is a research article peer reviewed

Peer-reviewed journal articles

• Overview of peer review
• Scholarly and academic - good enough?
• Find peer-reviewed articles

Using Library Search

Is a journal peer reviewed, check the journal.

Resources listed in  Library Search that are peer reviewed will include the Peer Reviewed icon.

For example:

If you have not used Library Search to find the article, which may indicate if it's peer reviewed, you can use Ulrichsweb to check.

• Go to Ulrichsweb

Screenshot of search box in UlrichsWeb  © Proquest

•  Enter the journal title in the search box.

Screenshot of results list in UlrichsWeb  © Proquest

•  If there are no results, do a search in Ulrichsweb to find journals in your field that are peer reviewed.

Be aware that not all articles in peer reviewed journals are refereed or peer reviewed, for example, editorials and book reviews.

If the journal is not listed in Ulrichsweb :

• Go to the journal's website
• Check for information on a peer review process for the journal. Try the Author guidelines , Instructions for authors  or About this journal sections.

If you can find no evidence that a journal is peer reviewed, but you are required to have a refereed article, you may need to choose a different article.

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• Last Updated: Dec 6, 2023 2:42 PM
• URL: https://guides.library.uq.edu.au/how-to-find/peer-reviewed-articles

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Q. How can I tell if an article is peer reviewed?

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Answered By: Georgiana Grant Last Updated: Jan 04, 2024     Views: 20968

Peer Reviewed Articles go through a process in which experts in the field (the author's peers) verify that the information and research methods are up to standards.  Peer reviewed articles are usually research articles or literature reviews and have certain characteristics in common.  This page has an overview on how to identify peer reviewed articles:  Recognize a scholarly/peer-reviewed article . 

The journal publisher's website

If you are unsure whether or not an article is peer reviewed, you must look at the journal rather than the article. One of the best places to find out if a journal is peer-reviewed is the journal website.  Most publications have a journal website that includes information for authors about the publication process.   If you find the journal website, look for the link that says information for authors, instructions for authors, guidelines for authors or something similar.  On this page is information about whether the articles are peer reviewed.

Article found in a library database

In an Ebsco database, you will look at the detailed record of the article (you will see this when you click on the title of a search result). Then click on the Source (the name of the publication):

You will then see information about the specific journal. The last item on the list will be a simple yes or no to your question: 

Other databases may provide the same information using different words or visual cues. If you are still unsure, please give us a call, chat with us, or send us an email and a Research Librarian can help you.

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Peer Reviewed Articles: What Are They?

• How to Tell if a Journal Article is Peer Reviewed
• Review Articles
• Types of Literature Sources, (Grey Literature)
• What are Evidence Based Reviews?

Peer Reviewed?

How do you determine whether an article qualifies as being a peer-reviewed journal article?

First, you need to be able to identify which journals are peer-reviewed. There are generally four methods for doing this

• Limiting a database search to peer-reviewed journals only.  You can do this in the Article Quick Search tab in the Library's home page.  
• Some  databases allow you to limit searches for articles to peer reviewed journals only. 
• If you cannot limit your initial search to peer-reviewed journals, you will need to check if the individual journal where the article was published is a peer-reviewed journal. You may want to utilize Method 3 below. 
• Examining the publication to see if it is peer-reviewed.

If  the first two methods described above did not identify the journal,(and the article), as peer-reviewed, you may then need to examine the journal physically or look at additional pages of the journal online to determine if it is peer-reviewed. ​ This method is not always successful with resources available only online. Try the following steps:

• Locate the journal in the Library or online, then identify the most current entire year’s issues.
• Locate the masthead of the publication. This usually consists of a box towards either the front or the end of the periodical, and contains publication information such as the editors of the journal, the publisher, the place of publication, the subscription cost and similar information. It is way easier to find in a print copy of the journal
• Does the journal say that it is peer-reviewed? If so, you’re done. If not, search farther within the journal's website.
• Check in and around the masthead to locate the method for submitting articles to the publication.  If you find information similar to “to submit articles, send three copies…”, the journal is probably peer-reviewed . In this case, you are inferring that the publication is then going to send the multiple copies of the article to the journal’s reviewers. This may not always be the case, so relying upon this information alone may not be foolproof.
• If you do not see this type of statement in the first issue of the journal that you look at, examine the remaining issues to see if this information is included. Sometimes publications will include this information in only a single issue a year.
• Is it scholarly, using technical terminology? Is the article format similar to the following - abstract, literature review, methodology, results, conclusion, and references? Are the articles written by scholarly researchers in the field? Is advertising non-existent, or kept to a minimum? Are there references listed in footnotes or bibliographies? If you answered yes to all these questions, the journal may very well be peer-reviewed. This determination would be strengthened by having met the previous criterion of a multiple-copies submission requirement. If you answered these questions no, the journal is probably not peer-reviewed.
• Find the journal web site on the internet, (not via library databases), and see if it states that the journal is peer-reviewed. Check the site URL to be sure it is the homepage of the journal or of the publisher of the journal.

