research an abstract noun

Understanding an Abstract Noun (Definition, Examples, Word List)

What is an abstract noun? How is it different from a common noun ? What are words that represent an abstract noun? These are all great questions that you probably have . Abstract nouns can get confusing when comparing them to regular common nouns or proper nouns. This comprehensive guide will break down the abstract noun, its use , and the functions that grammatically govern it.

What is an abstract noun? 

An abstract noun is a type of noun that represents intangible things. Things you can’t perceive with the five primary senses in the human body (taste, touch, smell, etc.).

Abstract noun definition 

As the name suggests, an abstract noun is a noun type. It refers to an intangible idea (one that you cannot fathom using your five senses). Such intangible concepts could include emotions, qualities, ideas, etc.

All nouns that do not have a tangible or physical object to refer to fall under the bracket of abstract nouns . Abstract nouns are widely used in English proverbs. 

Some common examples include health, wealth, parenthood, anger, courage, and more. 

Abstract noun compared to other nouns 

Nouns are an essential part of speech. They are instrumental in naming places, people, objects, animals, and intangible ideas.

You may have noticed that whenever you write a sentence , you are using at least one noun in it.

Nouns can get used differently in different sentence formations. Their functions can vary. Here are the main types of nouns you could use in a complete sentence:

Proper Nouns

Proper nouns are naming agents for places, people, or things. They usually start with a capital letter. 

For example:

• My name is Lisa. (Lisa is the proper noun )
• John lives in Finland. (Finland is the proper noun)
• Jazz is a famous book. (Jazz is the proper noun)

Common Nouns 

Nouns that refer to generic things are referred to as common nouns . 

• I bought a new book yesterday. (Book is the common noun)
• There is a pigeon on the windowsill. (Pigeon is the common noun)
• Rob bought a blue car. (Car is the common noun)

Countable Nouns 

Nouns that can be measured or counted are called countable nouns . 

• I take two spoons of sugar in my tea. (“Two” is the countable noun )
• She bought a dozen bananas at the market. (“A dozen” is the countable noun) 

Uncountable Nouns 

Nouns that cannot be measured or counted are called uncountable nouns. 

• I have plenty of homework. (Plenty is the uncountable noun)
• Is that enough milk in your coffee? (Enough is the uncountable noun)

Collective Nouns 

Collective nouns depict a group of objects, people, animals, and more. 

• A flock of sheep 
• A pile of books 
• A school of fish 
• A bevy of women 

Concrete Nouns 

Also referred to as material nouns, concrete nouns refer to things that have a physical presence and can be perceived using the five senses. 

Abstract Nouns 

Any noun that is intangible or which cannot be perceived using the five senses is an abstract noun . 

• Bravery is a virtue. (Bravery is the abstract noun)
• My childhood was merry and fun . (Childhood is the abstract noun)

Abstract nouns in comparison to concrete nouns 

Concrete noun, as the name suggests, includes all those objects which have a physical presence and are tangible. They can be perceived with the help of our five senses. These include nouns such as book, pen, cup, table silk, door, car, and so on. 

• I travel to school by bus . (School and bus are both concrete nouns )
• Sally opened the door. (Door is the concrete noun) 

Abstract nouns include everything that is intangible and cannot be perceived by the five senses. These include emotions, feelings, ideas, and more. 

• Honesty is the best policy. (Honesty is the abstract noun)
• Freedom is my birthright. (Freedom is the abstract noun) 

Abstract noun word list 

Here are some examples of abstract nouns based on their kind. 

• Feelings – sympathy, fear, anxiety, stress, pleasure
• State – Chaos, peace, misery, freedom
• Emotions – anger, joy, sorrow, hate 
• Qualities – determination, courage, honesty, generosity, patience 
• Concepts – democracy, charity, deceit, opportunity, comfort 
• Moments – career, death, marriage, childhood, birth 

More examples of commonly used abstract nouns

• Bravery 
• Brilliance 
• Childhood 
• Comfort 
• Compassion 
• Communication 
• Curiosity 
• Culture 
• Dedication 
• Energy 
• Faith 
• Friendship 
• Gossip 
• Information 
• Imagination 
• Intelligence 
• Integrity 
• Justice 
• Knowledge 
• Kindness 
• Liberty 
• Loyalty 
• Luxury 
• Motivation 
• Perseverance 
• Relaxation 
• Skill 
• Satisfaction 
• Strength 
• Success 
• Thought 
• Talent 
• Truth 
• Trust 
• Wisdom 
• Warmth 

Sentence examples with abstract nouns 

The following are three sentence examples with abstract nouns – 

• This cafe has a pleasant ambiance. (Ambiance is the abstract noun)
• Pride is a deadly sin. (Pride is the abstract noun)
• My friendship with Peter is of seven years . (Friendship is the abstract noun) 

Conversion of Verbs and Adjectives into Abstract Nouns 

Convert verbs and adjectives into abstract nouns by adding a suffix . The reverse is also a possibility. 

• Perceive – Perception 
• Inform – Information 
• Determine – Determination 
• Dark – Darkness 
• Silent – Silence

Why are abstract nouns important? 

Abstract nouns are tricky. Use concrete nouns to make them understandable in sentences. Abstract nouns are not of much use from a business point of view.

However, they are an integral part of any English grammar course. Conversions between abstract nouns and verbs or adjectives are essential while learning complete sentence construction.

Yes, warmth is an abstract noun. 

The abstract form of ability (abstract noun) is able.

Five examples of abstract nouns include honesty, glory, patience, determination, and truth.

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About the author

Dalia Y.: Dalia is an English Major and linguistics expert with an additional degree in Psychology. Dalia has featured articles on Forbes, Inc, Fast Company, Grammarly, and many more. She covers English, ESL, and all things grammar on GrammarBrain.

Core lessons

• Abstract Noun
• Accusative Case
• Active Sentence
• Alliteration
• Adjective Clause
• Adjective Phrase
• Adverbial Clause
• Appositive Phrase
• Body Paragraph
• Compound Adjective
• Complex Sentence
• Compound Words
• Compound Predicate
• Common Noun
• Comparative Adjective
• Comparative and Superlative
• Compound Noun
• Compound Subject
• Compound Sentence
• Copular Verb
• Collective Noun
• Colloquialism
• Conciseness
• Conditional
• Concrete Noun
• Conjunction
• Conjugation
• Conditional Sentence
• Comma Splice
• Correlative Conjunction
• Coordinating Conjunction
• Coordinate Adjective
• Cumulative Adjective
• Dative Case
• Declarative Statement
• Direct Object Pronoun
• Direct Object
• Dangling Modifier
• Demonstrative Pronoun
• Demonstrative Adjective
• Direct Characterization
• Definite Article
• Doublespeak
• Equivocation Fallacy
• Future Perfect Progressive
• Future Simple
• Future Perfect Continuous
• Future Perfect
• First Conditional
• Gerund Phrase
• Genitive Case
• Helping Verb
• Irregular Adjective
• Irregular Verb
• Imperative Sentence
• Indefinite Article
• Intransitive Verb
• Introductory Phrase
• Indefinite Pronoun
• Indirect Characterization
• Interrogative Sentence
• Intensive Pronoun
• Inanimate Object
• Indefinite Tense
• Infinitive Phrase
• Interjection
• Intensifier
• Indicative Mood
• Juxtaposition
• Linking Verb
• Misplaced Modifier
• Nominative Case
• Noun Adjective
• Object Pronoun
• Object Complement
• Order of Adjectives
• Parallelism
• Prepositional Phrase
• Past Simple Tense
• Past Continuous Tense
• Past Perfect Tense
• Past Progressive Tense
• Present Simple Tense
• Present Perfect Tense
• Personal Pronoun
• Personification
• Persuasive Writing
• Parallel Structure
• Phrasal Verb
• Predicate Adjective
• Predicate Nominative
• Phonetic Language
• Plural Noun
• Punctuation
• Punctuation Marks
• Preposition
• Preposition of Place
• Parts of Speech
• Possessive Adjective
• Possessive Determiner
• Possessive Case
• Possessive Noun
• Proper Adjective
• Proper Noun
• Present Participle
• Quotation Marks
• Relative Pronoun
• Reflexive Pronoun
• Reciprocal Pronoun
• Subordinating Conjunction
• Simple Future Tense
• Stative Verb
• Subjunctive
• Subject Complement
• Subject of a Sentence
• Sentence Variety
• Second Conditional
• Superlative Adjective
• Slash Symbol
• Topic Sentence
• Types of Nouns
• Types of Sentences
• Uncountable Noun
• Vowels and Consonants

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9 The meaning of “abstract nouns”: Locke, Bentham, and contemporary semantics

• Published: November 2013
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The meaning of “abstract nouns” raises fundamental philosophical and linguistic questions. Nobody was more aware of this than John Locke, whose treatment of the subject must be the central point of reference for modern semantics. Subsequent to Locke, Jeremy Bentham made another remarkable contribution with his theory of “fictitious entities”. In this chapter we set out an account of abstract nouns which builds on and seeks to re-connect with these largely forgotten antecedents. The chapter proposes several semantic templates for abstract noun meanings, and illustrates them with explications for English words such as illness, trauma, violence, suicide, beauty , and temperature . The chapter also deals with the important role of abstract nouns in constituting topics of discourse and with the profound untranslatability of many abstract nouns.

