proofreading a paper meaning

• Manuscript Review

What is the main purpose of proofreading a paper?

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It’s commonplace to feel nervous about submitting your scientific work. Whether you’re concerned about meeting research reviewers’ high expectations and/or the target journals’ guidelines. Proofreading is the final stage before a manuscript leaves your hands and enters the expanding universe of appraisal for publication. So, it makes sense that you want to deliver a perfectly written document, and avoid embarrassing mistakes.

Most of us simply have either friends or colleagues proofread our work, but they may have their own limitations regarding proficiency in text writing. Instead, it’s highly advised that you look for professional help at this important stage.

The main purpose of proofreading is to improve the quality of the paper, ensuring there are no lingering mistakes, and correcting generalized discourse errors or writing inconsistencies. Essentially, you want to make sure you have a well-defined communication goal. Analyzing whether the content is properly conveyed, and the sentences are syntactically and grammatically well-written, are just two of the basic tasks to achieve publication-ready work. Specifically, a perfect manuscript, ready to be published in the most recognized scientific journals.

What is proofreading

Proofreading is the last writing process before the author submits the article for publication. It is the stage of verification, by the author him or herself, or by another person. Thus, it is not only important to check grammar and spelling, it is also highly advised to ensure that the idea of the writer/author is in agreement with what he or she wants to communicate with the audience. In other words, that the article/work is clearly written for its intended target audience.

Proofreading Vs. Editing

Paper proofreader.

How often have you conducted high-quality research, but the article about that research didn’t match the quality of the research itself? How many times have you regretted missing a mistake that ultimately led to a failed submission?

Proofreading ensures flawless content for publication, increasing your chances of success. An excellent paper proofreader checks all digital sources related to the document, including websites, email addresses, etc.

A good paper proofreader is someone who will take care of your work as if it were his or her own and, in addition to correcting grammar errors, also detects the possibility of scientific plagiarism. Proofreading your scientific article using scientific editing will save you from the humiliation of having your article rejected by scientific journals due to grammatical errors or inadequate language.

Why is proofreading important?

Effective proofreading is absolutely essential for producing high-quality documents, whether academic or professional. When done clearly, correctly and thoroughly, proofreading can be the difference between writing something that communicates as it is supposed to or a huge misunderstanding. It can also be the difference between acceptance and rejection in a distinguished journal. No author creates an excellent text without reviewing, reflecting, and revising – or trusting someone to do so – before the final version of their manuscript is complete and submitted.

Language and text reviewing are important to detect:

• Grammar mistakes and numbering errors – e.g. forms of numbers, short and scientific forms, degrees of comparison, etc.
• Spelling mistakes – simple misspellings, or incorrect use of a homonym (words that sound alike, but have different meanings, e.g. “read,” for “red”), typographical error, etc.
• Inconsistency in the document format – this can be simple font, spacing and justification rules, or standard format for the applicable research sub-type (e.g. research review versus experiment)
• Punctuation errors – missing or extra commas, periods, and/or quotation marks used incorrectly
• Misplaced words – correct word choice improves the quality of your content
• Poorly structured paragraphs
• Errors in sentence structure

Whatever the nature of your research, Elsevier will be glad to give you a hand in reviewing and amending your manuscript. Professional editors can proofread your document so the final product is well-written, precise, and easy to read. With Elsevier’s medical editing and proofreading services team, we can help you with grammar, syntax, spelling, and punctuation; maximizing impact, and increasing your chances of publication.

Language Editing Services by Elsevier Author Services:

Find more about our  Language Editing Standard : completion within 7 business days; editing by native speakers in (scientific) American or British English; PhD or PhD candidates selection according to your field of study and an exclusive guarantee: free re-edit or your money back.

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What is Proofreading?: A Step-by-Step Guide

Master the art of flawless content with our blog “What is Proofreading? A Step-by-Step Guide. Dive into the intricacies of the Proofreading process and learn how to enhance the quality of your writing. Elevate your communication skills by understanding the nuances of Proofreading. Uncover the secrets to error-free content creation with our comprehensive blog.

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Table of Contents  

1) Understanding “What is Proofreading?” and its aspects 

2) Importance of Proofreading 

3) Benefits of Proofreading 

4) Step-by-step guide to Proofreading 

    a) Review the content 

    b) Check for spelling and grammar errors 

    c) Verify consistency and style 

    d) Ensure proper formatting 

    e) Cross-check facts and references 

    f) Read aloud for clarity and flow 

    g) Proofread in multiple rounds 

5) Conclusion 

Understanding “ What is Proofreading ?” and its aspects  

Before we move on to the step-by-step guide, we will first define Proofreading. Proofreading is the meticulous process of reviewing written content to identify and correct errors in grammar, spelling, punctuation, formatting, and overall clarity. Understanding Proofreading is essential for anyone involved in the writing process. Let's explore the key aspects of understanding Proofreading in more detail:  

1) Thorough examination of content: Proofreading goes beyond a cursory read-through of a document. It involves a meticulous examination of the content to identify and rectify errors. This examination encompasses grammar, punctuation, spelling, formatting, consistency, style, and overall coherence. 

2) Error detection and correction: The primary goal of Proofreading is to identify and correct errors in written materials. These errors can range from simple typos and grammatical mistakes to more complex issues like inconsistent formatting or unclear sentence structure. Proofreading ensures that these errors are identified and fixed, resulting in polished and error-free content. 

3) Enhancing writing quality: Proofreading is an integral part of the writing process that contributes to improving the overall quality of the written work. By eliminating errors, Proofreading enhances the clarity, readability, and coherence of the content. It ensures that the message is effectively conveyed to the intended audience. 

4) Attention to grammar and language: Proofreading involves paying close attention to grammar and language usage. It includes checking for proper subject-verb agreement, verb tenses, correct word choice, and sentence structure. Through Proofreading, inconsistencies and errors in grammar and language are identified and rectified, ensuring that the writing adheres to the rules and conventions of the English language. 

5) Consistency and coherence: Proofreading also focuses on maintaining consistency and coherence throughout the document. This includes checking for consistent use of terminology, formatting, headings, and citation styles. By ensuring consistency, Proofreading helps to create a cohesive and unified piece of writing that is easier for readers to follow and understand. 

6) Attention to detail: Proofreading requires a keen eye for detail. It involves scrutini s ing every aspect of the writing, including punctuation, spacing, indentation, and formatting. By paying attention to these details, Proofreading helps to eliminate errors and inconsistencies that might otherwise be overlooked. 

7) Attention to style and tone: Proofreading considers the style and tone of the writing. It ensures that the content aligns with the desired style guide or guidelines. This includes checking for appropriate language usage, tone consistency, and adherence to specific writing conventions. Proofreading ensures that the writing maintains the intended style and tone, enhancing the overall effectiveness of the message. 

8) Iterative process: Proofreading is an iterative process that involves multiple rounds of review. Each round focuses on different aspects, such as grammar, spelling, formatting, consistency, or style. Through these rounds, errors are gradually eliminated, and the content is refined for optimal quality. 

9) Final check for accuracy: Proofreading serves as the final check for accuracy before the content is published or shared. It verifies the factual accuracy of information, confirms proper citations and references, and ensures the overall integrity of the content. By conducting this final accuracy check, Proofreading helps to maintain the credibility and reliability of the written work. 

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Importance of Proofreading  

Now that we know the definition of Proofreading and its aspects, let's delve into the key reasons W hy Proofreading is Important :  

1) Maintaining credibility: Proofreading ensures that your written work is free from errors, showcasing your professionalism and attention to detail. By presenting polished and accurate content, you establish credibility and trust with your readers. Whether it's an academic paper, business document, or blog post, Proofreading demonstrates your commitment to delivering high-quality work. 

2) Enhancing clarity and understanding: Clear and error-free writing is essential for effective communication. Proofreading helps eliminate grammar, spelling, and punctuation mistakes that can hinder comprehension. By ensuring your writing is clear and coherent, you help readers understand your ideas and messages accurately, avoiding any confusion or misinterpretation. 

3) Polishing the presentation: Proper Proofreading ensures that your writing adheres to the desired formatting, style, and tone. Consistency in formatting, headings, font usage, and indentation contributes to a professional and polished appearance. When your work is well-presented and consistent, it reflects positively on your writing skills and attention to detail. 

4) Quality assurance: Proofreading is a quality assurance step that helps you deliver the best possible work. It enables you to identify and rectify any weak points, inconsistencies, or areas for improvement in your writing. By striving for excellence through Proofreading, you demonstrate your commitment to producing high-quality content. 

5) Professional growth: Engaging in the Proofreading process consistently can enhance your writing skills and attention to detail. It trains you to identify and correct errors, refine your style, and develop a stronger command of language. Over time, regular Proofreading helps you become a more proficient writer and communicator. 

Benefits of Proofreading  

Proofreading is considered to be an essential step in the writing process that offers numerous benefits. Let's explore the key benefits of Proofreading in more detail: 

1) Enhanced credibility and professionalism: Proofreading ensures that your written content is free from errors, making it more credible and professional. When your work is polished and error-free, it demonstrates your attention to detail and dedication to producing high-quality materials. This, in turn, enhances your credibility and reputation as a writer or organi s ation . 

