduced maternal and child mortality rates,” he added. “At the same time, it is a reminder of our shared responsibility to care for our planet and a moment to reflect on where we still fall short of our commitments to one another ”.

World Population Day  

“Let us protect human rights and the ability of all individuals to make informed choices about whether and when to have children,” the UN chief said in his message marking World Population Day , coinciding with the report. 

“ We still live in a world of vast gender inequality – and we are witnessing renewed assaults on women’s rights , including on essential health services,” said the Secretary-General. 

He called the day “an occasion to celebrate our diversity, recognize our common humanity, and marvel at advancements in health that have extended lifespans and dramatically reduced maternal and child mortality rates”. 

At the same time, Mr. Guterres described it as a reminder of “our shared responsibility to care for our planet and a moment to reflect on where we still fall short of our commitments to one another”. 

Amidst COVID-19, the climate crisis, wars and conflicts, humanitarian emergencies, hunger and poverty, he attested that “ our world is in peril ”. 

“Complications related to pregnancy and childbirth are still the leading cause of death among girls aged 15 to 19”. 

Mr. Guterres underscored that “ eight billion people means eight billion opportunities to live dignified and fulfilled lives ”. 

He urged everyone to contribute to a common future with greater equality and solidarity for the planet and future generations. 

Road to prosperity

World Population Day offers a moment to celebrate human progress, the World Population Fund ( UNFPA ) said in its  message  for the day.

Despite global challenges, UNFPA upheld that we live in a world in which "higher shares of people are educated and live healthier lives than at any previous point in history".

"Societies that invest in their people, in their rights and choices, have proven time and again that this is the road to the prosperity and peace that everyone wants—and deserves".

• World Population Report

Articles on Overpopulation

Displaying 1 - 20 of 45 articles.

Population can’t be ignored. It has to be part of the policy solution to our world’s problems

Jenny Stewart , UNSW Sydney

Thinking of having a baby as the planet collapses? First, ask yourself 5 big ethical questions

Craig Stanbury , Monash University

Global population hits 8 billion, but per-capita consumption is still the main problem

Lorenzo Fioramonti , University of Surrey ; Ida Kubiszewski , UCL ; Paul Sutton , University of Denver , and Robert Costanza , UCL

You are now one of 8 billion humans alive today. Let’s talk overpopulation – and why low income countries aren’t the issue

Matthew Selinske , RMIT University ; Leejiah Dorward , Bangor University ; Paul Barnes , UCL , and Stephanie Brittain , University of Oxford

8 billion people: why trying to control the population is often futile – and harmful

Melanie Channon , University of Bath and Jasmine Fledderjohann , Lancaster University

More than 1 in 5 US adults don’t want children

Zachary P. Neal , Michigan State University and Jennifer Watling Neal , Michigan State University

What the controversial 1972 ‘Limits to Growth’ report got right: Our choices today shape future conditions for life on Earth

Matthew E. Kahn , USC Dornsife College of Letters, Arts and Sciences

Curb population growth to tackle climate change: now that’s a tough ask

Michael P. Cameron , University of Waikato

Worried about Earth’s future? Well, the outlook is worse than even scientists can grasp

Corey J. A. Bradshaw , Flinders University ; Daniel T. Blumstein , University of California, Los Angeles , and Paul Ehrlich , Stanford University

Bob Brown is right – it’s time environmentalists talked about the population problem

Colin D. Butler , Australian National University

Beware far-right arguments disguised as environmentalism

Marc Hudson , Keele University

Why we should be wary of blaming ‘overpopulation’ for the climate crisis

Heather Alberro , Nottingham Trent University

Pasha 45: Spotlight on population growth in Africa

Ozayr Patel, The Conversation

Stabilising the global population is not a solution to the climate emergency – but we should do it anyway

Mark Maslin , UCL

Want to live longer? Consider the ethics

John K. Davis , California State University, Fullerton

Here’s what a population policy for Australia could look like

Liz Allen , Australian National University

‘Overpopulation’ and the environment: three ideas on how to discuss it in a sensitive way