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• English Grammar
• Parts of Speech
• Abstract Nouns

Abstract Nouns - Definition, Examples and Usage

Abstract nouns are naming words that you cannot see, smell, touch or perceive by any of your five senses. Learn more about abstract nouns, definitions, examples and usage of abstract nouns in this article.

Table of Contents

Definition of an abstract noun, converting verbs and adjectives into abstract nouns, test your knowledge on abstract nouns, frequently asked questions on abstract nouns, what is an abstract noun.

An abstract noun is used to refer to concepts, ideas, experiences, traits, feelings or entities that cannot be seen, heard, tasted, smelt or touched. Abstract nouns are not concrete or tangible. There are a lot of abstract nouns (virtues) used in proverbs.

An abstract noun is defined as ‘a noun , for example, beauty or freedom , that refers to an idea or a general quality, not to a physical object’, according to the Oxford Learners Dictionary. According to Collins Dictionary, ‘an abstract noun refers to a quality or idea rather than to a physical object.’

Examples of Abstract Nouns

Check out the following examples of abstract nouns.

A verb or an adjective can be converted into an abstract noun by the addition of a suffix and vice versa. Have a look at the examples given below.

Converting Verbs to Abstract Nouns

• Move – movement
• Reflect – reflection
• Perceive – perception
• Conscious – Consciousness
• Appear – Appearance
• Resist – Resistance
• Appoint – appointment
• Enjoy – enjoyment
• Assign – assignment
• Inform – information
• Decide – decision
• Describe – description
• Determine – determination
• Block – blockade

Converting Adjectives to Abstract Nouns

• Brave – bravery
• Truth – truthful
• Honest – honesty
• Weak – weakness
• Happy – happiness
• Sad – sadness
• Mad – madness
• Responsible – responsibility
• Possible – possibility
• Probable – probability
• Able – ability
• Independent – independence
• Free – freedom
• Silent – silence

Some words can function both as a noun and a verb without any change in spelling. Here are some examples for you.

• Love as a verb – I love the way she works with it.

Love as a noun – Love is one of the qualities everyone should possess

• Divorce as a verb – Harry cannot divorce his wife.

Divorce as a noun – Are you getting a divorce?

• Aim as a verb – You have to aim for the highest grades.

Aim as a noun – What is your aim?

• Battle as a verb – Teena had to battle hard to stay in shape.

Battle as a noun – Do you know who won the battle?

• Play as a verb – The children are playing outdoor games.

Play as a noun – The Shakespearean play was performed by young artists.

Let us now check how much you have learned about abstract nouns. Identify the abstract nouns in the following sentences.

• Honesty is the best policy.
• There is no possibility for you to reach home by six in the evening.
• This place has a really pleasant ambience.
• Pride goes before a fall.
• Brevity is the soul of wit.
• That man is testing my patience.
• Have you read about the theory of evolution?
• Truthfulness is always appreciated.
• Friendship is priceless.
• What do you think about his idea?

Let us find out if you have understood correctly. Check your answers here.

• Honesty is the best policy .
• This place has a really pleasant ambience .
• Brevity is the soul of wit .
• That man is testing my patience .
• Have you read about the theory of evolution ?
• What do you think about his idea ?

What is an abstract noun?

An abstract noun is used to refer to concepts, ideas, experiences, traits, feelings or entities that cannot be seen, heard, tasted, smelt or touched. Abstract nouns are not concrete or tangible.

Give some examples of abstract nouns.

Love, concept, experience, courage, judgement, probability, freedom and soul are some examples of abstract nouns.

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Abstract Noun

What is an abstract noun.

• consideration, parenthood, belief, anger

Table of Contents

More Examples of Abstract Nouns

Find the abstract noun test, abstract nouns vs concrete nouns, list of abstract nouns, why abstract nouns are important, video lesson.

Abstract or Concrete? It Could Be Ambiguous.

• anger, anxiety, beauty, beliefs, bravery, brilliance, chaos, charity, childhood, comfort, communication, compassion, courage, culture, curiosity, deceit, dedication, democracy, determination, energy, failure, faith, fear, freedom, friendship, generosity, gossip, happiness, hate, honesty, hope, imagination, information, integrity, intelligence, joy, justice, kindness, knowledge, liberty, life, love, loyalty, luxury, misery, motivation, opportunity, pain, patience, peace, perseverance, pleasure, pride, relaxation, sacrifice, satisfaction, skill, strength, success, sympathy, talent, thought, trust, truth, warmth, wisdom
• ...and my bicycle never leaned against the garage as it does today, all the dark blue speed drained out of it. (from "On Turning Ten" by American Poet Laureate Billy Collins
• If writing a poem, consider expressing abstract ideas using concrete nouns.

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This page was written by Craig Shrives .

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Abstract Nouns

What are abstract nouns.

Abstract nouns signify things that are impossible for us to perceive with the 5 senses.  These are nouns that are described as intangible or immaterial, which means we can’t hear, see, smell, taste, or touch them. They represent ideas and qualities that lack physical forms.

Let’s look at the following examples:

• Tae Hyung has shown great determination during the tryouts.
• They’ve known each other for 4 decades. Their friendship is truly remarkable. 
• Satu’s enthusiasm for a software overhaul is quite infectious.
• What kind of impression did you want to give to your colleagues?
• I know it was late so I deeply appreciate your consideration .

Abstract nouns can be classified in various ways, but to avoid repetition, abstract nouns may fall into the following groups:

• Human Qualities – dedication, sanity, beauty, honesty, intelligence, bravery, strength, jealousy, brilliance, calmness, sympathy, compassion, wisdom, patience, confidence, stupidity, honor, sophistication, wit, goodness
• Emotions and Feelings – love, hatred, envy, despair, sorrow, hope, anger, delight, excitement, grief, surprise, worry, regret, fascination, tiredness, pleasure, relief, misery, satisfaction, amazement
• Concepts and Ideas – adventure, loss, mercy, communication, knowledge, imagination, dictatorship, faith, opportunity, forgiveness, idea, fragility, liberty, motivation, justice, luxury, necessity, peace, reality, parenthood

Abstract nouns are usually studied in contrast to concrete nouns. Concrete nouns represent nouns that can be perceived by the 5 senses. Cars, butterflies, pizza, the Leaning Tower of Pisa, wands, and so on.

Abstract Nouns Rules

Study the table below for some rules for using concrete nouns:

3 Types of Abstract Nouns Examples

The entire list of abstract nouns is extensive. They refer to qualities, feelings, states of being, and characteristics. The following are examples of abstract nouns in sentences, grouped into 3 main ideas:

1. Human Qualities

• Liam’s scientific curiosity has always been there since he was a kid.
• My friends have done pretty well in life but they never treated me with any ego .
• When will Katrina develop the courage to stand up for herself?
• Samsoon takes his sense of determination after his dad.
• We need candidates who show enthusiasm for teamwork and mutual support.

2. Emotions and Feelings

• Young-hwan’s disbelief at the magician’s routine was apparent in his expression.
• Rainy weather always fills me with a strange kind of sadness .
• It’s not unusual to feel a level of uncertainty after you graduate.
• Alexis and his parents must be beaming with pride when he received a scholarship.
• Luka feels defeated so you’d do well to hide your disappointment about his score.

3. Concepts and Ideas

• Kugaha’s latest installation is marvelous. How does a person achieve such artistry ?
• Tony has seen his share of evil after ten years of working in the force.
• Unemployment is on the rise but I wonder if it’s because people are choosy.
• The truth is, Uriel wanted to study music but he opted for an industry that pays well.
• Many English Club members saw a vast improvement in their grades at school.

Abstract Nouns Exercises with Answers

Exercise on abstract nouns.

Identify whether the underlined noun in each sentence is abstract or concrete:

1. His family has run a business making custom signs for over 9 years.

2. We thank the foundation for such generosity in our outreach programs.

3. One of the issues plaguing the city is squalor in two of its districts.

4. Several hospitals in the region had built dormitories for front liners.

5. They say the opposite, but I think favoritism exists in families.

6. Are these the native dances that we should be doing research on?

7. There are some horror stories going around about the old hotel.

8. Ultimately, Kendra’s logic skills led her team to victory on the challenge.

9. You would think that parenthood is easy, but it’s extremely difficult.

10. I think I sprained some fingers after cutting so much wood.

11. Were there a lot of children at the park today?

12. Willem used to make colorful paper airplanes  to sell to his classmates.

13. The law should champion the defenseless, but it doesn’t seem like it.

14. Won’t you show me some mercy and not give me a ticket? Please?

15. Shaylene wanted to use a specific design of bricks for the pathway.

16. His fascination for anatomy has been misunderstood as a dark side.

17. Luigi bought too many bottles of water so they started giving them out.

18. How much information can you gather after a weekend of interviews?

19. There is a great need for more sustainable practices in the fishing industry.

20. Will we have time to visit a few temples at least? I want to take photos.

1. signs: concrete

2. generosity: abstract

3. squalor: abstract

4. hospitals: concrete

5. favoritism: abstract

6. dances: concrete

7. stories: concrete

8. redemption: abstract

9. victory: abstract

10. fingers: concrete

11. children: concrete

12. airplanes: concrete

13. law: abstract

14: mercy: abstract

15: bricks: concrete

16. fascination: abstract

17. bottles: concrete

18. knowledge: abstract

19. need: abstract

20. temples: concrete

Abstract Nouns List

The following table is a list of more abstract nouns:

Advice for ESL Students & English Language Learners

Nouns are considered the main part of speech in English grammar. They comprise the names of everything in existence, after all. But because of their volume, mastering the different types, rules, and overlapping concepts of nouns can be a huge challenge to English language learners. However, there are a few things that can make language studies a bit easier, not only with nouns but with all the other grammar subjects in the English language, too. The following advice serves that purpose. Read along and consider following them to aid with achieving your language goals.