2) Improved clarity and comprehension: Proofreading helps to clarify your ideas and improve the overall coherence of your writing. By eliminating errors, awkward phrasing, and unclear sentences, you make your content easier to understand for your audience. Clear and well-structured writing enhances comprehension, allowing your readers to grasp your message more effectively. 

3) Minimi s ed m isinterpretation and confusion: Error-free writing reduces the risk of misinterpretation or confusion among your readers. When your content is clear and accurate , it ensures that your message is conveyed precisely as intended. This helps to avoid any misunderstandings and ensures that your readers correctly grasp the information or ideas you are trying to communicate. 

4) Increased professionalism in communication: Whether you are writing an academic paper, a business report, or a blog post, Proofreading adds a professional touch to your communication. It shows that you take your work seriously and have a high standard of quality. Professionally proofread content reflects positively on your image and fosters trust and respect from your readers. 

5) Improved readability and engagement: Proofreading enhances the readability of your content, making it more engaging for your audience. By eliminating errors, awkward sentences, and confusing phrases, you create a smooth and enjoyable reading experience. When your writing flows well and is free from distractions, readers are more likely to stay engaged and continue reading. 

6) Maintained brand consistency: For businesses and organi s ations , Proofreading helps to maintain brand consistency across written materials. By adhering to consistent language, style, and tone, you reinforce your brand's identity and messaging. Consistent and error-free writing contributes to a cohesive and professional brand image. 

7) Error-free facts and references: Proofreading ensures the accuracy of facts, data, quotes, and references in your content. Cross-checking and verifying information during the Proofreading process helps you to avoid spreading misinformation or using unreliable sources. Accurate and properly cited information strengthens the credibility of your work and builds trust with your readers. 

8) Reduced embarrassment and reputational damage: Proofreading is a proactive measure to prevent embarrassing mistakes or errors that can damage your reputation. Typos, grammatical errors, or factual inaccuracies can undermine your professionalism and credibility. By thoroughly Proofreading your content, you minimi s e the risk of publishing content with avoidable mistakes. 

9) Better writing habits and skills: Regularly engaging in the Proofreading process helps you develop better writing habits and skills. By paying attention to the details and identifying errors, you become more conscious of your writing style, grammar rules, and formatting conventions. Over time, this improves your overall writing proficiency and enables you to produce higher-quality content. 

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Step-by-step guide to Proofreading  

This section of the blog will present you with a step-by-step guide to Proofreading:  

Review the c ontent  

To begin the Proofreading process, start by reviewing the entire content. This step allows you to gain a comprehensive understanding of the material, its purpose, and the target audience. While reviewing, take note of any areas that require attention or improvements. 

Check for s pelling and g rammar e rrors  

Next, thoroughly check the content for spelling and grammar errors. Look out for common mistakes such as typos, misspelt words, incorrect verb forms, subject-verb agreement issues, and punctuation errors. Utili s e spelling and grammar check tools, but you must remember that they are not foolproof and should be used alongside manual review. 

Verify c onsistency and s tyle  

Consistency in writing is vital for maintaining a smooth flow and ensuring the readers' comprehension. Verify that the terminology, formatting, headings, and citation styles are consistent throughout the document. Also, adhere to the specified style guide or guidelines, such as the Oxford Style Manual or The Guardian Style Guide, to maintain a unified and professional appearance. 

Ensure p roper f ormatting  

Formatting plays a significant role in the overall presentation of the document. Pay attention to the font styles, sizes, line spacing, margins, and indentation. Ensure that the formatting is consistent throughout and adheres to the desired standards or guidelines. Well-formatted content enhances readability and gives a polished look to the document.  

Cross-check f acts and r eferences  

For content that relies on facts, data, or references, it is essential to cross-check their accuracy and reliability. Verify the information, statistics, quotes, and references against credible sources. Ensure that all facts are properly cited and attributed. This step helps maintain the credibility of the content and avoids spreading misinformation. 

Read a loud for c larity and f low  

Reading the content aloud is a valuable technique to assess its clarity, flow, and overall effectiveness. Pay attention to sentence structure, coherence between paragraphs, and the overall tone of the writing. Reading aloud helps identify awkward phrasing, unclear sentences, and areas that may require rephrasing or restructuring to enhance the reader's understanding and engagement. 

Proofread in m ultiple r ounds  

To ensure thorough Proofreading , it is recommended to go through multiple rounds of review. Each round can focus on specific aspects such as grammar, spelling, consistency, formatting, and style. Taking breaks between rounds allows you to approach the content with a fresh perspective, making it easier to spot any overlooked errors or areas for improvement. 

During each round, carefully review the content, making necessary corrections and adjustments. Pay attention to the changes made in previous rounds to ensure consistency and accuracy throughout the document. This iterative process helps refine the content and ensure its quality. 

Conclusion  

Proofreading is a critical process in the journey of producing impeccable written content. By following the step-by-step guide outlined in this blog, you can improve the accuracy, clarity, and professionalism of your writing. Remember to dedicate ample time and attention to each stage of the Proofreading process to ensure optimal results. Hope this blog answered all your questions on What is Proofreading! 

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Editing and Proofreading

What this handout is about.

This handout provides some tips and strategies for revising your writing. To give you a chance to practice proofreading, we have left seven errors (three spelling errors, two punctuation errors, and two grammatical errors) in the text of this handout. See if you can spot them!

Is editing the same thing as proofreading?

Not exactly. Although many people use the terms interchangeably, editing and proofreading are two different stages of the revision process. Both demand close and careful reading, but they focus on different aspects of the writing and employ different techniques.

Some tips that apply to both editing and proofreading

• Get some distance from the text! It’s hard to edit or proofread a paper that you’ve just finished writing—it’s still to familiar, and you tend to skip over a lot of errors. Put the paper aside for a few hours, days, or weeks. Go for a run. Take a trip to the beach. Clear your head of what you’ve written so you can take a fresh look at the paper and see what is really on the page. Better yet, give the paper to a friend—you can’t get much more distance than that. Someone who is reading the paper for the first time, comes to it with completely fresh eyes.
• Decide which medium lets you proofread most carefully. Some people like to work right at the computer, while others like to sit back with a printed copy that they can mark up as they read.
• Try changing the look of your document. Altering the size, spacing, color, or style of the text may trick your brain into thinking it’s seeing an unfamiliar document, and that can help you get a different perspective on what you’ve written.
• Find a quiet place to work. Don’t try to do your proofreading in front of the TV or while you’re chugging away on the treadmill. Find a place where you can concentrate and avoid distractions.
• If possible, do your editing and proofreading in several short blocks of time. Your concentration may start to wane if you try to proofread the entire text at one time.
• If you’re short on time, you may wish to prioritize. Make sure that you complete the most important editing and proofreading tasks.

Editing is what you begin doing as soon as you finish your first draft. You reread your draft to see, for example, whether the paper is well-organized, the transitions between paragraphs are smooth, and your evidence really backs up your argument. You can edit on several levels:

Have you done everything the assignment requires? Are the claims you make accurate? If it is required to do so, does your paper make an argument? Is the argument complete? Are all of your claims consistent? Have you supported each point with adequate evidence? Is all of the information in your paper relevant to the assignment and/or your overall writing goal? (For additional tips, see our handouts on understanding assignments and developing an argument .)

Overall structure

Does your paper have an appropriate introduction and conclusion? Is your thesis clearly stated in your introduction? Is it clear how each paragraph in the body of your paper is related to your thesis? Are the paragraphs arranged in a logical sequence? Have you made clear transitions between paragraphs? One way to check the structure of your paper is to make a reverse outline of the paper after you have written the first draft. (See our handouts on introductions , conclusions , thesis statements , and transitions .)

Structure within paragraphs

Does each paragraph have a clear topic sentence? Does each paragraph stick to one main idea? Are there any extraneous or missing sentences in any of your paragraphs? (See our handout on paragraph development .)

Have you defined any important terms that might be unclear to your reader? Is the meaning of each sentence clear? (One way to answer this question is to read your paper one sentence at a time, starting at the end and working backwards so that you will not unconsciously fill in content from previous sentences.) Is it clear what each pronoun (he, she, it, they, which, who, this, etc.) refers to? Have you chosen the proper words to express your ideas? Avoid using words you find in the thesaurus that aren’t part of your normal vocabulary; you may misuse them.

Have you used an appropriate tone (formal, informal, persuasive, etc.)? Is your use of gendered language (masculine and feminine pronouns like “he” or “she,” words like “fireman” that contain “man,” and words that some people incorrectly assume apply to only one gender—for example, some people assume “nurse” must refer to a woman) appropriate? Have you varied the length and structure of your sentences? Do you tends to use the passive voice too often? Does your writing contain a lot of unnecessary phrases like “there is,” “there are,” “due to the fact that,” etc.? Do you repeat a strong word (for example, a vivid main verb) unnecessarily? (For tips, see our handouts on style and gender-inclusive language .)

Have you appropriately cited quotes, paraphrases, and ideas you got from sources? Are your citations in the correct format? (See the UNC Libraries citation tutorial for more information.)