Rebecca Laycock Pedersen , Keele University and David P. M. Lam , Leuphana University

Australia could house around 900,000 more migrants if we no longer let in tourists

Raja Junankar , UNSW Sydney

Making small cities bigger will help better distribute Australia’s 25 million people

Glen Searle , University of Sydney

A long fuse: ‘The Population Bomb’ is still ticking 50 years after its publication

Derek Hoff , University of Utah

Related Topics

• Biodiversity
• Climate change
• Consumption
• Family planning
• Global population
• Population control
• Population growth
• Sustainability

Top contributors

Director of the Complex Adaptive Systems Research Group, University of Newcastle

Associate Professor, The University of Western Australia

Honorary Professor, Australian National University

President, Center for Conservation Biology, Bing Professor of Population Studies, Stanford University

Chief Research Scientist, CSIRO

Honorary Professor of Demography, Macquarie University

Honorary Professor, Industrial Relations Research Centre, UNSW Sydney

ARC Australian Professorial Fellow, University of Adelaide

Distinguished Research Professor and Australian Laureate, James Cook University

Professor, University of Sydney

Matthew Flinders Professor of Global Ecology and Models Theme Leader for the ARC Centre of Excellence for Australian Biodiversity and Heritage, Flinders University

Demographer, ANU Centre for Social Policy Research, Australian National University

Associate Professor, Institute for Culture and Society & School of Humanities and Communication Arts, Western Sydney University

Adjunct professor, University of Technology Sydney

Chair professor, The University of Queensland

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Human Overpopulation: Causes and Effects in Developing Countries

This research paper outlines the causes and effects of human overpopulation, focusing in developing countries. The primary cause of this problems includes low mortality rates coupled with high birth rates. The exponential influx in human overpopulation has had negative effects on both the economic stability and environment of the affected countries. In addition to the causes and effects discussed, potential solutions are proposed to assist in the mitigation of the problem.

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Over-population is a great Problem for many countries. This research looks into all possible solutions for this problem. World population has jumped from about 2.5 billion in 1951 to 7.8 billion in April 2020 , 212% increase. Over-population has been an issue for many countries especially in Africa, as Africa has the highest fertility rate ,children per woman, and Africans are low on resources especially for those who live in the middle of Africa ,in other words, near the equator, Because of its hot weather and being low on water, it is optimum environment for spreading diseases and droughts which its direct relation to overpopulation was later discovered resulting in lack of resources and slowness of economy development. Human Resources management has important terminology which need to be understood before getting into our main topic: fertility rate is the average children per woman, birth rate is the average number of children born per year and death rate is the number of deaths per year, natural change is the birth rate minus the death rate . Social Scientists have many theories on what causes over-population, Some suggest that poverty is the main cause of over-population as it is seen in most poor countries like in Africa unlike rich countries in Europe. They suggest that families try to overcome their poor condition by having more children. But other scientists argue that it can be correlation and something else is causing both of them. They suggest that it is a high death rates. Also, through comparing between poor Countries and rich Countries, it can be notice that in poor countries, death rates are high as of that most families give birth to many children. There are other suggested causes like lack of education and child labor. Over-population can cause many serious problems especially for poor countries. For example, It can cause lack of water in developing countries because as the population grow, water consumption increases. For countries that do not have a fresh stable water source, This can lead to droughts and lower life expectancy. Also, population growth could cause Extinction of wild life and pollution because forests and natural environments for various animals are cut down to free more space for buildings and cities. green house effect was learned in (CH.2.11) about and learned In (ES.2.10) about the role of plants in stabilizing the carbon dioxide percentage and the role of forest in keeping biodiversity in (BI.2.12). For These reasons, Countries tried to solve this issue, because of its significant impact on the economy and the productivity. Some of these solutions were the “one-child-policy” and “two-child-policy” tried by China to control population growth using the law. The Results of them were very fast. Other places tried to control over-population by focusing on education especially for girls like in Europe in the 19 th century. To conclude, This research will focus on these prior solutions and others and discuss why they work.