1. Use Grammar Lists

There are fewer grammar tools that can function as effectively as lists, tables, charts, and diagrams. These tools are valuable in introducing grammar concepts and breaking them down into simplified segments. They can make topics much easier to grasp and almost always contain real-world sentence examples that are great for the acquisition of new workable vocabulary and the construction of sentences. The challenge is picking the ones that work for you. If you can’t find any, you can make your own and customize it according to your own study habits and preferences.

2. Use Audio-Visual Resources

Traditional classes aren’t enough for learning a language. Independent learning should go hand in hand with formal academic training. Since self-studying is a necessity, a great way to maximize it is to learn with the right resources. One effective and smart way to do so is to ensure that you have ample exposure to English media. Incorporating audio-visual materials is both an educational and entertaining way to achieve fluency. TV shows, films, podcasts, dedicated instructional videos, interactive learning software like LillyPad.ai, social media clips, and so on can show you how English speakers (native or otherwise) use the language in different professional, academic, and social contexts. You only need to consume these tools with purpose, which means taking content in with the intention of learning elements of the language. It can go a long way to add some punch to your aptitude.

3. Practical Use

Teachers from all branches of study would share the adage “theory means nothing without practice.” This is especially true when you’re learning languages. Your teachers are simply your guides; they won’t be there to speak or write English for you. The most efficient way to improve your level is to use the language as often as possible. It isn’t uncommon for someone who has impeccable grammar to be horrible at speaking or verbal communication. It’s likely because a major part of their studies is spent on books, not in actual interaction. While it’s true that most English language learners don’t live in areas where English is spoken all the time, there’s always a way to create your own learning environment. You can organize study groups or English clubs with same-minded people. You can nurture friendships both with native and non-native speakers alike. Not only will you have the avenue for practicing English, you can also develop your social and cultural intelligence.

Additionally, it is important for learners to properly understand concrete nouns and common and proper nouns .

Common Errors Made by English Learners

Errors in concrete nouns are rooted in any of the three factors below. Study the table in order to avoid making the same errors:

Learning Strategies and Best Practices with Abstract Nouns

The best way to master concrete nouns is to remember 3 simple things. Let’s take a look at the following list:

• The five senses are sight, smell, touch, taste, and hearing. Nouns that refer to these are sensory words and are therefore concrete.
• If a noun can’t be sensed physically, it’s an abstract noun. “Concept” nouns are all abstract. Most nouns that describe emotions are abstract. You can’t experience it with the senses, but rather experience it in thought or idea.
• Concrete and abstract nouns go hand in hand. We can understand abstract nouns better by adding concrete or sensory qualities to them. Concrete nouns illustrate abstract nouns further.

Abstract Nouns Frequently Asked Questions

Try to figure out the verbs from which these abstract nouns are taken:

1. blockade 2. movement 3. consciousness 4. appointment 5. resistance 6. reflection 7. perception 8. disappearance 9. enjoyment 10. hatred

Try to figure out the adjectives from which these abstract nouns are taken:

1. fragility 2. happiness 3. sincerity 4. gentleness 5. impossibility 6. freedom 7. madness 8. silence 9. dependence 10. responsibility

Abstract nouns are nouns that cannot be grasped by the five senses. This classification is comprised of ideas, emotions, or concepts. If you hate someone, it’s easy to see from your behavior: unreceptive, aloof, or blatantly dismissive. But you can’t actually see the word “hate.” Hate is considered an abstract noun.

No. It’s possible to quantify abstract nouns as long as you confirm that they are countable. For example, the word “skill” refers to a person’s ability to do something, but the word itself can’t actually be seen. It is an abstract noun.

When used generally, it is an uncountable noun. “Bob has skill,” for example. But when used to indicate different kinds of skills it is countable. “Bob has lots of amazing skills.”

All sensory nouns are concrete. A “chair” is a thing you can touch and see, and in some instances even smell or taste if you want to.

You can even hear it if someone hits or throws it. This makes the word a concrete noun. But ideas, emotions, and beliefs don’t have physical forms. Love, Christianity, law, and so on are examples of such and are considered abstract.

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What Are Abstract Nouns And How Do You Use Them?

• What's An Abstract Noun?
• Abstract Vs. Concrete Nouns
• Get Help With Grammar Coach

You probably know that a noun is a word that refers to a person, place, thing, or idea—this is a grammar concept we learn pretty early on in school. And there are, of course, several different types of nouns that we use to refer to all of the things we experience during our lives: We eat food. We meet friends. We go to the store. These nouns refer to the people and physical objects that we interact with.

But what about the things that we can’t actually see or touch? Aren’t words like love , victory , and alliance nouns, too? Yes, they are, and there is a term you may not remember from your grade-school days that we use to refer to these things: the abstract noun.

What is an abstract noun?

An abstract noun is “a noun denoting something immaterial and abstract.” Another common way to think about abstract nouns is that they refer to things that you cannot experience with the five senses . You cannot see, smell, hear, taste, or touch abstract nouns. Abstract nouns refer to intangible things that don’t exist as physical objects.

For example, the word cat refers to a cute animal. You can see and touch a cat. The noun cat is not an abstract noun. On the other hand, the word luck refers to a complex idea about how likely it is that good or bad events are going to happen to someone. Luck doesn’t exist as a physical object; you can’t eat luck nor can you go to a store and buy luck. Luck is an abstract noun because it refers to an intangible concept rather than a physical object that we can experience with our senses.

What about those nouns that you can tangibly sense? Learn more about concrete nouns here.

Abstract noun examples

Unlike most other nouns, abstract nouns don’t refer to people or places. After all, people and places are real things that exist in our world. Even nouns that refer to fictional characters and places, such as Godzilla or Valhalla , are not, the reasoning goes, abstract nouns because these things would have a physical form if they were actually real.

So, all abstract nouns are “things.” Remember, though, that abstract nouns only refer to intangible things such as emotions, ideas, philosophies, and concepts. Let’s stop being abstract and look at some specific examples so we can get a better understanding of abstract nouns.

Even though we often say that we “feel” emotions, we don’t mean that literally. You “feel” emotions like happiness or anger as thoughts in your mind or activity in your brain and body. You can’t hold happiness in your hand or eat a plate of sadness. You can see people or animals expressing these emotions through actions, but emotions are not tangible objects. So, we refer to them with abstract nouns.

• Examples: happiness, sadness, anger, surprise, disgust, joy, fear, anxiety, hope

Ideas, concepts and beliefs

Besides emotions, abstract nouns are also used to refer to other concepts and ideas. These kinds of abstract nouns give names to complex topics and give us a glimpse into a big part of what makes us human—our big, wrinkly brains! While most abstract nouns are common nouns, meaning that they refer to general ideas, they can also be proper nouns, such as Christianity.

• Examples: government, dedication, cruelty, justice, Christianity, Islam, Cubism

List of abstract nouns

Abstract nouns can be pretty tough to understand, so let’s look at a bunch more:

• religion, science, experimentation, research, magnetism, creativity, invisibility, kindness, greed, laziness, effort, speed, concentration, confusion, dizziness, time, situation, existence, death, anarchy, law, democracy, relief, opportunity, technology, discovery, hopelessness, defeat, friendship, patience, decay, holiness, youth, childhood, Stoicism, Marxism

The difference between abstract & concrete nouns

Getting a grasp on what abstract nouns are, exactly, can be tough. While abstract nouns refer to intangible things without a physical form, all of the people, places, and things that do actually have a physical form are referred to by a type of noun: a concrete noun. Unlike abstract nouns, concrete nouns can be experienced with the five senses: they can take a material form rather than an image, say, in your mind’s eye of catness.  You can see a tree . You can eat a pineapple. You can hear an engine. You can smell socks. You can touch a lamp.

So, your five senses can help you distinguish between abstract and concrete nouns. Remember, words for fictional people, places, and things are considered to be concrete nouns even if they don’t actually exist in our world. You may not be able to smell a zombie in everyday life, but you would be able to if it were real—just remember to run away if you ever saw one!

Let’s put your noun knowledge to the test with some example sentences. Read each sentence and see if you can figure out if each italicized noun is an abstract noun or a concrete noun.