As you edit at all of these levels, you will usually make significant revisions to the content and wording of your paper. Keep an eye out for patterns of error; knowing what kinds of problems you tend to have will be helpful, especially if you are editing a large document like a thesis or dissertation. Once you have identified a pattern, you can develop techniques for spotting and correcting future instances of that pattern. For example, if you notice that you often discuss several distinct topics in each paragraph, you can go through your paper and underline the key words in each paragraph, then break the paragraphs up so that each one focuses on just one main idea.

Proofreading

Proofreading is the final stage of the editing process, focusing on surface errors such as misspellings and mistakes in grammar and punctuation. You should proofread only after you have finished all of your other editing revisions.

Why proofread? It’s the content that really matters, right?

Content is important. But like it or not, the way a paper looks affects the way others judge it. When you’ve worked hard to develop and present your ideas, you don’t want careless errors distracting your reader from what you have to say. It’s worth paying attention to the details that help you to make a good impression.

Most people devote only a few minutes to proofreading, hoping to catch any glaring errors that jump out from the page. But a quick and cursory reading, especially after you’ve been working long and hard on a paper, usually misses a lot. It’s better to work with a definite plan that helps you to search systematically for specific kinds of errors.

Sure, this takes a little extra time, but it pays off in the end. If you know that you have an effective way to catch errors when the paper is almost finished, you can worry less about editing while you are writing your first drafts. This makes the entire writing proccess more efficient.

Try to keep the editing and proofreading processes separate. When you are editing an early draft, you don’t want to be bothered with thinking about punctuation, grammar, and spelling. If your worrying about the spelling of a word or the placement of a comma, you’re not focusing on the more important task of developing and connecting ideas.

The proofreading process

You probably already use some of the strategies discussed below. Experiment with different tactics until you find a system that works well for you. The important thing is to make the process systematic and focused so that you catch as many errors as possible in the least amount of time.

• Don’t rely entirely on spelling checkers. These can be useful tools but they are far from foolproof. Spell checkers have a limited dictionary, so some words that show up as misspelled may really just not be in their memory. In addition, spell checkers will not catch misspellings that form another valid word. For example, if you type “your” instead of “you’re,” “to” instead of “too,” or “there” instead of “their,” the spell checker won’t catch the error.
• Grammar checkers can be even more problematic. These programs work with a limited number of rules, so they can’t identify every error and often make mistakes. They also fail to give thorough explanations to help you understand why a sentence should be revised. You may want to use a grammar checker to help you identify potential run-on sentences or too-frequent use of the passive voice, but you need to be able to evaluate the feedback it provides.
• Proofread for only one kind of error at a time. If you try to identify and revise too many things at once, you risk losing focus, and your proofreading will be less effective. It’s easier to catch grammar errors if you aren’t checking punctuation and spelling at the same time. In addition, some of the techniques that work well for spotting one kind of mistake won’t catch others.
• Read slow, and read every word. Try reading out loud , which forces you to say each word and also lets you hear how the words sound together. When you read silently or too quickly, you may skip over errors or make unconscious corrections.
• Separate the text into individual sentences. This is another technique to help you to read every sentence carefully. Simply press the return key after every period so that every line begins a new sentence. Then read each sentence separately, looking for grammar, punctuation, or spelling errors. If you’re working with a printed copy, try using an opaque object like a ruler or a piece of paper to isolate the line you’re working on.
• Circle every punctuation mark. This forces you to look at each one. As you circle, ask yourself if the punctuation is correct.
• Read the paper backwards. This technique is helpful for checking spelling. Start with the last word on the last page and work your way back to the beginning, reading each word separately. Because content, punctuation, and grammar won’t make any sense, your focus will be entirely on the spelling of each word. You can also read backwards sentence by sentence to check grammar; this will help you avoid becoming distracted by content issues.
• Proofreading is a learning process. You’re not just looking for errors that you recognize; you’re also learning to recognize and correct new errors. This is where handbooks and dictionaries come in. Keep the ones you find helpful close at hand as you proofread.
• Ignorance may be bliss, but it won’t make you a better proofreader. You’ll often find things that don’t seem quite right to you, but you may not be quite sure what’s wrong either. A word looks like it might be misspelled, but the spell checker didn’t catch it. You think you need a comma between two words, but you’re not sure why. Should you use “that” instead of “which”? If you’re not sure about something, look it up.
• The proofreading process becomes more efficient as you develop and practice a systematic strategy. You’ll learn to identify the specific areas of your own writing that need careful attention, and knowing that you have a sound method for finding errors will help you to focus more on developing your ideas while you are drafting the paper.

Think you’ve got it?

Then give it a try, if you haven’t already! This handout contains seven errors our proofreader should have caught: three spelling errors, two punctuation errors, and two grammatical errors. Try to find them, and then check a version of this page with the errors marked in red to see if you’re a proofreading star.

Works consulted

We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.

Especially for non-native speakers of English:

Ascher, Allen. 2006. Think About Editing: An ESL Guide for the Harbrace Handbooks . Boston: Wadsworth Cengage Learning.

Lane, Janet, and Ellen Lange. 2012. Writing Clearly: Grammar for Editing , 3rd ed. Boston: Heinle.

For everyone:

Einsohn, Amy. 2011. The Copyeditor’s Handbook: A Guide for Book Publishing and Corporate Communications , 3rd ed. Berkeley: University of California Press.

Lanham, Richard A. 2006. Revising Prose , 5th ed. New York: Pearson Longman.

Tarshis, Barry. 1998. How to Be Your Own Best Editor: The Toolkit for Everyone Who Writes . New York: Three Rivers Press.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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OASIS: Writing Center

Writing a paper: proofreading, introduction.

Proofreading involves reading your document to correct the smaller typographical, grammatical, and spelling errors. Proofreading is usually the very last step you take before sending off the final draft of your work for evaluation or publication. It comes after you have addressed larger matters such as style, content, citations, and organization during revising. Like revising, proofreading demands a close and careful reading of the text. Although quite tedious, it is a necessary and worthwhile exercise that ensures that your reader is not distracted by careless mistakes.

Tips for Proofreading

• Set aside the document for a few hours or even a few days before proofreading. Taking a bit of time off enables you to see the document anew. A document that might have seemed well written one day may not look the same when you review it a few days later. Taking a step back provides you with a fresh (and possibly more constructive) perspective.
• Make a conscious effort to proofread at a specific time of day (or night!) when you are most alert to spotting errors. If you are a morning person, try proofreading then. If you are a night owl, try proofreading at this time.
• Reviewing the document in a different format and having the ability to manually circle and underline errors can help you take the perspective of the reader, identifying issues that you might ordinarily miss. Additionally, a hard copy gives you a different visual format (away from your computer screen) to see the words anew.
• Although useful, programs like Word's spell-checker and Grammarly can misidentify or not catch errors. Although grammar checkers give relevant tips and recommendations, they are only helpful if you know how to apply the feedback they provide. Similarly, MS Word's spell checker may not catch words that are spelled correctly but used in the wrong context (e.g., differentiating between their, they're , and there ). Beyond that, sometimes a spell checker may mark a correct word as wrong simply because the word is not found in the spell checker's dictionary. To supplement tools such as these, be sure to use dictionaries and other grammar resources to check your work. You can also make appointments with our writing instructors for feedback concerning grammar and word choice, as well as other areas of your writing!
• Reading a text aloud allows you to identify errors that you might gloss over when reading silently. This technique is particularly useful for identifying run-on and other types of awkward sentences. If you can, read for an audience. Ask a friend or family member to listen to your work and provide feedback, checking for comprehension, organization, and flow.
• Hearing someone else read your work allows you to simply listen without having to focus on the written words yourself. You can be a more critical listener when you are engaged in only the audible words.
• By reading the document backwards, sentence by sentence, you are able to focus only on the words and sentences without paying attention to the context or content.
• Placing a ruler or a blank sheet of paper under each line as you read it will give your eyes a manageable amount of text to read.
• If you can identify one type of error that you struggle with (perhaps something that a faculty member has commented on in your previous work), go through the document and look specifically for these types of errors. Learn from your mistakes, too, by mastering the problem concept so that it does not appear in subsequent drafts.
• Related to the previous strategy of checking for familiar errors, you can proofread by focusing on one error at a time. For instance, if commas are your most frequent problem, go through the paper checking just that one problem. Then proofread again for the next most frequent problem.
• After you have finished making corrections, have someone else scan the document for errors. A different set of eyes and a mind that is detached from the writing can identify errors that you may have overlooked.
• Remember that proofreading is not just about errors. You want to polish your sentences, making them smooth, interesting, and clear. Watch for very long sentences, since they may be less clear than shorter, more direct sentences. Pay attention to the rhythm of your writing; try to use sentences of varying lengths and patterns. Look for unnecessary phrases, repetition, and awkward spots.

Download and print a copy of our proofreading bookmark to use as a reference as you write!

• Proofreading Bookmark Printable bookmark with tips on proofreading a document.

Proofreading for Grammar Video

Note that this video was created while APA 6 was the style guide edition in use. There may be some examples of writing that have not been updated to APA 7 guidelines.

• Mastering the Mechanics: Proofreading for Grammar (video transcript)

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• Writing Tips

Why Proofreading Is Important

4-minute read

• 11th February 2023

Any type of writing can benefit from proofreading. In this article, we’ll explain what proofreading can help you achieve with your work and why it’s so important.