International Journal of Science, Technology and Society

Casmir Onyeneke

Overpopulation is an immensely dangerous problem that does not affect only individual countries but worldwide. It has many causes such as immigration; early marriages, poor medical access, and education are the main reasons to be indicated. While it has many reasons why the earth faces overcrowded, overpopulation's effects are considered a perilous crisis more than itself. In the past, researchers have explored how to stop this rising problem even though; they have tried several ways from mass sterilization to awarding families with no child. None of these approaches worked instead, people tend to have more children and take this dangerous risk. Therefore, depletion of natural resources, rise in pollution, epidemics, and other diseases are the main consequences of overpopulation which this literature review has discussed. Even though scientists and nature activists have argued about the effects of overcrowding on the earth, humankind, and health, there is no right solution taken by us: humans as a creator of this crisis. Furthermore, this research will concentrate on the possible effects of overpopulation on the earth, health, and human kind. Plus, it suggests some possible solutions. Significantly, it illustrates how overpopulation will impact natural resources and human health.

Sandu Szilveszter

The purpose of this paper is to shed light on the overpopulation issue of India and propose recommendations on how to overcome it. First, the paper summarizes the crucial demographic conditions and where India stands by numbers. Then the article presents the development of the state approach to the issue starting from the early years of independence up to the current policies. It does so by scrutinizing its cultural, economic and social factors and implications of overpopulation and identifies socioeconomic backwardness, early marriages and family norms, lack of adequate health care infrastructure and education as the correlated and interdependent features supporting the trend of overpopulation. The authors come up with three recommendations to tackle the issue – women empowerment, education and industrialization.

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Analysis of how to tackle this issue. Written in spring 2016.

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Rapid population growth has been a topic of contestation on a socio-economic, statistical and environmental stance. It has proven to be a great cause for concern in much literature with regards to resource scarcity and the earth’s capacity to withstand the impact thereof, it’s hindrance to economic and social development and it’s threat to global political stability. It is important to note that 80 percent of the world’s population is found in low income countries (Hewitt) and in as much as it has been proven that population growth is exponential in these areas, it has not been resolved that one is poor because they have many children. Quite the opposite has been argued of which recent literature on the relationship between population and development has illustrated. When looking specifically at overpopulation in relation to development, there are two standpoints. The first one sees population growth being the barrier to sustainable development efforts as a whole, and much like the Malthusian Theory it urges control over population growth. The other standpoint denounces claims that overpopulation is the cause of social and economic development problems, but says it is a symptom of it. This essay will be looking at both standpoints so as to critically evaluate whether overpopulation really is the principle cause of development problems.

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Home > CUNY Graduate Center > Dissertations, Theses, and Capstone Projects > 1906

Dissertations, Theses, and Capstone Projects

Overpopulation and the impact on the environment.

Doris Baus , The Graduate Center, City University of New York Follow

Date of Degree

Document type, degree name.

Liberal Studies

Sophia Perdikaris

Subject Categories

Agricultural and Resource Economics | Demography, Population, and Ecology | Economics | Education Policy | Environmental Policy | Environmental Studies | Family, Life Course, and Society | Growth and Development | Health Economics | Health Policy | International and Area Studies | International Relations | Place and Environment | Politics and Social Change | Social and Behavioral Sciences | Urban Studies | Urban Studies and Planning

overpopulation, environmental impact, malthus, population growth, environmental issues, causes of overpopulation