• Billionaire Jeff Bezos is famous for his wealth.
• Next week, we are going on vacation to Belgium.
• When I grow up, I want to be a superhero.
• They said he was possessed by a ghost.
• The robot had many impressive abilities.
• Her blindness didn’t stop her from being successful.
• I was attacked by a swarm of bees.
• She sells seashells by the seashore.
• We heard shouting from next door.
•  The girl just wants attention from her parents.

Good grammar: not an abstract concept

We’ve got a noun for you: genius! And that’s what you’ll be when you check your writing on Thesaurus.com’s Grammar Coach™ . This writing tool uses machine learning technology uniquely designed to catch grammar and spelling errors. Its Synonym Swap will find the best nouns, adjectives, and more to help say what you really mean, guiding you toward clearer, stronger, writing.

Whether you’re writing about a person, place, or thing, perfect grammar has never been easier!

Answers: 1. Abstract 2. Concrete 3. Concrete 4. Concrete 5. Abstract 6. Abstract 7. Concrete 8. Concrete 9. Concrete 10. Abstract

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What Is Abstract Noun? Definitions, Rules & Examples

Grammar is an essential aspect of language, and understanding its basic concepts is crucial to effective communication. Abstract nouns are one such concept that can be challenging to comprehend. These nouns represent ideas, feelings, and qualities, making them different from concrete nouns that are tangible objects.

In this article, we will provide a comprehensive guide to abstract nouns, including definitions, rules, and examples to help you grasp the concept.

Table of Contents

What is an Abstract Noun?

An abstract noun is a word that represents a quality, idea, or concept that cannot be physically touched or seen. Unlike concrete nouns that refer to tangible objects, abstract nouns refer to intangible concepts that exist only in the mind. Examples of abstract nouns include:

• Intelligence

Rules for Identifying Abstract Nouns

Here are some rules to help you identify abstract nouns:

• Abstract nouns are always singular.
• They cannot be perceived by the five senses.
• They are usually intangible.
• They can be formed from adjectives, verbs, and common nouns.
• They can be used as the subject of a sentence or the object of a verb.

Examples of Abstract Nouns

Let’s take a look at some examples of abstract nouns in sentences:

• Love is the most powerful emotion.
• Honesty is the best policy.
• Wisdom comes with age and experience.
• Courage is not the absence of fear but the triumph over it.
• Beauty lies in the eyes of the beholder.
• Justice must be served for the greater good.
• Loyalty is a rare and valuable trait.
• Freedom is a fundamental human right.
• Intelligence is the ability to learn and understand.
• Joy is a feeling of great happiness.

Types of Abstract Nouns

Abstract nouns can be divided into different types based on the category they belong to. Here are some of the most common types of abstract nouns:

• Emotions and Feelings : Love, joy, anger, fear, happiness, sadness, hope, etc.
• Concepts and Ideas : Freedom, democracy, justice, democracy, equality, morality, etc.
• Qualities and Characteristics : Honesty, kindness, bravery, intelligence, beauty, etc.
• States and Conditions : Peace, war, health, sickness, poverty, wealth, etc.

Commonly Confused Abstract Nouns

Some abstract nouns can be easily confused with other parts of speech, such as adjectives, verbs, or even concrete nouns. Here are some examples of commonly confused abstract nouns:

• Education vs. Educated: Education is an abstract noun that represents the process of learning, while educated is an adjective that describes someone who has acquired knowledge through education.
• Silence vs. Quiet: Silence is an abstract noun that represents the absence of sound, while quiet is an adjective that describes a low level of noise.
• Idea vs. Opinion: An idea is an abstract noun that represents a concept or thought, while an opinion is a noun that represents a personal belief or viewpoint.

How to Use Abstract Nouns in a Sentence

Using abstract nouns in a sentence can be tricky. Here are some tips to help you use them correctly:

• Use them as the subject of a sentence: Abstract nouns can be used as the subject of a sentence. For example, “Love is a beautiful thing,” where love is the abstract noun.
• Use them as the object of a verb: Abstract nouns can also be used as the object of a verb. For example, “He showed great courage in the face of danger,” where courage is the abstract noun.
• Use them in prepositional phrases: Abstract nouns can also be used in prepositional phrases. For example, “She has a great sense of humor,” where humor is the abstract noun.
• Use them in comparisons: Abstract nouns can be used in comparisons to describe the degree of an attribute. For example, “His intelligence is higher than hers,” where intelligence is the abstract noun.

Tips for Identifying Abstract Nouns

Identifying abstract nouns can be a bit challenging, but with these tips, you’ll be able to identify them with ease:

• Look for nouns that represent qualities, ideas, or concepts that cannot be touched or seen.
• Identify nouns that are intangible and cannot be perceived by the five senses.
• Pay attention to singular nouns, as abstract nouns are always singular.
• Identify nouns that can be formed from adjectives, verbs, or common nouns.
• Look for nouns that can be used as the subject of a sentence or the object of a verb.
• Q. What is the difference between abstract and concrete nouns? A. Concrete nouns refer to tangible objects, while abstract nouns refer to qualities, ideas, and concepts that are intangible.
• Q. Can abstract nouns be plural? A. No, abstract nouns are always singular.
• Q. What are some common examples of abstract nouns? A. Some common examples of abstract nouns include love, joy, courage, beauty, honesty, intelligence, freedom, justice, loyalty, and wisdom.
• Q. Can abstract nouns be used in plural form? A. No, abstract nouns cannot be used in plural form as they represent intangible concepts that cannot be quantified.

In conclusion, abstract nouns represent intangible concepts, ideas, and qualities that cannot be physically touched or seen. They are always singular and cannot be used in plural form. Abstract nouns can be formed from adjectives, verbs, or common nouns, and they can be used as the subject of a sentence or the object of a verb.

By understanding abstract nouns, you can improve your communication skills and use language more effectively. With the help of the tips, rules, and examples provided in this article, you’ll be able to identify and use abstract nouns with confidence.

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Abstract Nouns: List of 165 Important Abstract Nouns from A to Z

By: Author English Study Online

Posted on Last updated: November 3, 2023

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If you’re learning English, you’ve probably come across these tricky little words before. In this article, we’ll be exploring what abstract nouns are, how to use them, and why they’re important in the English language. We’ll be providing examples of abstract nouns and explaining how they differ from concrete nouns. We’ll also be discussing how to recognize abstract nouns in a sentence and how to use them correctly in your writing.

Table of Contents

Abstract Noun Definition

Abstract nouns are intangible concepts or ideas that cannot be experienced with the five senses. They represent things like emotions , ideas, qualities , and states of being . Unlike concrete nouns that refer to physical objects or things that can be perceived by the senses, abstract nouns cannot be seen, touched, heard, smelled, or tasted.

Examples of abstract nouns include love, peace, hope, freedom, happiness, courage, and honesty . These nouns represent concepts that cannot be measured or quantified, but they are essential to human experience and communication. For example, we use abstract nouns like love to express a deep emotional connection to someone or something.

One way to identify abstract nouns is to think about whether you can see, touch, hear, smell, or taste the thing being described. If you cannot, it is likely an abstract noun. For example, the word “ beauty” is an abstract noun because it is a concept that cannot be seen or touched.

It is important to note that abstract nouns can be difficult to define precisely because they represent intangible concepts. However, they are essential to effective communication and can add depth and nuance to our language. By understanding abstract nouns, we can better express ourselves and connect with others on a deeper level.

Abstract Nouns List

Types of Abstract Nouns

As we mentioned earlier, abstract nouns are intangible ideas that cannot be perceived with the five senses. In this section, we will explore some of the different types of abstract nouns.

Emotions are one of the most common types of abstract nouns. They refer to feelings that we experience, such as love, anger, sadness, and happiness . These emotions cannot be seen or touched, but they can be felt and expressed through language and behavior.

Ideas are another type of abstract noun. They refer to concepts and thoughts that exist in our minds, such as freedom, democracy, justice, and equality . These ideas are not physical objects, but they can have a powerful impact on our lives and society.

Qualities are abstract nouns that describe characteristics or attributes of people, things, or ideas. Examples of qualities include honesty, bravery, intelligence, and creativity. These qualities cannot be seen or touched, but they can be demonstrated through actions and behaviors.

Experiences

Experiences are abstract nouns that refer to events or situations that we encounter in our lives. Examples of experiences include success, failure, adventure, and tragedy . These experiences cannot be physically touched or seen, but they can have a profound impact on our lives and shape who we are as individuals.

Abstract Nouns vs. Concrete Nouns

In English, nouns can be divided into two main categories: abstract nouns and concrete nouns . Abstract nouns are used to describe ideas, concepts, and feelings that cannot be perceived through the senses. Concrete nouns, on the other hand, are used to describe physical objects that can be seen, touched, heard, smelled, or tasted.

• For example, the word “ love ” is an abstract noun because it describes a feeling or emotion that cannot be seen or touched.
• In contrast, the word “ table ” is a concrete noun because it describes a physical object that can be seen and touched.

It is important to understand the difference between abstract and concrete nouns because they are used differently in sentences. Concrete nouns are often used as the subject or object of a sentence, while abstract nouns are often used to describe a quality or attribute of a concrete noun.

• For example, in the sentence “ The dog chased the ball ,” “dog” and “ball” are both concrete nouns because they describe physical objects.