What Is Proofreading?

Proofreading is a type of editing . It’s the process of reviewing a piece of writing for errors in grammar, punctuation, spelling, and formatting. It takes place after the writing process is complete, and it’s the last type of editing you’ll do before publication.

While earlier stages of editing might make more significant changes to the structure and content of a document, proofreading focuses on catching surface-level errors that the writer has made or that previous edits have introduced.

What Is the History of Proofreading?

Proofreading gets its name from traditional printing presses, where “galley proofs” were mockups of a printed manuscript to test how the published document would look. These “proofs” were then checked for mistakes before being used in the expensive process of printing.

Historically, proofreading was done on paper using symbols called proofing marks . While proofing marks are still in use, these days, most modern proofreading is carried out on a computer using word processing software, such as Microsoft Word .

Why Is Proofreading Important?

Proofreading is crucial to ensuring that a piece of writing is clear, accurate, and easy to understand. These qualities are essential for any document that’s going to be published or shared in some way, from novels to dissertations.

Proofreading helps written work appear professional, reliable, and credible, which is especially important in the case of academic and business writing . It can also help maintain the “ suspension of disbelief ” in works of fiction.

In addition, proofreading saves time and money by catching mistakes before they’re published, submitted, or widely distributed.

What Impact Can Errors Have?

Even the smallest mistake can have a major impact on a piece of writing. In some cases, an error in grammar, punctuation, spelling, or formatting can cause confusion and lead to misinterpretation of what the author intended to say. A missing comma, for example, can completely change the meaning of a sentence:

And the same is true of typos that confuse similar words:

Errors in a text can also:

●  Distract readers from the point being made

●  Detract from the credibility of the work

●  Make the work difficult to read and understand

●  Negatively impact an author’s reputation

This can lead to significant consequences, such as poor grades, rejection from publishers, or missed career opportunities.

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To summarize:

●  Proofreading is an essential step in the writing process that helps to ensure written work is clear, accurate, and easy to understand.

●  It’s particularly important for academic and professional writing, as errors can detract from the credibility of the work.

●  Errors can have serious consequences for an author and damage their professional reputation.

●  Proofreading can prevent confusion and save time by catching errors before they’re published.

Whether you choose to proofread your own work or use a professional, proofreading is essential to producing a good quality piece of writing.

1. What are the most common errors found during proofreading?

Some errors appear more often than others.

10 of the most common proofreading errors are:

• Incorrect apostrophe usage
• Missing commas
• Comma splices
• Sentence fragments
• Dangling and misplaced modifiers
• Confusing homophones, such as their/there/they’re, its/it’s, and to/too/two
• Faulty subject–verb agreement
• Misused sayings and idioms
• Inconsistent formatting
• General spelling errors

When proofreading your work, it’s a good idea to keep an eye out for these types of mistakes.

2. How can I proofread a large document efficiently?

It’s difficult to stay focused when looking through a long document, especially if you’ve already read it multiple times.

To help stay on track when proofreading large documents, try:

• Following a proofreading checklist
• Choosing one type of error to focus on at a time (e.g. first checking the entire document for spelling errors, then grammatical errors, and so on)
• Splitting the document into smaller, more manageable chunks
• Taking frequent breaks to rest your eyes (and your brain!)

3. How do I find a professional proofreader?

If you want a second pair of eyes on your writing, a professional proofreader can help.

Here at Proofed, we have a team of over 750 expert proofreaders ready to clean up your writing. 

Whether you’re writing an academic paper , job application , or novel manuscript , our proofreaders can help make sure your work is at its best. Try us out today with a free trial .

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Table of Contents

Collaboration, information literacy, writing process.

Proofreading

• © 2023 by Joseph M. Moxley - University of South Florida

Proofreading refers to a step in  the writing process --the act of critically reading a document with the goal of identifying errors at the word and sentence-level. Proofreading  is crucial to establishing a professional tone in school and workplace contexts . Learn how to edit documents so that your works meet the needs and expectations of your readers.

What is Proofreading

Proofreading refers to a step in the writing process–the process of rereading a document with the goal of identifying word and sentence-level errors .

Synonymous Terms

The terms proofreading , editing , and revision , and may be used interchangeably by some people. However, subject matter experts in writing studies make distinctions between these intellectual strategies by noting their different foci:

a focus on the big picture – the global perspective.

• Content Development
• Organization
• Rhetorical Stance

a focus on line-by-line editing – the local perspective

a focus on a last chance to catch any errors

• Final check for errors

Proofreading may also be referred to as correcting or copy editing.

Related Concepts: Global Perspective ; Local Perspective ; Proofreading ; Revision ; Structured Revision; Styles of Writing

Why Does Proofreading Matter?

As noted for editing , proofreading is critical to establishing a professional tone in academic writing and workplace writing.

Have you ever sent off an email message or submitted a school paper only to later discover that it was full of typographical errors?  How could you have missed all of these errors?

The answer seems to have something to do with how our brains work. Our brains recognize patterns.  This is part of the reason why people who read frequently tend to read faster than infrequent readers: their brains more speedily recognize and process patterns of words on the page.

Texts that we write ourselves are the texts that we can read fastest of all, because our brains are already deeply familiar with the patterns of our words.

But what helps us as readers can hurt us as writers.  When we read our own work, our brains tend to quickly see the patterns that we put on the page rather than the individual words.  We see what we meant to write, and not necessarily what we actually wrote. 

To our readers, however, who are not as familiar with our words, the errors are more apparent—and they detract from our credibility as authors.

To proofread effectively, we need to distance ourselves from the text and see it as our readers will see it.

How to Proofread

The little changes that you make during editing and proofreading can have a profound and disproportionate effect on your target reader’s experience interpretation of your document.

The following techniques can help you critically evaluate your document at the sentence level:

• After working hard to develop the substance of a message, you may be weary of it and eager to turn it over to your instructor. If possible, however, you are wise to set the draft aside and work on another task before trying to edit it. For example, try editing after you first wake up, then after lunch, and then before dinner. Are you surprised that you can keep finding ways to improve the document?
• It has become commonplace for postsecondary writing instructors in the U.S. to suggest that writers not worry about proofreading during the early stages of a writing project. This can be sound advice because time spent proofreading could be wasted if what you’re editing doesn’t respond to the demands of the school assignment or isn’t rhetorically sensitive. Plus, why edit a freewrite when the goal during freewriting is to develop ideas?
• Try reading your document backwards: Begin with the last sentence and move upward toward the introduction
• Place sheets of paper above and below each sentence in the document as you read through it
• Place slashes between each sentence and then evaluate each one separately
• If you are using a personal computer, try printing the document with a different font, such as size 14 or size 10 point instead of the normal size 12.
• Look for mistakes to cluster. When you find one error in paragraph seven, for example, carefully examine the surrounding sentences to see if you had a lapse of concentration when you wrote and copyedited that section.
• Look for errors that you often make, such as sentence fragments or subject-verb agreement.

Brevity - Say More with Less

Clarity (in Speech and Writing)

Coherence - How to Achieve Coherence in Writing

Flow - How to Create Flow in Writing

Inclusivity - Inclusive Language

The Elements of Style - The DNA of Powerful Writing

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Proofreading is the act of reviewing, identifying, and correcting errors in your research paper before it is handed in to be graded by your professor. Common errors found within the text of a paper can be both typographical [i.e., an error in typing] and grammatical [i.e., faulty, unconventional use of language]. However, the act of proofreading can also include identifying and correcting problems with the narrative flow of your paper [i.e., the logical sequence of thoughts and ideas], problems with concise writing [i.e., wordiness and imprecise vocabulary], and problems created by word processing software applications [e.g., unintentional font types, indented paragraphs, line spacing, uneven margins, or orphan headings, sentences, or words].

Editing and Proofreading Strategies. Writing@CSU. Colorado State University; Proofreading. The Writing Center. University of Wisconsin, Madison.

Proofreading Strategies

Proofreading is often the final act before handing in your paper. It is important because most professors grade papers not only on the quality of how you addressed the research problem and the overall organization of the study, but also on the quality of the grammar, punctuation, formatting, and narrative flow of your paper. The assigning of research papers is not just an exercise in developing good research and critical thinking skills, but it is also intended to help you become a better writer. Below are step-by-step strategies you can follow.

Before You Proofread

• Revise the larger aspects of the text . Don't proofread for the purpose of making corrections at the sentence and word level [the act of editing] if you still need to work on the overall focus, development, and organization of the paper or you need to re-arrange or change specific sections [the act of revising].
• Set your paper aside between writing and proofreading . Give yourself a day or so between the writing of your paper and proofreading it. This will help you identify mistakes more easily. This is also a reason why you shouldn't wait until the last minute to draft your paper because it won't provide the time needed to step away before proofreading.
• Eliminate unnecessary words before looking for mistakes . Throughout your paper, you should try to avoid using inflated diction if a more concise phrase works equally well. Simple, precise language is easier to proofread than overly complex sentence constructions and vocabulary. At the same time, also identify and change empty or repetitive phrases.
• Know what to look for . Make a mental note of the mistakes you need to watch for based on comments from your professor on previous drafts of the paper or that you have received about papers written in other classes. This will help you to identify repeated patterns of mistakes more readily.
• Review your list of references . Review the sources mentioned in your paper and make sure you have properly cited them in your bibliography. Also make sure that the titles cited in your bibliography are mentioned in the text. Any omissions should be resolved before you begin proofreading your paper.