In this research paper, the main focus is on the issue of overpopulation and its impact on the environment. The growing size of the global population is not an issue that appeared within the past couple of decades, but its origins come from the prehistoric time and extend to the very present day. Throughout the history, acknowledged scientists introduced the concept of “overpopulation” and predicted the future consequences if the world follows the same behavioral pattern. According to predictions, scientists invented the birth control pill and set population control through eugenics. Despite that, population continued to increase and fight with constant diseases. Migration was another component that encouraged population rise, which imposes severe threats to the environment. Urbanization destroys natural habitats and reinforces carbon dioxide emissions, which cause climate change and global warming. Species are becoming extinct and humanity is at threat that it set up for itself. Food scarcity and shortage of water as well as lack of job opportunities and inadequate education are the results of global inequality. Uneven distribution of natural resources, financial means, and individual rights give rise to poverty and define the global culture as greedy, despite the aid of international organizations and agencies. Solutions to overpopulation lie in the efforts of national institutions to implement policies that will correspond to the guidelines given by international institutions that work for the best of the global community. Within this global network, individuals act in their best interest, leaving the rest in extreme poverty and shortage. The inequality supports issues that contribute to overpopulation and leads to a humanity’s extinction.

Recommended Citation

Baus, Doris, "Overpopulation and the Impact on the Environment" (2017). CUNY Academic Works. https://academicworks.cuny.edu/gc_etds/1906

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The Overpopulation Project

A feed from The Overpopulation Project , an organization that studies the environmental impacts of overpopulation and explores humane policies to end population growth around the world. Learn more about The Overpopulation Project’s research initiatives here .

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Humanity's rapid expansion leads to environmental destruction, starvation and ravaged biodiversity. Population growth needs to be ended as soon as possible for sustainability. By Malte Andersson and Frank Götmark Published 2024-01-02 in Svenska Dagbladet, morning newspaper in Sweden (translated by TOP) In 1800, the world's population was 1 billion, but in 2022 we exceeded 8 … Continue reading Earth’s nature is being ravaged by population growth →

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The post No need to hold COP 29: Just follow Japan’s lead! appeared first on The Overpopulation Project .

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The post The Catalyst of Overpopulation in the Gaza Conflict appeared first on The Overpopulation Project .

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The post How to Fix the Planet, the Easy Way appeared first on The Overpopulation Project .

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The post How environmental professionals acknowledge overpopulation – and then ignore it appeared first on The Overpopulation Project .

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The post Delusional population projections lead us sleepwalking into catastrophe appeared first on The Overpopulation Project .

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The post Human population reduction is not a quick fix for environmental problems appeared first on The Overpopulation Project .

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Use of Abortion Pills Has Risen Significantly Post Roe, Research Shows

By Pam Belluck

Pam Belluck has been reporting about reproductive health for over a decade.

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On the eve of oral arguments in a Supreme Court case that could affect future access to abortion pills, new research shows the fast-growing use of medication abortion nationally and the many ways women have obtained access to the method since Roe v. Wade was overturned in June 2022.

The Details

A study, published on Monday in the medical journal JAMA , found that the number of abortions using pills obtained outside the formal health system soared in the six months after the national right to abortion was overturned. Another report, published last week by the Guttmacher Institute , a research organization that supports abortion rights, found that medication abortions now account for nearly two-thirds of all abortions provided by the country’s formal health system, which includes clinics and telemedicine abortion services.

The JAMA study evaluated data from overseas telemedicine organizations, online vendors and networks of community volunteers that generally obtain pills from outside the United States. Before Roe was overturned, these avenues provided abortion pills to about 1,400 women per month, but in the six months afterward, the average jumped to 5,900 per month, the study reported.

Overall, the study found that while abortions in the formal health care system declined by about 32,000 from July through December 2022, much of that decline was offset by about 26,000 medication abortions from pills provided by sources outside the formal health system.

“We see what we see elsewhere in the world in the U.S. — that when anti-abortion laws go into effect, oftentimes outside of the formal health care setting is where people look, and the locus of care gets shifted,” said Dr. Abigail Aiken, who is an associate professor at the University of Texas at Austin and the lead author of the JAMA study.