In the sentence “The dog showed loyalty to its owner,” “loyalty” is an abstract noun because it describes a quality of the dog’s behavior.

Here are some more examples of abstract and concrete nouns:

List of Common Abstract Nouns

Usage of abstract nouns.

Abstract nouns play a crucial role in both writing and speech. In this section, we will explore the different ways in which abstract nouns can be used effectively.

Abstract nouns are often used in writing to convey emotions and ideas that cannot be easily expressed through concrete nouns. Here are some ways in which abstract nouns can be used effectively in writing:

• Describing emotions: Abstract nouns such as “love,” “happiness,” and “sadness” can be used to describe emotions in a way that is more impactful than using concrete nouns. For example, instead of saying “She felt a warm feeling in her heart,” we can say “She felt a deep sense of love.”
• Explaining concepts: Abstract nouns can be used to explain complex concepts in a concise and clear manner. For example, instead of saying “The process of photosynthesis involves the conversion of light energy into chemical energy,” we can say “Photosynthesis is the process by which plants convert light energy into chemical energy.”
• Creating imagery: Abstract nouns can be used to create vivid imagery in writing. For example, instead of saying “The sunset was beautiful,” we can say “The sky was painted with hues of orange, pink, and purple, creating a breathtaking display of beauty.”

Abstract nouns are also commonly used in speech to convey ideas and emotions. Here are some ways in which abstract nouns can be used effectively in speech:

• Expressing feelings: Abstract nouns can be used to express feelings and emotions in a way that is more impactful than using concrete nouns. For example, instead of saying “I am happy,” we can say “I am filled with a sense of happiness.”
• Discussing ideas: Abstract nouns can be used to discuss complex ideas and concepts in a clear and concise manner. For example, instead of saying “The economy is experiencing a period of growth,” we can say “There is a sense of prosperity in the economy.”
• Creating connections: Abstract nouns can be used to create connections between different ideas and concepts. For example, instead of saying “These two ideas are related,” we can say “There is a strong connection between these two concepts.”

Abstract Nouns List | Infographic

Practice Exercises

Practice exercises are a great way to reinforce your understanding of abstract nouns. In this section, we’ll cover two types of exercises: identifying exercises and usage exercises.

Identifying Exercises

In identifying exercises, you’ll be asked to identify the abstract noun in a sentence. Here are a few examples:

• The beauty of nature is awe-inspiring.
• Her kindness towards others is admirable.
• The concept of time is difficult to grasp.

In each of these sentences, the abstract noun is underlined. Can you identify them? The answers are:

Usage Exercises

Usage exercises are a bit more challenging. In these exercises, you’ll be asked to use abstract nouns in your own sentences. Here are a few examples:

• Write a sentence using the abstract noun “love”.
• Write a sentence using the abstract noun “happiness”.
• Write a sentence using the abstract noun “freedom”.

Here are some possible answers:

• Our love for each other grows stronger every day.
• Her happiness was contagious and spread to everyone around her.
• Freedom is a fundamental right that should be protected at all costs.

Practice exercises are a great way to improve your understanding of abstract nouns. Make sure to keep practicing until you feel confident in your ability to identify and use abstract nouns correctly.

Frequently Asked Questions

What are some common examples of abstract nouns in English?

There are many examples of abstract nouns in English, including love, courage, intelligence, creativity, communication, development, importance, and many more. Abstract nouns are words that describe intangible concepts or ideas that cannot be seen, touched, or heard.

How can abstract nouns be formed?

Abstract nouns can be formed in several ways. One common way is to add a suffix to a verb, such as -tion, -ment, -ness, -ity, or -ance. For example, the verb “create” can be turned into the abstract noun “creativity” by adding the suffix -ity. Another way to form abstract nouns is by converting adjectives into abstract nouns, such as “beauty” from “beautiful” or “happiness” from “happy”.

Is the word ’emotion’ considered an abstract noun?

Yes, the word ’emotion’ is considered an abstract noun. Emotion is an intangible concept that cannot be seen or touched. It is a feeling or state of mind that is often associated with specific physical sensations , but is not itself a physical object. Other examples of abstract nouns that are related to emotions include love, happiness, sadness, and anger.

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What’s An Abstract Noun, And How Do You Use Them?

Table of Contents

You probably already know that a noun is a word that denotes a person, place, thing, or idea—this is a concept we learn relatively early in school. Needless to say, there are several different types of nouns that we use to depict everything we experience during our life: We eat food. We meet friends. We go to w ork. These types of nouns refer to the people and physical objects that we interact with.

So what about the things that we can’t physically see or touch? Aren’t words like victory, joy , and alliance nouns, too? Yes, they are. There is a term you may or may not remember from your grade-school days that we use to refer to such a thing: the abstract noun.

Related: Abstract vs. Concrete Language

What’s an Abstract Noun?

An abstract noun is “a noun that refers to something immaterial or abstract.” Another prevalent way to think about abstract nouns is that they refer to things you can’t experience with the five senses . You can’t touch, see, hear, smell, or taste, abstract nouns. Abstract nouns refer to intangible things, something that doesn’t exist as a physical object.

For instance, the word puppy refers to a cute animal. You can see and touch a puppy . The noun puppy is not an abstract noun. Oppositely, the word luck refers to a compound idea about how likely it is that good or bad events are going to occur to a person. Luck does not exist as a physical object; you can’t smell luck nor can you go to a store and buy it. Luck is an abstract noun because it denotes an immaterial concept rather than a physical object that we can experience with our senses.

Abstract Noun Examples

Unlike most other types of nouns, abstract nouns don’t refer to people or places. People and places are real things that do exist in the world. Even nouns that refer to fictional characters or places, such as King Kong or Neverland , are not, as reasoning goes, abstract nouns because these things would have a physical form if they were, in fact, real.

So, all abstract nouns are “things.” You must remember, though, that an abstract noun only refers to something intangible like emotions, concepts, ideas, and philosophies. Let’s stop being abstract and take a look at some distinct examples so we can better understand them.

Although we usually will say that we “feel” emotions, we don’t mean it literally. You “feel” emotions like sadness or anger as a thought in your mind or an activity in your brain and body. You can’t hold sadness in your hand or eat a bowl of happiness. You can see people or animals expressing those emotions through actions, but emotions are intangible objects. So this is why we refer to them with abstract nouns.

• Examples: anger, sadness, surprise, disgust, joy, fear, happiness, anxiety, hope, confusion, relief

Ideas, concepts, and beliefs

Apart from emotions, they are also utilized to refer to other concepts and beliefs. This kind of abstract nouns gives names to complex topics and gives us a glimpse into a big piece of what makes us human—our brains! While abstract nouns are mostly common nouns, meaning that they refer to a general idea, they can also be proper nouns, like Christianity.  

• Examples: Christianity, Islam, Cubism, government, dedication, cruelty, justice

A List of Abstract Nouns

To better understand, let’s look at a whole bunch more of them:

• Stoicism, Marxism, religion, science, magnetism, creativity, invisibility, kindness, greed, laziness, effort, time, speed, concentration, confusion, dizziness, situation, existence, death, anarchy, law, democracy, relief, hopelessness, defeat, opportunity, technology, discovery, friendship, patience, decay, holiness, youth, childhood, experimentation, research

Difference Between an Abstract and Concrete Nouns

Grasping what abstract nouns are, exactly, can be difficult. While abstract nouns refer to things that are not tangible and without a physical form, all of the people, places, and things that actually do have a physical form are referred to by a certain kind of noun: a concrete noun. Unlike an abstract noun, concrete nouns actually can be experienced with all five senses: they can take a physical form rather than an image, say, in your mind’s eye of catness. You can smell a flower . You can touch a lamp. You can eat an apple. You can hear an alarm. You can see a hillside.  

So, your five senses can help you differentiate between abstract and concrete nouns. Keep in mind, words for fictional people, places, and things are deemed concrete nouns even though they don’t actually exist in our world. You might never be able to smell a zombie in everyday life, but you could if it were real—just remember to run if you ever do see one!

Related: Inanimate Object and Expressive Writing

Now let’s put your noun knowledge to the test with some sample sentences. Read every sentence and see if you can decide if each italicized noun is either abstract or concrete.

• Billionaire Elon Musk is famous for his wealth.
• Next month, we are going on vacation to Paris.
• When I grow up, I wish to be a superhero.
• She said that he’s possessed by a ghost.
• This robot has many impressive abilities.
• His blindness didn’t stop him from being successful.
• She was attacked by a swarm of bees.
• She sells seashells by the seashore.
• They heard shouting from across the street.
•  That girl only wants attention from her parents.

Answers: 1. Abstract 2. Concrete 3. Concrete 4. Concrete 5. Abstract 6. Abstract 7. Concrete 8. Concrete 9. Concrete 10. Abstract

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Article contents

Abstract nouns in the romance languages.