NOTE:   Do not confuse the act of revising your paper with the act of editing it. Editing is intended to tighten up language so that your paper is easier to read and understand. This should be the focus when you proofread. If your professor asks you to revise your paper, review the text above concerning ways to improve the overall quality of your paper. The act of revision implies that there is something within the paper that needs to be changed, improved, or re-organized in some significant way. If the reason for a revision is not specified, always ask for clarification.

Individualize the Act of Proofreading

Individualizing your proofreading process to match weaknesses in your writing will help you correct errors more efficiently and effectively . For example, I still tend to make subject-verb agreement errors. Accept the fact that you likely won't be able to check for everything, so be introspective about what your typical problem areas are and look for each type of error individually. Here's how:

• Think about what errors you typically make . Review instructors' comments about your writing and/or set up an appointment to review your paper with a staff member in the Writing Center .
• Learn how to fix those errors . Talk with your professor about helping you understand why you make the errors you do so that you can learn how to avoid them while writing.
• Use specific strategies . Develop strategies you are most comfortable with to find and correct your particular errors in usage, sentence structure, spelling, and punctuation.
• Where you proofread is important! Effective and efficient proofreading requires extended focus and concentration. If you are easily distracted by external activity or noise, proofread in a quiet corner of the library rather than at a table in Starbucks.
• Proofread in several short blocks of time . Avoid trying to proofread your entire paper all at once, otherwise, it will be difficult to maintain your concentration. A good strategy is to start your proofreading each time at the beginning of your paper. It will take longer to make corrections, but you may be surprised how many mistakes you find in text that you have already reviewed.

In general, verb tense should be in the following format, although variations can occur within the text of each section depending on the narrative style of your paper. Note that references to prior research mentioned anywhere in your paper should always be stated in the past tense.

• Abstract--past tense [summary description of what I did]
• Introduction--present tense [I am describing the study to you now]
• Literature Review--past tense [the studies I reviewed have already been published]
• Methodology--past tense [the way I gathered and synthesized data has already happened]
• Results--past tense [the findings of my study have already been discovered]
• Discussion--present tense [I am talking to you now about how I interpreted the findings]
• Conclusion--present tense [I am summarizing the study for you now]

General Strategies for Strengthening Your Paper

As noted above, proofreading involves a detailed examination of your paper to ensure there are no content errors. However, proofreading is also an opportunity to strengthen the overall quality of your paper beyond correcting specific grammar, diction, or formatting mistakes. Before you begin reviewing your paper line-by-line, step back and reflect on what you have written; consider if there are ways to improve each section of the paper by taking into consideration the following “big picture” elements of good writing.

Introduction . Look for any language that reflects broad generalizations, indeterminate phrasing, or text that does not directly inform the reader about the research and its significance. This can include unnecessary qualifiers or text, such as, "This study includes a significant review of the literature [what constitutes "significant"?], "There are a number of findings that are important [just state the number of findings; leave it to the discussion to argue the context of their importance], and, for example, "This research reminds me of...." [why does the research study relate to remembering something; is this first person perspective essential to introducing the research problem].

Research Topic . Make sure the topic does not come across as ambiguous, simplistic, overly broad, or ill-defined. A strong research problem and the associated research questions establish a set of assumptions that should be nuanced, yet challenges the reader to think. Review the Choosing a Research Problem page in this guide. Place yourself in the position of a reader totally unfamiliar with the topic, then, critically evaluate the research problem, any associated research questions you are trying to address, and the theoretical framework. Ask yourself if there is anything that may not make sense or requires further explanation or refinement. The rest of the paper will build on these elements, but the introduction of these foundational aspects of your paper should be clearly and concisely stated.

Paragraph Transitions . Review the overall paper to make sure the narrative flow is coherent throughout and that there are smooth transitions between paragraphs. Ensure that major transitions in text have a heading or sub-heading [if needed] and that the paragraph prior to the transition let's the reader know that you are about to shift to a new idea. Also, look for text that is overly long or that contains too much description and too little analysis and interpretation. Sometimes you need a long paragraph to describe a complex idea, event, or issue, but review them to make sure they can't be broken apart into shorter, more readable paragraphs.

Discussion of Results . Read over your discussion of the research findings and make sure you have not treated any of the evidence as unproblematic or uncomplicated. Make sure you have discussed the results through a critical lens of analysis that takes into account alternative interpretations or possible counter-arguments. In most cases, your discussion section should demonstrate a thorough understanding of the study's findings and their implications, both positive, supportive findings and negative, unanticipated findings.

Conclusion . Make sure you have done more than simply re-state the research problem and what you did. Provide the reader with a sense of closure by ensuring that the conclusion has highlighted all the main points of the paper and tells the reader why the study was important, what the paper's broader significance and implications might be, and, if applicable, what areas of the study require further research. Also note that the conclusion is usually no more than two or three paragraphs. If your conclusion is longer, look for ways to condense the text and be alert to information that is superfluous or should be integrated into other parts of your paper [e.g., new information].

Specific Strategies to Help Identify Errors

Once you have made any necessary revisions to your paper and looked for ways to strengthen its overall quality, focus on identifying and correcting specific errors within the text.

• Work from a printout, not a computer screen . Besides sparing your eyes from the strain of glaring at a computer screen, proofreading from a printout allows you to easily skip around to where errors might have been repeated throughout the paper [e.g., misspelling the name of a person].
• Read out loud . This is especially helpful for spotting run-on sentences and missing words, but you'll also hear other problems that you may not have identified while reading the text out loud. This will also help you adopt the role of the reader, thereby helping you to understand the paper as your audience might.
• Use a ruler or blank sheet of paper to cover up the lines below the one you're reading . This technique keeps you from skipping over possible mistakes and allows you to deliberately pace yourself as you read through your paper.
• Circle or highlight every punctuation mark in your paper . This forces you to pay attention to each mark you used and to confirm its purpose in each sentence or paragraph. This is a particularly helpful strategy if you tend to misuse or overuse a punctuation mark, such as a comma or semi-colon.
• Use the search function of the computer to find mistakes . Using the Ctrl F search [find] feature can help identify repeated errors faster. For example, if you overuse a phrase or repeatedly rely on the same qualifier [e.g., "important"], you can do a search for those words or phrases and in each instance make a decision about whether to remove it, rewrite the sentence, or use a synonym.
• If you tend to make many mistakes, check separately for each kind of error , moving from the most to the least important, and following whatever technique works best for you to identify that kind of mistake. For instance, read through once [backwards, sentence by sentence] to check for fragments; read through again [forward] to be sure subjects and verbs agree, and again [perhaps using a computer search for "this," "it," and "they"] to trace pronouns to antecedents.
• End with using a computer spell checker or reading backwards word by word . Remember that a spell checker won't catch mistakes with homonyms [e.g., "they're," "their," "there"] or certain word-to-word typos [like typing "he" when you meant to write "the"]. The spell-checker function can catch some errors quickly, but it is not a substitute for carefully reviewing the text. This also applies to the grammar check function as well.
• Leave yourself enough time . Since many errors are made and overlooked by speeding through writing and proofreading, setting aside the time to carefully review your writing will help you identify errors you might otherwise miss. Always read through your writing slowly. If you read through the paper at a normal speed, you won't give your eyes sufficient time to spot errors.
• Ask a friend to read your paper . Offer to proofread a friend's paper if they will review yours. Having another set of eyes look over your writing will often spot errors that you would have otherwise missed.

NOTE:   Pay particular attention to the spelling of proper nouns [an individual person, place, or organization]. Make sure the name is carefully capitalized and spelled correctly, and that this spelling has been used consistently throughout the text of your paper. This is especially true for proper nouns transliterated into English or that have been spelled differently over time. In this case, choose the spelling most consistently used by researchers in the literature you have cited so, if asked, you can explain the logic of your choice.

Carduner, Jessie. "Teaching Proofreading Skills as a Means of Reducing Composition Errors." Language Learning Journal 35 (2007): 283-295; Gaste, Barbara. “Editing and Proofreading Your Own Work.” American Medical Writers Association (AMWA) Journal 30 (2015): 147-151; Editing and Proofreading. Writing Center, University of North Carolina; Proofreading. Writing Center, University of Wisconsin, Madison; Proofreading. Writing Center, University of Maryland; Harris, Jeanette. "Proofreading: A Reading/Writing Skill." College Composition and Communication 38 (1987): 464-466; Editing and Proofreading. The Writing Center. University of North Carolina; Mintz, Steve. “Simple Ways to Strengthen Your Students’ Writing.” Higher Ed Gamma (Opinion). Inside Higher Ed , August 17, 2022; Revising vs. Proofreading, Kathleen Jones Wright Writing Center, Indiana University of Pennsylvania; Editing and Proofreading Strategies. Student Writing Support, University of Minnesota; Saleh, Naveed. The Writer's Guide to Self-Editing: Essential Tips for Online and Print Publication . Jefferson, NC: McFarland, 2019; Writing a Paper. Walden Writing Center, Walden University; The Writing Process: Proofreading. The Purdue Online Writing Lab, Purdue University.