The co-authors were a statistics professor at the university; the founder of Aid Access, a Europe-based organization that helped pioneer telemedicine abortion in the United States; and a leader of Plan C, an organization that provides consumers with information about medication abortion. Before publication, the study went through the rigorous peer review process required by a major medical journal.

The telemedicine organizations in the study evaluated prospective patients using written medical questionnaires, issued prescriptions from doctors who were typically in Europe and had pills shipped from pharmacies in India, generally charging about $100. Community networks typically asked for some information about the pregnancy and either delivered or mailed pills with detailed instructions, often for free.

Online vendors, which supplied a small percentage of the pills in the study and charged between $39 and $470, generally did not ask for women’s medical history and shipped the pills with the least detailed instructions. Vendors in the study were vetted by Plan C and found to be providing genuine abortion pills, Dr. Aiken said.

The Guttmacher report, focusing on the formal health care system, included data from clinics and telemedicine abortion services within the United States that provided abortion to patients who lived in or traveled to states with legal abortion between January and December 2023.

It found that pills accounted for 63 percent of those abortions, up from 53 percent in 2020. The total number of abortions in the report was over a million for the first time in more than a decade.

Why This Matters

Overall, the new reports suggest how rapidly the provision of abortion has adjusted amid post-Roe abortion bans in 14 states and tight restrictions in others.

The numbers may be an undercount and do not reflect the most recent shift: shield laws in six states allowing abortion providers to prescribe and mail pills to tens of thousands of women in states with bans without requiring them to travel. Since last summer, for example, Aid Access has stopped shipping medication from overseas and operating outside the formal health system; it is instead mailing pills to states with bans from within the United States with the protection of shield laws.

What’s Next

In the case that will be argued before the Supreme Court on Tuesday, the plaintiffs, who oppose abortion, are suing the Food and Drug Administration, seeking to block or drastically limit the availability of mifepristone, the first pill in the two-drug medication abortion regimen.

The JAMA study suggests that such a ruling could prompt more women to use avenues outside the formal American health care system, such as pills from other countries.

“There’s so many unknowns about what will happen with the decision,” Dr. Aiken said.

She added: “It’s possible that a decision by the Supreme Court in favor of the plaintiffs could have a knock-on effect where more people are looking to access outside the formal health care setting, either because they’re worried that access is going away or they’re having more trouble accessing the medications.”

Pam Belluck is a health and science reporter, covering a range of subjects, including reproductive health, long Covid, brain science, neurological disorders, mental health and genetics. More about Pam Belluck

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Title: realm: reference resolution as language modeling.

Abstract: Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user's screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in reference resolution, particularly for non-conversational entities, remains underutilized. This paper demonstrates how LLMs can be used to create an extremely effective system to resolve references of various types, by showing how reference resolution can be converted into a language modeling problem, despite involving forms of entities like those on screen that are not traditionally conducive to being reduced to a text-only modality. We demonstrate large improvements over an existing system with similar functionality across different types of references, with our smallest model obtaining absolute gains of over 5% for on-screen references. We also benchmark against GPT-3.5 and GPT-4, with our smallest model achieving performance comparable to that of GPT-4, and our larger models substantially outperforming it.

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Americans’ use of chatgpt is ticking up, but few trust its election information.

It’s been more than a year since ChatGPT’s public debut set the tech world abuzz . And Americans’ use of the chatbot is ticking up: 23% of U.S. adults say they have ever used it, according to a Pew Research Center survey conducted in February, up from 18% in July 2023.

The February survey also asked Americans about several ways they might use ChatGPT, including for workplace tasks, for learning and for fun. While growing shares of Americans are using the chatbot for these purposes, the public is more wary than not of what the chatbot might tell them about the 2024 U.S. presidential election. About four-in-ten adults have not too much or no trust in the election information that comes from ChatGPT. By comparison, just 2% have a great deal or quite a bit of trust.