• Philipp Burdy Philipp Burdy Institute for Romance Studies, University of Bamberg
• https://doi.org/10.1093/acrefore/9780199384655.013.459
• Published online: 21 December 2022

Abstract words such as Fr. livraison ‘delivery’, It. fedeltà ‘faithfulness’, Sp. semejanza ‘resemblance’, belong to the word class of nouns. They do not possess materiality and therefore lack sensory perceivability. Within the spectrum of nouns, abstract nouns are located on the opposite side of proper names; between them, there are common nouns, collective nouns, and mass nouns. Abstract nouns are in part non-count and not able to be pluralized.

In terms of meaning, there is typically a threefold division in groups: (a) Action/result nouns (e.g., Fr. lavage ‘washing’, It. giuramento ‘oath’, Sp. mordedura ‘bite’); (b) Quality nouns (e.g., Fr. dignité ‘dignity’, It. biancore ‘whiteness’, Sp. modestia ‘modesty’); and (c) Status nouns (e.g., Fr. episcopat ‘episcopate’, It. cuginanza ‘cousinhood’, Sp. almirantazgo ‘admiralship’). From a purely morphological standpoint, a classification of abstract nouns according to derivation basis appears suitable: (a) (primary) denominal abstract nouns (e.g., Fr. duché ‘dukedom’, It. linguaggio ‘language’, Sp. añada ‘vintage’); (b) (primary) deadjectival abstract nouns (e.g., Fr. folie ‘madness’, It. bellezza ‘beauty’, Sp. cortesía ‘courtesy’); and (c) (primary) deverbal abstract nouns (e.g., Fr. mouvement ‘movement’, It. scrittura ‘writing’, Sp. venganza ‘revenge’). Other abstract nouns arise from conversion, for example, Fr. le devoir ‘duty’, It. il freddo ‘coldness’, Sp. el cambio ‘change’.

In light of this, the question of how far the formation of abstract nouns in Romance languages follows Latin patterns (derivation with suffixes) or whether new processes emerge is of particular interest. In addition, the individual Romance languages display different preferences in choosing abstract-forming morphological processes. On the one hand, there is a large number of Latin abstract-forming suffixes whose outcomes preserve the same function in the Romance languages, such as -ía ( astrología ‘astrology’), -ura ( scriptura ‘writing’), -ĭtia ( pigrĭtia ‘sloth’), -io ( oratio ‘speaking’). Furthermore, there is a group of Latin suffixes that gave rise to suffixes deriving abstract nouns only in Romance. Among these are, for example, -aticu (Fr. péage ‘road toll’, Sp. hallazgo ‘discovery’), -aceu (Sp. cuchillazo ‘knife thrust’), -aria (Sp. borrachera ‘drunkenness’, It. vecchiaia ‘old age’). On the other hand, suffixless processes of abstract noun formation are coming to full fruition only in Romance: The conversion of past participles (e.g., Fr. vue ‘sight’, It. dormita ‘sleep’, Sp. llegada ‘arrival’) is of special importance. The conversion of infinitives to nouns with abstract meaning is least common in Modern French (e.g., penser ‘thought’) and most common in Romanian ( iertare ‘pardon’, durere ‘pain’, etc.). Deverbal noun formation without suffixes (Fr. amende ‘fine’, It. carica ‘charge’, Sp. socorro ‘help’, etc.), in contrast, is known to have developed a broad pan-Romance geographic spread.

• Romance languages
• word formation
• abstract nouns
• suffixation

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• How to Write an Abstract | Steps & Examples

How to Write an Abstract | Steps & Examples

Published on February 28, 2019 by Shona McCombes . Revised on July 18, 2023 by Eoghan Ryan.

An abstract is a short summary of a longer work (such as a thesis ,  dissertation or research paper ). The abstract concisely reports the aims and outcomes of your research, so that readers know exactly what your paper is about.

Although the structure may vary slightly depending on your discipline, your abstract should describe the purpose of your work, the methods you’ve used, and the conclusions you’ve drawn.

One common way to structure your abstract is to use the IMRaD structure. This stands for:

• Introduction

Abstracts are usually around 100–300 words, but there’s often a strict word limit, so make sure to check the relevant requirements.

In a dissertation or thesis , include the abstract on a separate page, after the title page and acknowledgements but before the table of contents .

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Table of contents

Abstract example, when to write an abstract, step 1: introduction, step 2: methods, step 3: results, step 4: discussion, tips for writing an abstract, other interesting articles, frequently asked questions about abstracts.

Hover over the different parts of the abstract to see how it is constructed.

This paper examines the role of silent movies as a mode of shared experience in the US during the early twentieth century. At this time, high immigration rates resulted in a significant percentage of non-English-speaking citizens. These immigrants faced numerous economic and social obstacles, including exclusion from public entertainment and modes of discourse (newspapers, theater, radio).

Incorporating evidence from reviews, personal correspondence, and diaries, this study demonstrates that silent films were an affordable and inclusive source of entertainment. It argues for the accessible economic and representational nature of early cinema. These concerns are particularly evident in the low price of admission and in the democratic nature of the actors’ exaggerated gestures, which allowed the plots and action to be easily grasped by a diverse audience despite language barriers.

Keywords: silent movies, immigration, public discourse, entertainment, early cinema, language barriers.

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You will almost always have to include an abstract when:

• Completing a thesis or dissertation
• Submitting a research paper to an academic journal
• Writing a book or research proposal
• Applying for research grants

It’s easiest to write your abstract last, right before the proofreading stage, because it’s a summary of the work you’ve already done. Your abstract should:

• Be a self-contained text, not an excerpt from your paper
• Be fully understandable on its own
• Reflect the structure of your larger work

Start by clearly defining the purpose of your research. What practical or theoretical problem does the research respond to, or what research question did you aim to answer?

You can include some brief context on the social or academic relevance of your dissertation topic , but don’t go into detailed background information. If your abstract uses specialized terms that would be unfamiliar to the average academic reader or that have various different meanings, give a concise definition.

After identifying the problem, state the objective of your research. Use verbs like “investigate,” “test,” “analyze,” or “evaluate” to describe exactly what you set out to do.

This part of the abstract can be written in the present or past simple tense  but should never refer to the future, as the research is already complete.

• This study will investigate the relationship between coffee consumption and productivity.
• This study investigates the relationship between coffee consumption and productivity.

Next, indicate the research methods that you used to answer your question. This part should be a straightforward description of what you did in one or two sentences. It is usually written in the past simple tense, as it refers to completed actions.

• Structured interviews will be conducted with 25 participants.
• Structured interviews were conducted with 25 participants.

Don’t evaluate validity or obstacles here — the goal is not to give an account of the methodology’s strengths and weaknesses, but to give the reader a quick insight into the overall approach and procedures you used.

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Next, summarize the main research results . This part of the abstract can be in the present or past simple tense.

• Our analysis has shown a strong correlation between coffee consumption and productivity.
• Our analysis shows a strong correlation between coffee consumption and productivity.
• Our analysis showed a strong correlation between coffee consumption and productivity.

Depending on how long and complex your research is, you may not be able to include all results here. Try to highlight only the most important findings that will allow the reader to understand your conclusions.

Finally, you should discuss the main conclusions of your research : what is your answer to the problem or question? The reader should finish with a clear understanding of the central point that your research has proved or argued. Conclusions are usually written in the present simple tense.

• We concluded that coffee consumption increases productivity.
• We conclude that coffee consumption increases productivity.

If there are important limitations to your research (for example, related to your sample size or methods), you should mention them briefly in the abstract. This allows the reader to accurately assess the credibility and generalizability of your research.

If your aim was to solve a practical problem, your discussion might include recommendations for implementation. If relevant, you can briefly make suggestions for further research.

If your paper will be published, you might have to add a list of keywords at the end of the abstract. These keywords should reference the most important elements of the research to help potential readers find your paper during their own literature searches.

Be aware that some publication manuals, such as APA Style , have specific formatting requirements for these keywords.

It can be a real challenge to condense your whole work into just a couple of hundred words, but the abstract will be the first (and sometimes only) part that people read, so it’s important to get it right. These strategies can help you get started.

Read other abstracts

The best way to learn the conventions of writing an abstract in your discipline is to read other people’s. You probably already read lots of journal article abstracts while conducting your literature review —try using them as a framework for structure and style.

You can also find lots of dissertation abstract examples in thesis and dissertation databases .

Reverse outline

Not all abstracts will contain precisely the same elements. For longer works, you can write your abstract through a process of reverse outlining.

For each chapter or section, list keywords and draft one to two sentences that summarize the central point or argument. This will give you a framework of your abstract’s structure. Next, revise the sentences to make connections and show how the argument develops.

Write clearly and concisely

A good abstract is short but impactful, so make sure every word counts. Each sentence should clearly communicate one main point.

To keep your abstract or summary short and clear:

• Avoid passive sentences: Passive constructions are often unnecessarily long. You can easily make them shorter and clearer by using the active voice.
• Avoid long sentences: Substitute longer expressions for concise expressions or single words (e.g., “In order to” for “To”).
• Avoid obscure jargon: The abstract should be understandable to readers who are not familiar with your topic.
• Avoid repetition and filler words: Replace nouns with pronouns when possible and eliminate unnecessary words.
• Avoid detailed descriptions: An abstract is not expected to provide detailed definitions, background information, or discussions of other scholars’ work. Instead, include this information in the body of your thesis or paper.