USC Writing Center

S hould you need help proofreading your paper, take advantage of the assistance offered by consultants at the USC Writing Center located on the second floor of Taper Hall, room 216. Consultations are free and they can help you with any aspect of the writing process. Walk-in help is provided when consultants are available, but you should schedule an appointment online because the Center gets very busy as the semester progresses. If you meet with a consultant be sure to bring a copy of your writing assignment, any relevant handouts or texts, and any outlines or drafts you've written. Also, the Center conducts helpful, fifty minute small-group writing skills workshops for students that cover a wide range of topics. These workshops provide an opportunity for you to improve your skills related to an aspect of writing that you may be struggling with, particularly if English is not your native language.

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Home / Guides / Writing Guides / Writing Tips / How to Proofread a Paper

How to Proofread a Paper

There are five steps to the writing process— prewriting, writing, revising, editing, and publishing. After writing your paper, there are two whole steps you need to do before turning it in. That’s right—revising your essay takes just as many steps as writing it in the first place.

Step 1: Revising—The Big Picture

As you can see, revising is step 3, or the first thing you should do after writing your essay. Revising does not mean looking at spelling or grammar; that comes with the next step, editing. Rather, revising means looking at the paper as a whole and identifying (and correcting) to make the essay flow better.

• Organization

Read your essay with an eye for how it’s organized. For example, does it makes sense to talk about gathering ingredients for a ham sandwich in the last paragraph before the conclusion? Not really.

To identify bad organization, you have to know how you’ve organized your paper. You can organize your essay in many different ways, some of which include:

• Chronology (progression through time)
• Logic (what makes the most sense to talk about first, second, and so on)
• Topic (group body paragraphs so that the topic stays the same until you’re ready to move on)

The decision about how to organize the paper should be made in step 1 of the writing process, during prewriting. Outlines are helpful for ensuring that you write the essay in an organized way. You may find when you revise the essay that the organization on the outline had some gaps in logic or chronology. That’s ok—this is the time to move paragraphs around!

• Making Sentences Flow

A step below organization is checking for flow. Look at each individual paragraph and ensure that the sentences string together in a rhythm that can easily be followed. In other words, you want the reader to be able to move easily through the writing without having to pause to figure out what you were trying to say.

An easy way to fix this problem is transition words . There are many, many words that help sentences connect to one another. Use words such as:

• In other words

There are tons more, but the idea is that you use these terms when you want to connect the idea of one sentence to the preceding sentence, whether it agrees or not.

Example: Transition Word of Agreement

I jogged to the store to catch up with my friend, who I’d spied driving down the road. Strangely enough , she didn’t turn the car off when she went inside.

Example: Transition Word of Disagreement

My dog sat languishing in the sun to warm up after being in the cold air conditioning. Be that as it may, I don’t like him to get too hot, so I brought him back inside.

• Making Quotes and Examples Flow

Another way to make sure your essay flows well is to ensure that every quote, paraphrase, summary, or example is well introduced and explained. When you fail to do this, it makes the reader pause.

If you tell your reader who says it, then follow the quote with an analysis of the quote and why you used it, the reader is able to keep up a good rhythm. That’s your goal.

• Introduction of quote/paraphrase (top bread)
• The quote/paraphrase itself (meat or sandwich filling)
• Analysis/explanation (bottom bread)

This strategy will ensure that your readers are clued in to each quote and can read at a steady pace.

Example: Quotation Sandwich

In his article on salads, Sam Sifton of the New York Times says, “[Julia’s] recipe for simple vinaigrette may anyway change your life for the better, forever.” Vinaigrettes may be known for lacking the creaminess that traditional salad dressing has, but Sifton pushes us to give them another look.

*Referenced article is linked here .

Step 2: Editing—The Details

When you get to the fourth step of the writing process, editing, you’re in for some fine tuning. This step ensures that your writing is correct and easier to read.

• Basic Paper Formatting

With any essay that you’re turning in for a grade, there should be some sort of format you follow. The most common formats for students are MLA format and APA format , but teachers can add their own rules. Pay attention to what is required and check for this formatting once your revising is done. Look at example pages to make sure you’ve got it right. Do you have one-inch margins? Size 12 font? Is your heading in the correct place? And so on.

• Checking for Slang

Although some slang might be ok in essay in order for your voice to shine through, most of the time, formal writing is required. Unless your teacher tells you that slang is ok, avoid using words like “ain’t” or “man” or whatever is popular online or at school these days.

Read through your essay and look for these words. You may find it helpful to have someone else read through it, or to read it out loud yourself. When you find slang words, replace them with formal terms.

One of the most important things to look for when you’re editing your paper is proper grammar. While there are many grammar rules, here are a few major ones to make sure you’ve got it right:

• Subject-verb agreement
• Verb tense consistency
• Plural agreement
• Pronoun agreement

It might be helpful to review grammar rules from previous years of study to ensure that you’re getting it right. You can also submit your essay to a tutor for their help in identifying incorrect grammar.

• Punctuation

Finally, one of the most basic and important parts of an essay is ensuring punctuation is correct. This means you’re looking at commas, periods, semicolons, colons, apostrophes, dashes, quotation marks, and so on. You’re looking both for missing and incorrectly placed punctuation. Commas can be quite complex, but here’s a quick snapshot of some of the most pertinent comma rules:

• Comma before a coordinating conjunction
• Comma after an introductory phrase
• Comma before a quote or after it, depending on its location in the sentence
• Comma in a series of items

Again, it might be helpful to look at basic punctuation rules before reviewing your essay. It’s also helpful to have someone else, like a tutor, look over the essay to catch mistakes you missed.

During the revising and editing steps of the writing process, there certainly is a lot to do. But don’t let that overwhelm you. Take it one step at a time. Ignore comma errors while revising; then forget about organization when you’re hunting for missing periods. In the end, your polished essay will likely be well rewarded.

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Proofreading

Proofreading means examining your text carefully to find and correct typographical errors and mistakes in grammar, style, and spelling. Here are some tips.

Before You Proofread

• Be sure you’ve revised the larger aspects of your text. Don’t make corrections at the sentence and word level if you still need to work on the focus, organization, and development of the whole paper, of sections, or of paragraphs.
• Set your text aside for a while (15 minutes, a day, a week) between writing and proofing. Some distance from the text will help you see mistakes more easily.
• Eliminate unnecessary words before looking for mistakes. See the writing center handout how to write clear, concise, direct sentences.
• Know what to look for. From the comments of your professors or a writing center instructor on past papers, make a list of mistakes you need to watch for.

When You Proofread

• Work from a printout, not the computer screen. (But see below for computer functions that can help you find some kinds of mistakes.)
• Read out loud. This is especially helpful for spotting run-on sentences, but you’ll also hear other problems that you may not see when reading silently.
• Use a blank sheet of paper to cover up the lines below the one you’re reading. This technique keeps you from skipping ahead of possible mistakes.
• Use the search function of the computer to find mistakes you’re likely to make. Search for “it,” for instance, if you confuse “its” and “it’s;” for “-ing” if dangling modifiers are a problem; for opening parentheses or quote marks if you tend to leave out the closing ones.
• If you tend to make many mistakes, check separately for each kind of error, moving from the most to the least important, and following whatever technique works best for you to identify that kind of mistake. For instance, read through once (backwards, sentence by sentence) to check for fragments; read through again (forward) to be sure subjects and verbs agree, and again (perhaps using a computer search for “this,” “it,” and “they”) to trace pronouns to antecedents.
• End with a spelling check, using a computer spelling checker or reading backwards word by word. But remember that a spelling checker won’t catch mistakes with homonyms (e.g., “they’re,” “their,” “there”) or certain typos (like “he” for “the”).

When You Want to Learn More

• Take a class. The Writing Center offers many workshops, including a number of grammar workshops.
• Use a handbook. A number of handbooks are available to consult in the Writing Center, and each Writing Center computer has an online handbook.
• Consult a Writing Center instructor. Writing Center instructors won’t proofread your papers, but they’ll be glad to explain mistakes, help you find ways to identify and fix them, and share Writing Center handouts that focus on particular problems.

Check for information on how to make an appointment with a Writing Center instructor .

For further information see our resources on Peer Reviews .

Grammar and Punctuation

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Using Dashes

Using Commas

Using Semicolons

Using Coordinating Conjunctions

Using Conjunctive Adverbs

Subject-Verb Agreement

Using Gender–Neutral Pronouns in Academic Writing

How to Proofread

Twelve Common Errors: An Editing Checklist

Clear, Concise Sentences

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Proofreading is primarily about searching your writing for errors, both grammatical and typographical, before submitting your paper for an audience (a teacher, a publisher, etc.). Use this resource to help you find and fix common errors.

Proofreading can be a difficult process, especially when you’re not sure where to start or what this process entails. Think of proofreading as a process of looking for any inconsistencies and grammatical errors as well as style and spelling issues. Below are a few general strategies that can help you get started.

General Strategies Before You Proofread

General strategies while you proofread, when you are done.