Pew Research Center conducted this study to understand Americans’ use of ChatGPT and their attitudes about the chatbot. For this analysis, we surveyed 10,133 U.S. adults from Feb. 7 to Feb. 11, 2024.

Everyone who took part in the survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way, nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for this analysis , along with responses, and the survey methodology .

Below we’ll look more closely at:

• Which U.S. adults have used ChatGPT
• How Americans are using it
• How much Americans trust ChatGPT’s election information

Who has used ChatGPT?

Most Americans still haven’t used the chatbot, despite the uptick since our July 2023 survey on this topic . But some groups remain far more likely to have used it than others.

Differences by age

Adults under 30 stand out: 43% of these young adults have used ChatGPT, up 10 percentage points since last summer. Use of the chatbot is also up slightly among those ages 30 to 49 and 50 to 64. Still, these groups remain less likely than their younger peers to have used the technology. Just 6% of Americans 65 and up have used ChatGPT.

Differences by education

Highly educated adults are most likely to have used ChatGPT: 37% of those with a postgraduate or other advanced degree have done so, up 8 points since July 2023. This group is more likely to have used ChatGPT than those with a bachelor’s degree only (29%), some college experience (23%) or a high school diploma or less (12%).

How have Americans used ChatGPT?

Since March 2023, we’ve also tracked three potential reasons Americans might use ChatGPT: for work, to learn something new or for entertainment.

The share of employed Americans who have used ChatGPT on the job increased from 8% in March 2023 to 20% in February 2024, including an 8-point increase since July.

Turning to U.S. adults overall, about one-in-five have used ChatGPT to learn something new (17%) or for entertainment (17%). These shares have increased from about one-in-ten in March 2023.

Use of ChatGPT for work, learning or entertainment has largely risen across age groups over the past year. Still, there are striking differences between these groups (those 18 to 29, 30 to 49, and 50 and older).

For example, about three-in-ten employed adults under 30 (31%) say they have used it for tasks at work – up 19 points from a year ago, with much of that increase happening since July. These younger workers are more likely than their older peers to have used ChatGPT in this way.

Adults under 30 also stand out in using the chatbot for learning. And when it comes to entertainment, those under 50 are more likely than older adults to use ChatGPT for this purpose.

A third of employed Americans with a postgraduate degree have used ChatGPT for work, compared with smaller shares of workers who have a bachelor’s degree only (25%), some college (19%) or a high school diploma or less (8%).

Those shares have each roughly tripled since March 2023 for workers with a postgraduate degree, bachelor’s degree or some college. Among workers with a high school diploma or less, use is statistically unchanged from a year ago.

Using ChatGPT for other purposes also varies by education level, though the patterns are slightly different. For example, a quarter each of postgraduate and bachelor’s degree-holders have used ChatGPT for learning, compared with 16% of those with some college experience and 11% of those with a high school diploma or less education. Each of these shares is up from a year ago.

ChatGPT and the 2024 presidential election

With more people using ChatGPT, we also wanted to understand whether Americans trust the information they get from it, particularly in the context of U.S. politics.

About four-in-ten Americans (38%) don’t trust the information that comes from ChatGPT about the 2024 U.S. presidential election – that is, they say they have not too much trust (18%) or no trust at all (20%).

A mere 2% have a great deal or quite a bit of trust, while 10% have some trust.

Another 15% aren’t sure, while 34% have not heard of ChatGPT.

Distrust far outweighs trust regardless of political party. About four-in-ten Republicans and Democrats alike (including those who lean toward each party) have not too much or no trust at all in ChatGPT’s election information.

Notably, however, very few Americans have actually used the chatbot to find information about the presidential election: Just 2% of adults say they have done so, including 2% of Democrats and Democratic-leaning independents and 1% of Republicans and GOP leaners.

These survey findings come amid growing national attention on chatbots and misinformation. Several tech companies have recently pledged to prevent the misuse of artificial intelligence – including chatbots – in this year’s election. But recent reports suggest chatbots themselves may provide misleading answers to election-related questions .