If you’re struggling to edit down to the required length, you can get help from expert editors with Scribbr’s professional proofreading services or use the paraphrasing tool .

Check your formatting

If you are writing a thesis or dissertation or submitting to a journal, there are often specific formatting requirements for the abstract—make sure to check the guidelines and format your work correctly. For APA research papers you can follow the APA abstract format .

Checklist: Abstract

The word count is within the required length, or a maximum of one page.

The abstract appears after the title page and acknowledgements and before the table of contents .

I have clearly stated my research problem and objectives.

I have briefly described my methodology .

I have summarized the most important results .

I have stated my main conclusions .

I have mentioned any important limitations and recommendations.

The abstract can be understood by someone without prior knowledge of the topic.

You've written a great abstract! Use the other checklists to continue improving your thesis or dissertation.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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An abstract is a concise summary of an academic text (such as a journal article or dissertation ). It serves two main purposes:

• To help potential readers determine the relevance of your paper for their own research.
• To communicate your key findings to those who don’t have time to read the whole paper.

Abstracts are often indexed along with keywords on academic databases, so they make your work more easily findable. Since the abstract is the first thing any reader sees, it’s important that it clearly and accurately summarizes the contents of your paper.

An abstract for a thesis or dissertation is usually around 200–300 words. There’s often a strict word limit, so make sure to check your university’s requirements.

The abstract is the very last thing you write. You should only write it after your research is complete, so that you can accurately summarize the entirety of your thesis , dissertation or research paper .

Avoid citing sources in your abstract . There are two reasons for this:

• The abstract should focus on your original research, not on the work of others.
• The abstract should be self-contained and fully understandable without reference to other sources.

There are some circumstances where you might need to mention other sources in an abstract: for example, if your research responds directly to another study or focuses on the work of a single theorist. In general, though, don’t include citations unless absolutely necessary.

The abstract appears on its own page in the thesis or dissertation , after the title page and acknowledgements but before the table of contents .

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• Nouns and pronouns
• Abstract Noun | Definition, Examples & Worksheet

Abstract Noun | Definition, Examples & Worksheet

Published on 25 February 2023 by Jack Caulfield . Revised on 18 April 2023.

An abstract noun is a noun that refers to something non-physical – something conceptual that you can’t perceive directly with your senses. Examples include ‘sadness’, ‘analysis’, ‘government’, and ‘adulthood’.

Abstract nouns are contrasted with concrete nouns , which are words like ‘cat’, ‘desk’, or ‘Andrew’ that refer to physical objects and entities.

The passage of time isn’t easy to perceive.

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Table of contents

Abstract nouns vs concrete nouns, abstract noun examples, formation of abstract nouns, worksheet: concrete vs abstract nouns, other interesting language articles, frequently asked questions.

Abstract nouns differ from concrete nouns in terms of what they describe:

• Abstract nouns refer to anything that isn’t directly observable. That could mean personal qualities, measurements of time, cultural movements, or concepts.
• Concrete nouns refer to what can be perceived with the senses: things, people, animals, and places.

The same word could often be interpreted as abstract or concrete depending on your perspective and on the context in which it is used. The distinction is often very subjective.

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Abstract nouns represent a wide variety of things – anything that isn’t represented by a concrete noun, in fact. The table below explores a few different categories of things that abstract nouns can refer to.

A lot (though not all) of the examples given in the previous section followed a few specific patterns in terms of the suffixes they ended with (e.g., ‘-ness’, ‘-ism’).

This is because abstract nouns are formed from adjectives , verbs , and other nouns in a number of standard ways. Common ways of forming abstract nouns are shown in the table below.

Want to test your understanding of the difference between concrete and abstract nouns? Try the worksheet below. Just decide whether each highlighted noun is concrete or abstract .

• Practice questions
• Answers and explanations
• The dog seemed to enjoy its dinner .
• The price of adhering to one’s principles can be high.
• The name of my cat is Whiskers .
• The foundations of the house have begun to sink due to a lack of maintenance .
• My neighbour John has some questionable ideas about politics .
• Both ‘dog’ and ‘dinner’ are concrete nouns , since they represent physical entities in the world.
• ‘Price’ and ‘ principles ‘ are both abstract nouns because you can’t touch or see a principle or a price (although you might see something representing a price, so a noun like ‘price tag’ would be considered concrete).
• The concept of a name is abstract. ‘Cat’ is a concrete noun because a cat is a physical being. ‘Whiskers’ is concrete whether you take it to mean the speaker’s cat or simply the word ‘Whiskers’ in its use as a name – both of these can be perceived with the senses.
• ‘Foundations’ and ‘house’ both represent specific physical things and are therefore concrete nouns. ‘Lack’ and ‘maintenance’ are both more conceptual and are therefore abstract.
• Both the common noun ‘neighbour’ and the proper noun ‘John’ (here used as an appositive ) are concrete nouns, since they refer to people. ‘Ideas’ and ‘politics’ are both abstract because they refer to concepts rather than physical things.

If you want to know more about commonly confused words, definitions, common mistakes, and differences between US and UK spellings, make sure to check out some of our other language articles with explanations, examples, and quizzes.

Nouns & pronouns

• Common nouns
• Proper nouns
• Collective nouns
• Personal pronouns
• Uncountable and countable nouns
• Verb tenses
• Phrasal verbs
• Sentence structure
• Active vs passive voice
• Subject-verb agreement
• Interjections
• Determiners
• Prepositions

There are many ways to categorize nouns into various types, and the same noun can fall into multiple categories or even change types depending on context.

Some of the main types of nouns are:

• Common nouns and proper nouns
• Countable and uncountable nouns
• Concrete and abstract nouns
• Possessive nouns
• Attributive nouns
• Appositive nouns
• Generic nouns

An abstract noun is a noun describing something that can’t be directly perceived with the senses .

Abstract nouns may refer to general or philosophical concepts (e.g., “art,” “democracy,” “evidence”), emotions and personal qualities (e.g., “happiness,” “impatience”), time measurements (e.g., “hours,” “January”), or states of being (e.g., “solidity,” “instability”).

Abstract nouns are the opposite of concrete nouns , which refer to physical things that can be perceived with the senses: objects, substances, places, people and animals, and so on. For example, “window,” “Dorian,” and “sand.”

A concrete noun is a noun describing a physical entity that can be perceived with the senses . Concrete nouns may refer to things (e.g., “phone,” “hat”), places (e.g., “France,” “the post office”), or people and animals (e.g., “dog,” “doctor,” “Jamal”).

Concrete nouns are contrasted with abstract nouns , which refer to things that can’t be directly perceived—ideas, theories, concepts, and so on. Examples include “happiness,” “condemnation,” “ethics,” and “time.”

Sources for this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

Caulfield, J. (2023, April 18). Abstract Noun | Definition, Examples & Worksheet. Scribbr. Retrieved 25 March 2024, from https://www.scribbr.co.uk/nouns/abstract-nouns/

Aarts, B. (2011). Oxford modern English grammar . Oxford University Press.

Butterfield, J. (Ed.). (2015). Fowler’s dictionary of modern English usage (4th ed.). Oxford University Press.

Garner, B. A. (2016). Garner’s modern English usage (4th ed.). Oxford University Press.

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Writing an Abstract for Your Research Paper

Definition and Purpose of Abstracts

An abstract is a short summary of your (published or unpublished) research paper, usually about a paragraph (c. 6-7 sentences, 150-250 words) long. A well-written abstract serves multiple purposes:

• an abstract lets readers get the gist or essence of your paper or article quickly, in order to decide whether to read the full paper;
• an abstract prepares readers to follow the detailed information, analyses, and arguments in your full paper;
• and, later, an abstract helps readers remember key points from your paper.

It’s also worth remembering that search engines and bibliographic databases use abstracts, as well as the title, to identify key terms for indexing your published paper. So what you include in your abstract and in your title are crucial for helping other researchers find your paper or article.

If you are writing an abstract for a course paper, your professor may give you specific guidelines for what to include and how to organize your abstract. Similarly, academic journals often have specific requirements for abstracts. So in addition to following the advice on this page, you should be sure to look for and follow any guidelines from the course or journal you’re writing for.

The Contents of an Abstract

Abstracts contain most of the following kinds of information in brief form. The body of your paper will, of course, develop and explain these ideas much more fully. As you will see in the samples below, the proportion of your abstract that you devote to each kind of information—and the sequence of that information—will vary, depending on the nature and genre of the paper that you are summarizing in your abstract. And in some cases, some of this information is implied, rather than stated explicitly. The Publication Manual of the American Psychological Association , which is widely used in the social sciences, gives specific guidelines for what to include in the abstract for different kinds of papers—for empirical studies, literature reviews or meta-analyses, theoretical papers, methodological papers, and case studies.

Here are the typical kinds of information found in most abstracts:

• the context or background information for your research; the general topic under study; the specific topic of your research
• the central questions or statement of the problem your research addresses
• what’s already known about this question, what previous research has done or shown
• the main reason(s) , the exigency, the rationale , the goals for your research—Why is it important to address these questions? Are you, for example, examining a new topic? Why is that topic worth examining? Are you filling a gap in previous research? Applying new methods to take a fresh look at existing ideas or data? Resolving a dispute within the literature in your field? . . .
• your research and/or analytical methods
• your main findings , results , or arguments
• the significance or implications of your findings or arguments.