• Make sure that you leave plenty of time after you have finished your paper to walk away for a day or two, a week, or even 20 minutes. This will allow you to approach proofreading with fresh eyes.
• Print out a hard copy. Reading from a computer screen is not the most effective way to proofread. Having a hardcopy of your paper and a pen will help you.
• Have a list of what to look for. This will help you manage your time and not feel overwhelmed by proofreading. You can get this list from previous assignments where your instructor(s) noted common errors you make.
• Don’t rush . Many mistakes in writing occur because we rush. Read slowly and carefully to give your eyes enough time to spot errors.
• Read aloud to yourself. Reading a paper aloud encourages you to read each word and can help you notice small mistakes.
• Read aloud to a friend and have the friend give you oral feedback.
• Have a friend read your paper aloud while you don’t read along.
• Use the search in document function of the computer to look for common errors from your list.
• Read from the end. Read individual sentences one at a time starting from the end of the paper rather than the beginning. This forces you to pay attention to the sentence itself rather than to the ideas of the paper as a whole.
• Role-play. While reading, put yourself in your audience's shoes. Playing the role of the reader encourages you to see the paper as your audience might.
• Have a friend look at your paper after you have made all the corrections you identified. A new reader will be able to help you catch mistakes that you might have overlooked.
• Make an appointment with a Writing Lab tutor if you have any further questions or want someone to teach you more about proofreading.
• Ask your teacher to look at the areas you usually have trouble with to see if you have made any progress.

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Blog • Understanding Publishing

Last updated on Dec 06, 2022

What Do Proofreading Marks Mean?

Having your work come back from an editor covered in red pen is daunting to begin with. Receiving a manuscript that's covered in proofreading marks that might as well be hieroglyphics can be even more intimidating.

But before you reach for your cipher wheel, remember that the purpose of proofreading marks isn’t to confuse you. It’s to provide a detailed roadmap to a professional and polished final manuscript . While independent authors are not likely to spring for large print runs that would be ruined by more than a couple of typos — proofreading still remains an essential step for most serious self-publishers.

These days, it’s unlikely for the proofreading process to occur via pen-and-paper, meaning that the use of proofreading marks is also becoming increasingly rare. Most proofreaders used tools such a “tracked changes” to flag issues — and many indie authors choose to forgo professional proofers for software such as Grammarly or the simple red, dotted line that signifies a typo in processors such as Microsoft Word or Google Docs.

That being said, screen fatigue is a real thing, and there’s something about a hard-copy document that seems to draw the eye to errors more than a digital one. So if you’re working with a proofreader and want to ensure the collaboration fulfills its top potential, it’s worth getting to know the meaning of the more common proofreading marks.

So, without further ado…

What are proofreading marks?

Proofreading marks are used to highlight spelling, vocabulary, grammatical, and punctuation errors, along with formatting and layout issues.

When proofreaders are editing a hard-copy manuscript, they will leave corrections on both sides of the margins: on the left-hand margins for corrections in the first half of the sentence and on the right-hand side for corrections in the latter half of the sentence. A corresponding mark will also be included in-line to indicate where the issue is.

Alright, now let’s take a look at these formidable editing symbols.

Proofreading marks chart

The following proofreading symbols are taken from The Chicago Manual of Style 17th edition. While in-house style guides may vary from publisher to publisher, these symbols are standard across the industry.

Let’s start with operational marks, which cover everything from spelling to sentence structure to improper spacing.

• The “delete” symbol on its own will refer to a word, while “delete and close up” will refer to a letter in a word.
• The ”let it stand” symbol would be used when more than one round of proofreading was done, and it indicates that a correction or alteration should be ignored.
• The “transpose” symbol indicates the order of words needs to be changed (spot the transpose problem).

Next up are the punctuation marks, which — you guessed it — indicate that punctuation needs to be added.

Then there are typography marks, which denote formatting corrections.

Lastly, these are common abbreviations used by proofreaders to indicate issues related to the copy itself.

The importance of proofreading

Ensuring that your book is polished and error-free is just as important to the reader experience as the writing quality. Your book can’t effectively communicate if the reader is constantly paused by spelling mistakes, awkward sentence structures,       or      uneven       spacing.

Here's what these proofreading marks look like in use, when a proofreader returns a manuscript, and when their suggestions have been incorporated:

Looking to get your book proofread?

First, we recommend doing as much of the work yourself as possible. Here are a few resources that will help:

• The Reedsy Book Editor will point out spelling and grammatical errors as you go.
• How to Self-Edit Your Manuscript Like a Pro is a free Reedsy Learning course covering the ten most common writing mistakes — both how to find and fix them.
• What to Expect From Beta Readers And Where to Find Them is a post written by a Reedsy author who worked extensively with beta readers to get his book ready for publication.
• What are Sensitivity Readers? And Should Authors Use Them? is all about the controversial topic of sensitivity readers and what they actually do.

Free course: How to self-edit like a pro

Rid your manuscript of the most common writing mistakes with this 10-day online course. Get started now.

Once you’ve done all the proofreading work you can, we encourage you to consider working with a professional. Proofreading is the final stage of the editing process and will ensure your book fully meets its potential for success.

The average costs of working with a professional proofreader on Reedsy are:

• $350 for a 40k-word book
• $520 for a 60k-word book
• $700 for an 80k-word book

Head to our marketplace to request quotes from a variety of professional proofreaders for free.

Have you ever worked with a professional proofreader? Or do you prefer to go the DIY route? Leave any thoughts or questions in the comments below!

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Proofreading Marks: What Do They Mean?

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Last updated: June 1, 2017

An overview of commonly used proofreading symbols

If you've ever had a hard copy of a document proofread, chances are that you're familiar with the strange typology of professional proofreaders. Your returned document is so full of symbols (hieroglyphics? squiggles? cuneiform script?!) that you think it has been translated into Martian!

These strange markings are the "footprint" that your proofreader has left on the document to highlight where changes need to be made to the text. The proofreader uses a series of symbols and abbreviations to suggest changes, correct spelling errors, improve punctuation, and generally enhance the quality and readability of a hard copy document.

Locating proofreading marks

In hard copy proofreading, corrections typically appear in the left or right margins beside the line containing the error. A mark is also placed in the text to indicate where the correction needs to be made. A caret (^) indicates an addition, and a line through the text indicates a deletion or a replacement. Proofreading marks are traditionally written in red ink for better visibility.

Frequently used proofreading marks

Delete a letter: a diagonal line through the letter with the delete mark in the margin

Delete a word: a straight line through the word with the delete mark in the margin

Frequently used abbreviations

Faulty diction: DICT

Awkwardly expressed or constructed: AWK

Wordy, too verbose: WDY

Wrong word used (e.g. to/too): WW

Eliminate the need for proofreading marks

Deciphering a proofreader's suggested changes used to take hours; fortunately, it doesn't have to any more. Submit your document to any of our proofreading services today for a speedy, easy-to-use document review that makes use of Tracked Changes instead.

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Title: dore: a dataset for portuguese definition generation.

Abstract: Definition modelling (DM) is the task of automatically generating a dictionary definition for a specific word. Computational systems that are capable of DM can have numerous applications benefiting a wide range of audiences. As DM is considered a supervised natural language generation problem, these systems require large annotated datasets to train the machine learning (ML) models. Several DM datasets have been released for English and other high-resource languages. While Portuguese is considered a mid/high-resource language in most natural language processing tasks and is spoken by more than 200 million native speakers, there is no DM dataset available for Portuguese. In this research, we fill this gap by introducing DORE; the first dataset for Definition MOdelling for PoRtuguEse containing more than 100,000 definitions. We also evaluate several deep learning based DM models on DORE and report the results. The dataset and the findings of this paper will facilitate research and study of Portuguese in wider contexts.

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Eng 101 oer: proofreading & editing.

• Reading to Write
• Why We Write
• Rhetorical Context
• Brainstorming
• Proofreading & Editing
• Paragraph Development
• Thesis Statements
• Introductions
• Conclusions
• Transitions & Phrases
• Peer Reviews
• Exemplification
• Classification
• Cause/Effect
• Grammar Resources

Tips for Proofreading and Editing

• Get some distance from the text!  It’s hard to edit or proofread a paper that you’ve just finished writing—it’s still to familiar, and you tend to skip over a lot of errors. Put the paper aside for a few hours, days, or weeks. Go for a run. Take a trip to the beach. Clear your head of what you’ve written so you can take a fresh look at the paper and see what is really on the page. Better yet, give the paper to a friend—you can’t get much more distance than that. Someone who is reading the paper for the first time, comes to it with completely fresh eyes.
• Decide what medium lets you proofread most carefully.  Some people like to work right at the computer, while others like to sit back with a printed copy that they can mark up as they read.
• Try changing the look of your document.  Altering the size, spacing, color, or style of the text may trick your brain into thinking it’s seeing an unfamiliar document, and that can help you get a different perspective on what you’ve written.
• Find a quiet place to work.  Don’t try to do your proofreading in front of the TV or while you’re chugging away on the treadmill. Find a place where you can concentrate and avoid distractions.
• If possible, do your editing and proofreading in several short blocks of time.  Your concentration may start to wane if you try to proofread the entire text at one time.
• If you’re short on time, you may wish to prioritize.  Make sure that you complete the most important editing and proofreading tasks.

Proofreading

Why proofread it’s the content that really matters, right.