Note: Here are the questions used for this analysis , along with responses, and the survey methodology .

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New Research Reveals Full Diversity of Killer Whales as Two Species Come into View on Pacific Coast

March 27, 2024

Long viewed as one worldwide species, killer whale diversity now merits more. Southern Resident Connections - Post 35

Scientists have resolved one of the outstanding questions about one of the world’s most recognizable creatures, identifying two well-known killer whales in the North Pacific Ocean as separate species.

Killer whales are one of the most widespread animals on Earth. They have long been considered one worldwide species known scientifically as Orcinus orca , with different forms in various regions known as “ecotypes.”

However, biologists have increasingly recognized the differences between resident and Bigg’s killer whales. Resident killer whales maintain tight-knit family pods and prey on salmon and other marine fish. Bigg’s killer whales roam in smaller groups, preying on other marine mammals such as seals and whales. (Killer whales actually belong to the dolphin family.) Bigg’s killer whales, sometimes called transients, are named for Canadian scientist Michael Bigg, the first to describe telltale differences between the two types.

He noted in the 1970s that the two animals did not mix with each other even when they occupied many of the same coastal waters. This is often a sign of different species.

The finding recognizes the accuracy of the listing of Southern Resident killer whales as a Distinct Population Segment warranting protection under the Endangered Species Act in 2005. At the time, NOAA described the distinct population segment as part of an unnamed subspecies of resident killer whales in the North Pacific.

Now a team of scientists from NOAA Fisheries and universities have assembled genetic, physical, and behavioral evidence. The data distinguish two of the killer whale ecotypes of the North Pacific Coast—residents and Bigg’s—as separate species.

“We started to ask this question 20 years ago, but we didn’t have much data, and we did not have the tools that we do now,” said Phil Morin, an evolutionary geneticist at NOAA Fisheries’ Southwest Fisheries Science Center and lead author of the new paper . “Now we have more of both, and the weight of the evidence says these are different species.”

Genetic data from previous studies revealed that the two species likely diverged more than 300,000 years ago and come from opposite ends of the killer whale family tree. That makes them about as genetically different as any killer whale ecotypes around the globe. Subsequent studies of genomic data confirm that they have evolved as genetically and culturally distinct groups, which occupy different niches in the same Northwest marine ecosystem.

“They’re the most different killer whales in the world, and they live right next to each other and see each other all the time,” said Barbara Taylor, a former NOAA Fisheries marine mammal biologist who was part of the science panel that assessed the status of Southern Residents. “They just do not mix.”

Recognizing New Species

The Taxonomy Committee of the Society of Marine Mammalogy will determine whether to recognize the new species in its official list of marine mammal species . The committee will likely determine whether to accept the new designations at its next annual review this summer.

The scientists proposed scientific names for the new species based on their earliest published descriptions in the 1800s. Neither will keep the ubiquitous worldwide moniker, orca . The team proposed to call resident killer whales Orcinus ater , a Latin reference to their dominant black coloring. Bigg’s killer whales would be called Orcinus rectipinnus , a combination of Latin words for erect wing, probably referring to their tall, sharp dorsal fin.

Both species names were originally published in 1869 by Edward Drinker Cope, a Pennsylvania scientist known more for unearthing dinosaurs than studying marine mammals. He was working from a manuscript that California whaling captain Charles Melville Scammon had sent to the Smithsonian Institution describing West Coast marine mammals, including the two killer whales. While Cope credited Scammon for the descriptions, Scammon took issue with Cope for editing and publishing Scammon’s work without telling him. (See accompanying story .)

The Smithsonian Institution had shared Scammon’s work with Cope, and a Smithsonian official later apologized to Scammon for what he called “Cope’s absurd blunder.”