Your abstract should be intelligible on its own, without a reader’s having to read your entire paper. And in an abstract, you usually do not cite references—most of your abstract will describe what you have studied in your research and what you have found and what you argue in your paper. In the body of your paper, you will cite the specific literature that informs your research.

When to Write Your Abstract

Although you might be tempted to write your abstract first because it will appear as the very first part of your paper, it’s a good idea to wait to write your abstract until after you’ve drafted your full paper, so that you know what you’re summarizing.

What follows are some sample abstracts in published papers or articles, all written by faculty at UW-Madison who come from a variety of disciplines. We have annotated these samples to help you see the work that these authors are doing within their abstracts.

Choosing Verb Tenses within Your Abstract

The social science sample (Sample 1) below uses the present tense to describe general facts and interpretations that have been and are currently true, including the prevailing explanation for the social phenomenon under study. That abstract also uses the present tense to describe the methods, the findings, the arguments, and the implications of the findings from their new research study. The authors use the past tense to describe previous research.

The humanities sample (Sample 2) below uses the past tense to describe completed events in the past (the texts created in the pulp fiction industry in the 1970s and 80s) and uses the present tense to describe what is happening in those texts, to explain the significance or meaning of those texts, and to describe the arguments presented in the article.

The science samples (Samples 3 and 4) below use the past tense to describe what previous research studies have done and the research the authors have conducted, the methods they have followed, and what they have found. In their rationale or justification for their research (what remains to be done), they use the present tense. They also use the present tense to introduce their study (in Sample 3, “Here we report . . .”) and to explain the significance of their study (In Sample 3, This reprogramming . . . “provides a scalable cell source for. . .”).

Sample Abstract 1

From the social sciences.

Reporting new findings about the reasons for increasing economic homogamy among spouses

Gonalons-Pons, Pilar, and Christine R. Schwartz. “Trends in Economic Homogamy: Changes in Assortative Mating or the Division of Labor in Marriage?” Demography , vol. 54, no. 3, 2017, pp. 985-1005.

Sample Abstract 2

From the humanities.

Analyzing underground pulp fiction publications in Tanzania, this article makes an argument about the cultural significance of those publications

Emily Callaci. “Street Textuality: Socialism, Masculinity, and Urban Belonging in Tanzania’s Pulp Fiction Publishing Industry, 1975-1985.” Comparative Studies in Society and History , vol. 59, no. 1, 2017, pp. 183-210.

Sample Abstract/Summary 3

From the sciences.

Reporting a new method for reprogramming adult mouse fibroblasts into induced cardiac progenitor cells

Lalit, Pratik A., Max R. Salick, Daryl O. Nelson, Jayne M. Squirrell, Christina M. Shafer, Neel G. Patel, Imaan Saeed, Eric G. Schmuck, Yogananda S. Markandeya, Rachel Wong, Martin R. Lea, Kevin W. Eliceiri, Timothy A. Hacker, Wendy C. Crone, Michael Kyba, Daniel J. Garry, Ron Stewart, James A. Thomson, Karen M. Downs, Gary E. Lyons, and Timothy J. Kamp. “Lineage Reprogramming of Fibroblasts into Proliferative Induced Cardiac Progenitor Cells by Defined Factors.” Cell Stem Cell , vol. 18, 2016, pp. 354-367.

Sample Abstract 4, a Structured Abstract

Reporting results about the effectiveness of antibiotic therapy in managing acute bacterial sinusitis, from a rigorously controlled study

Note: This journal requires authors to organize their abstract into four specific sections, with strict word limits. Because the headings for this structured abstract are self-explanatory, we have chosen not to add annotations to this sample abstract.

Wald, Ellen R., David Nash, and Jens Eickhoff. “Effectiveness of Amoxicillin/Clavulanate Potassium in the Treatment of Acute Bacterial Sinusitis in Children.” Pediatrics , vol. 124, no. 1, 2009, pp. 9-15.

“OBJECTIVE: The role of antibiotic therapy in managing acute bacterial sinusitis (ABS) in children is controversial. The purpose of this study was to determine the effectiveness of high-dose amoxicillin/potassium clavulanate in the treatment of children diagnosed with ABS.

METHODS : This was a randomized, double-blind, placebo-controlled study. Children 1 to 10 years of age with a clinical presentation compatible with ABS were eligible for participation. Patients were stratified according to age (<6 or ≥6 years) and clinical severity and randomly assigned to receive either amoxicillin (90 mg/kg) with potassium clavulanate (6.4 mg/kg) or placebo. A symptom survey was performed on days 0, 1, 2, 3, 5, 7, 10, 20, and 30. Patients were examined on day 14. Children’s conditions were rated as cured, improved, or failed according to scoring rules.

RESULTS: Two thousand one hundred thirty-five children with respiratory complaints were screened for enrollment; 139 (6.5%) had ABS. Fifty-eight patients were enrolled, and 56 were randomly assigned. The mean age was 6630 months. Fifty (89%) patients presented with persistent symptoms, and 6 (11%) presented with nonpersistent symptoms. In 24 (43%) children, the illness was classified as mild, whereas in the remaining 32 (57%) children it was severe. Of the 28 children who received the antibiotic, 14 (50%) were cured, 4 (14%) were improved, 4(14%) experienced treatment failure, and 6 (21%) withdrew. Of the 28children who received placebo, 4 (14%) were cured, 5 (18%) improved, and 19 (68%) experienced treatment failure. Children receiving the antibiotic were more likely to be cured (50% vs 14%) and less likely to have treatment failure (14% vs 68%) than children receiving the placebo.

CONCLUSIONS : ABS is a common complication of viral upper respiratory infections. Amoxicillin/potassium clavulanate results in significantly more cures and fewer failures than placebo, according to parental report of time to resolution.” (9)

Some Excellent Advice about Writing Abstracts for Basic Science Research Papers, by Professor Adriano Aguzzi from the Institute of Neuropathology at the University of Zurich:

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Topic modeling and BookNLP: Seeking the emotional turn in the history of eighteenth-century English fiction

Image of the first edition of "Robinson Crusoe" with data visualizations by Professor Hui.

Join us on April 2nd at between 12 and 1:30 pm for a paper presentation titled " Topic modeling and BookNLP: Seeking the emotional turn in the history of eighteenth-century English fiction" on topics modelling, emotions, and data visualizations by Professor Haifeng Hui from School of Foreign Languages, Huazhong University of Science and Technology, China. Professor Hui is also currently a Visiting Scholar at Center for East Asian Studies at Stanford, and an affiliated researcher with CESTA. Lunch will be served for in person participants. If you cannot make it in person, we will have a Zoom option available as well. RSVP via this link to receive the Zoom link or to make sure that we have enough food.

Dr. Haifeng Hui (惠海峰 ) is Professor of English at the School of Foreign Languages, Huazhong University of Science and Technology, China. He researches children’s literature from diverse theoretical perspectives, including narratology, stylistics, adaptation studies, and digital humanities. He serves as an Advisor Board member of International Research in Children's Literature, and an editor of Jeunesse: Young People, Texts, Cultures. He received his B.A. (2003), M.A. (2006) and Ph.D. (2012) from Peking University. He is also a visiting scholar at University of California at Los Angeles (2014-2015). Haifeng’s recent publications include Adaptation of British Literary Classics for Children (Peking University Press, 2019), “Canon Studies in China: Traditions, Modernization and Revisions in the Global Context,” Poetics Today (2021), “Embedded Mental States in Percy Jackson and the Lightning Thief and Uneven Distribution of Narratorial Attention,” Orbis Litterarrum (2023), “What Can Digital Humanities Do for Literary Adaptation Studies: Distant Reading of Children's Editions of Robinson Crusoe,” Digital Scholarship in the Humanities (2023).

Abstract of the Paper

While topic modeling has been widely used in NLP tasks, its application to literary texts has encountered challenges and dilemmas. In this seminar, I will begin by conducting topic modeling on a single novel, Robinson Crusoe , as a case study to demonstrate how we can use topic modeling for fiction analysis with the help of word2vec to differentiate different topic words in space, which makes it easier to interpret their significances. The result is further validated by readings of different editions of the novel through topic modeling. In the next step, I will apply topic modeling to the history of eighteenth-century English fiction, where I have found an interesting phenomenon of the topic of 'cry'. Following this clue, I have studied the evolution of emotional expression in English novels in the 18th century. By using BookNLP, I extract the usage of nouns and verbs related to emotions and feelings over the course of time, and explore how this trend is influenced by gender. The digital evidence amassed in this research contributes to understanding issues concerning the emergence of sentimentalism and emotions in literary works.

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• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

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

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

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• Chemical engineering
• 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).

Author information

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

Authors and Affiliations

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

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

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

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

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

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

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

Łukasz Kreft & Alexander Botzki

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

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

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Correspondence to Kevin J. Verstrepen .

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

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

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Received : 30 October 2023

Accepted : 21 February 2024

Published : 26 March 2024

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

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