Content is important. But like it or not, the way a paper looks affects the way others judge it. When you’ve worked hard to develop and present your ideas, you don’t want careless errors distracting your reader from what you have to say. It’s worth paying attention to the details that help you to make a good impression.

Most people devote only a few minutes to proofreading, hoping to catch any glaring errors that jump out from the page. But a quick and cursory reading, especially after you’ve been working long and hard on a paper, usually misses a lot. It’s better to work with a definite plan that helps you to search systematically for specific kinds of errors.

Sure, this takes a little extra time, but it pays off in the end. If you know that you have an effective way to catch errors when the paper is almost finished, you can worry less about editing while you are writing your first drafts. This makes the entire writing process more efficient.

Try to keep the editing and proofreading processes separate. When you are editing an early draft, you don’t want to be bothered with thinking about punctuation, grammar, and spelling. If your worrying about the spelling of a word or the placement of a comma, you’re not focusing on the more important task of developing and connecting ideas.

The Proofreading Process

You probably already use some of the strategies discussed below. Experiment with different tactics until you find a system that works well for you. The important thing is to make the process systematic and focused so that you catch as many errors as possible in the least amount of time.

• Don’t rely entirely on spelling checkers.  These can be useful tools but they are far from foolproof. Spell checkers have a limited dictionary, so some words that show up as misspelled may really just not be in their memory. In addition, spell checkers will not catch misspellings that form another valid word. For example, if you type “your” instead of “you’re,” “to” instead of “too,” or “there” instead of “their,” the spell checker won’t catch the error.  
• Grammar checkers can be even more problematic.  These programs work with a limited number of rules, so they can’t identify every error and often make mistakes. They also fail to give thorough explanations to help you understand why a sentence should be revised. You may want to use a grammar checker to help you identify potential run-on sentences or too-frequent use of the passive voice, but you need to be able to evaluate the feedback it provides.  
• Proofread for only one kind of error at a time.  If you try to identify and revise too many things at once, you risk losing focus, and your proofreading will be less effective. It’s easier to catch grammar errors if you aren’t checking punctuation and spelling at the same time. In addition, some of the techniques that work well for spotting one kind of mistake won’t catch others.  
• Read slow, and read every word.  Try  reading out loud , which forces you to say each word and also lets you hear how the words sound together. When you read silently or too quickly, you may skip over errors or make unconscious corrections.  
• Separate the text into individual sentences.  This is another technique to help you to read every sentence carefully. Simply press the return key after every period so that every line begins a new sentence. Then read each sentence separately, looking for grammar, punctuation, or spelling errors. If you’re working with a printed copy, try using an opaque object like a ruler or a piece of paper to isolate the line you’re working on.  
• Circle every punctuation mark.  This forces you to look at each one. As you circle, ask yourself if the punctuation is correct.  
• Read the paper backwards.  This technique is helpful for checking spelling. Start with the last word on the last page and work your way back to the beginning, reading each word separately. Because content, punctuation, and grammar won’t make any sense, your focus will be entirely on the spelling of each word. You can also read backwards sentence by sentence to check grammar; this will help you avoid becoming distracted by content issues.  
• Proofreading is a learning process.  You’re not just looking for errors that you recognize; you’re also learning to recognize and correct new errors. This is where handbooks and dictionaries come in. Keep the ones you find helpful close at hand as you proofread.  
• Ignorance may be bliss, but it won’t make you a better proofreader.  You’ll often find things that don’t seem quite right to you, but you may not be quite sure what’s wrong either. A word looks like it might be misspelled, but the spell checker didn’t catch it. You think you need a comma between two words, but you’re not sure why. Should you use “that” instead of “which”? If you’re not sure about something, look it up.  
• The proofreading process becomes more efficient as you develop and practice a systematic strategy.  You’ll learn to identify the specific areas of your own writing that need careful attention, and knowing that you have a sound method for finding errors will help you to focus more on developing your ideas while you are drafting the paper.

Editing is what you begin doing as soon as you finish your first draft. You reread your draft to see, for example, whether the paper is well-organized, the transitions between paragraphs are smooth, and your evidence really backs up your argument. You can edit on several levels:

Have you done everything the assignment requires? Are the claims you make accurate? If it is required to do so, does your paper make an argument? Is the argument complete? Are all of your claims consistent? Have you supported each point with adequate evidence? Is all of the information in your paper relevant to the assignment and/or your overall writing goal? 

Overall structure

Does your paper have an appropriate introduction and conclusion? Is your thesis clearly stated in your introduction? Is it clear how each paragraph in the body of your paper is related to your thesis? Are the paragraphs arranged in a logical sequence? Have you made clear transitions between paragraphs? 

Structure within paragraphs

Does each paragraph have a clear topic sentence? Does each paragraph stick to one main idea? Are there any extraneous or missing sentences in any of your paragraphs? 

Have you defined any important terms that might be unclear to your reader? Is the meaning of each sentence clear? (One way to answer this question is to read your paper one sentence at a time, starting at the end and working backwards so that you will not unconsciously fill in content from previous sentences.) Is it clear what each pronoun (he, she, it, they, which, who, this, etc.) refers to? Have you chosen the proper words to express your ideas? Avoid using words you find in the thesaurus that aren’t part of your normal vocabulary; you may misuse them.

Have you used an appropriate tone (formal, informal, persuasive, etc.)? Is your use of gendered language (masculine and feminine pronouns like “he” or “she,” words like “fireman” that contain “man,” and words that some people incorrectly assume apply to only one gender—for example, some people assume “nurse” must refer to a woman) appropriate? Have you varied the length and structure of your sentences? Do you tends to use the passive voice too often? Does your writing contain a lot of unnecessary phrases like “there is,” “there are,” “due to the fact that,” etc.? Do you repeat a strong word (for example, a vivid main verb) unnecessarily? (For tips, see our  handouts on style  and  gender-inclusive language .)

Have you appropriately cited quotes, paraphrases, and ideas you got from sources? Are your citations in the correct format? 

As you edit at all of these levels, you will usually make significant revisions to the content and wording of your paper. Keep an eye out for patterns of error; knowing what kinds of problems you tend to have will be helpful, especially if you are editing a large document like a thesis or dissertation. Once you have identified a pattern, you can develop techniques for spotting and correcting future instances of that pattern. For example, if you notice that you often discuss several distinct topics in each paragraph, you can go through your paper and underline the key words in each paragraph, then break the paragraphs up so that each one focuses on just one main idea.

Common Marks:  

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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).

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

Authors and Affiliations

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

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

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

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

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

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

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

Łukasz Kreft & Alexander Botzki

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

Philippe Malcorps & Luk Daenen

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Contributions

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

Corresponding author

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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Astronomers only knew of a single binary Cepheid system—they just found nine more

by Andy Tomaswick, Universe Today

Measuring the distance to far away objects in space can be tricky. We don't even know the precise distance to even our closest neighbors in the universe—the Small and Large Magellanic Clouds. But, we're starting to get to the tools to measure it. One type of tool is a Cepheid Variable—a type of star that varies its luminosity in a well-defined pattern. However, we don't know much about their physical properties, making utilizing them as distance markers harder.

Finding their physical properties would be easier if there were any Cepheid binaries that we could study, but astronomers have only found one pair so far. Until a recent paper from researchers from Europe, the US, and Chile shows measurements of nine additional binary Cepheid systems—enough that we can start understanding the statistics of these useful distance markers. The paper is published on the arXiv preprint server.

Like traditional stars, binary Cepheid systems result when two stars orbit around each other. In this case, both of those stars must be Cepheids—meaning they are massive compared to our sun and much brighter. In addition, their luminosity must vary in a repeatable pattern so that we can track it consistently.

All of those features can vary a lot if two stars change in luminosity but at different rates and phases around each other. It's difficult to parse out which star is waxing, which is waning, and which direction they are moving in, both compared to us and each other. Long periods of observation are required to fix some of those variables, and that is precisely what the new paper describes.

The researchers looked at nine sets of Cepheids that were believed to be binary systems but hadn't yet been confirmed due to the difficulty of separating the two stars from each other. They pulled data from the Optical Gravitational Lensing Experiment (OGLE) database, a variable star observation project run by the university of Warsaw for over 30 years. In so doing, they could confirm, for the first time, that each of these suspected binaries contained two separate stars.

Those nine binary systems were located in the Small and Large Magellanic Cloud and the Milky Way. One located in the Milky Way is by far the closest, at only 11 kiloparsecs (about 3,000 light-years) away. The researchers also had good luck because of the length of orbital periods of the binaries they studied—most were over five years, and a shorter observational data set might not have caught them.

Understanding how these systems exist and where they are is just the first step. Using them for more helpful science is the next. The most obvious way to do so is to increase our understanding of Cepheids.

Despite being one of the most commonly used distance markers in the universe, we know surprisingly little about how they form, what they're made of, or their life cycle. Closely studying a binary system, where the stars interact, could help shed light (figuratively in this sense) on some of those properties.

As the authors point out in their paper, this is part of a long-term ongoing project—they were also part of the team that confirmed the original Cepheid binary system back in 2014.

OGLE continues to collect more data, as are other sky surveys, and there are likely more Cepheid binaries out there. Every new discovery will help improve our statistical understanding of these critical distance markers—we just need to take the time to find them first.

Journal information: arXiv

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