Species Reflect Ecosystem

The contested question of whether Southern Residents were distinct enough to merit endangered species protections initially drove much of the research that helped differentiate the two species, said Eric Archer, who leads the Marine Mammal Genetics Program at the Southwest Fisheries Science Center and is a coauthor of the new research paper. The increasing processing power of computers has made it possible to examine killer whale DNA in ever finer detail. He said the findings not only validate protection for the animals themselves, but also help reveal different components of the marine ecosystems the whales depend on.

“As we better understand what makes these species special, we learn more about how they use the ecosystems they inhabit and what makes those environments special, too,” he said.

The new research synthesizes the earliest accounts of killer whales on the Pacific Coast with modern data on physical characteristics. The team also use aerial imaging (called photogrammetry ), and measurement and genetic testing of museum specimens at the Smithsonian and elsewhere. While the two species look similar to the untrained eye, the evidence demonstrates they are very different species. The two species use different ecological niches, such as specializing in different prey, said Kim Parsons, a geneticist at the NOAA Fisheries Northwest Fisheries Science Center in Seattle and coauthor of the new research.

Recent research with drones that collect precise aerial photos has helped differentiate Bigg’s killer whales as longer and larger. This might better equip them to go after large marine mammal prey. The smaller size of residents is likely better suited to deep dives after their salmon prey, said John Durban, an associate professor at Oregon State University’s Marine Mammal Institute. His killer whale drone research is done collaboratively with Holly Fearnbach, a researcher at SR³.

The different prey of the two species may also help explain their different trajectories. Southern Residents are listed as endangered in part because of the scarcity of their salmon prey. Bigg’s killer whales, by contrast, have multiplied while feeding on plentiful marine mammals, including California sea lions.

While killer whales represent some of the most efficient predators the world has ever seen, Durban said science is still unraveling the diversity among them. The identification of additional killer whale species is likely to follow. One leading candidate may be “Type D” killer whales identified in the Southern Ocean around Antarctica.

Other killer whales in Antarctic waters also look very different from the best-known black and white killer whales. This reflects a wider diversity within the species, said Durban, who has used drones to study killer whales around the world. “The more we learn,” he said, “the clearer it becomes to me that at least some of these types will be recognized as different species in due course.”

Southern Resident Connections

Southern Resident killer whales are icons of a vibrant but struggling marine ecosystem that is important to us all. Join us in exploring the ecological connections that tie this system together, and the ways we are protecting and working to recover the whales we all care so much about.

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More Information

• New Research Reveals Two Species of Killer Whale
• How Scientists Chose Names for Newly Identified Killer Whale Species
• Two Species of Killer Whale Infographic
• Marine Mammal Genetics Research
• 2004 Status Review of Southern Resident Killer Whales
• Saving the Southern Resident Killer Whales
• Listing of Southern Resident Killer Whale Under the ESA
• Killer Whale Ecotypes Poster

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• Open access
• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

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

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

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

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

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Introduction

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

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

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

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

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

Extensive analysis identifies relationships between chemical compounds in beer

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

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

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

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

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

Tasting panel assessments reveal sensorial relationships in beer

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

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

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

Tasting panel assessments of specific flavors correlate with chemical composition

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

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

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

Public consumer reviews complement expert panel data

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

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

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

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

Models can predict beer sensory profiles from chemical data

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Model validation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Beer selection

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

Chemical dataset

Sample preparation.

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

HS-GC-FID/FPD

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

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

HS-SPME-GC-MS

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

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

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

Discrete photometric and enzymatic analysis

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

NIR analyses

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

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

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

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

Online reviews from a public database

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

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

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

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

Beer price collection and processing

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

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

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

Model dissection

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

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

Validation of causal chemical properties

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

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

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

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

Reporting summary

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

Data availability

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

Code availability

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

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Acknowledgements

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

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

Authors and Affiliations

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

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

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

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

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

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

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

Łukasz Kreft & Alexander Botzki

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

Philippe Malcorps & Luk Daenen

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Contributions

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

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Correspondence to Kevin J. Verstrepen .

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

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

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