Adapted from "How to Recognize Peer Reviewed Journals" , Angelo State University

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Methodology

• What Is Peer Review? | Types & Examples

What Is Peer Review? | Types & Examples

Published on December 17, 2021 by Tegan George . Revised on June 22, 2023.

Peer review, sometimes referred to as refereeing , is the process of evaluating submissions to an academic journal. Using strict criteria, a panel of reviewers in the same subject area decides whether to accept each submission for publication.

Peer-reviewed articles are considered a highly credible source due to the stringent process they go through before publication.

There are various types of peer review. The main difference between them is to what extent the authors, reviewers, and editors know each other’s identities. The most common types are:

• Single-blind review
• Double-blind review
• Triple-blind review

Collaborative review

Open review.

Relatedly, peer assessment is a process where your peers provide you with feedback on something you’ve written, based on a set of criteria or benchmarks from an instructor. They then give constructive feedback, compliments, or guidance to help you improve your draft.

Table of contents

What is the purpose of peer review, types of peer review, the peer review process, providing feedback to your peers, peer review example, advantages of peer review, criticisms of peer review, other interesting articles, frequently asked questions about peer reviews.

Many academic fields use peer review, largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the manuscript. For this reason, academic journals are among the most credible sources you can refer to.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

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Depending on the journal, there are several types of peer review.

Single-blind peer review

The most common type of peer review is single-blind (or single anonymized) review . Here, the names of the reviewers are not known by the author.

While this gives the reviewers the ability to give feedback without the possibility of interference from the author, there has been substantial criticism of this method in the last few years. Many argue that single-blind reviewing can lead to poaching or intellectual theft or that anonymized comments cause reviewers to be too harsh.

Double-blind peer review

In double-blind (or double anonymized) review , both the author and the reviewers are anonymous.

Arguments for double-blind review highlight that this mitigates any risk of prejudice on the side of the reviewer, while protecting the nature of the process. In theory, it also leads to manuscripts being published on merit rather than on the reputation of the author.

Triple-blind peer review

While triple-blind (or triple anonymized) review —where the identities of the author, reviewers, and editors are all anonymized—does exist, it is difficult to carry out in practice.

Proponents of adopting triple-blind review for journal submissions argue that it minimizes potential conflicts of interest and biases. However, ensuring anonymity is logistically challenging, and current editing software is not always able to fully anonymize everyone involved in the process.

In collaborative review , authors and reviewers interact with each other directly throughout the process. However, the identity of the reviewer is not known to the author. This gives all parties the opportunity to resolve any inconsistencies or contradictions in real time, and provides them a rich forum for discussion. It can mitigate the need for multiple rounds of editing and minimize back-and-forth.

Collaborative review can be time- and resource-intensive for the journal, however. For these collaborations to occur, there has to be a set system in place, often a technological platform, with staff monitoring and fixing any bugs or glitches.

Lastly, in open review , all parties know each other’s identities throughout the process. Often, open review can also include feedback from a larger audience, such as an online forum, or reviewer feedback included as part of the final published product.

While many argue that greater transparency prevents plagiarism or unnecessary harshness, there is also concern about the quality of future scholarship if reviewers feel they have to censor their comments.

In general, the peer review process includes the following steps:

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to the author, or
• Send it onward to the selected peer reviewer(s)
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
• Lastly, the edited manuscript is sent back to the author. They input the edits and resubmit it to the editor for publication.

In an effort to be transparent, many journals are now disclosing who reviewed each article in the published product. There are also increasing opportunities for collaboration and feedback, with some journals allowing open communication between reviewers and authors.

It can seem daunting at first to conduct a peer review or peer assessment. If you’re not sure where to start, there are several best practices you can use.

Summarize the argument in your own words

Summarizing the main argument helps the author see how their argument is interpreted by readers, and gives you a jumping-off point for providing feedback. If you’re having trouble doing this, it’s a sign that the argument needs to be clearer, more concise, or worded differently.

If the author sees that you’ve interpreted their argument differently than they intended, they have an opportunity to address any misunderstandings when they get the manuscript back.

Separate your feedback into major and minor issues

It can be challenging to keep feedback organized. One strategy is to start out with any major issues and then flow into the more minor points. It’s often helpful to keep your feedback in a numbered list, so the author has concrete points to refer back to.

Major issues typically consist of any problems with the style, flow, or key points of the manuscript. Minor issues include spelling errors, citation errors, or other smaller, easy-to-apply feedback.

Tip: Try not to focus too much on the minor issues. If the manuscript has a lot of typos, consider making a note that the author should address spelling and grammar issues, rather than going through and fixing each one.

The best feedback you can provide is anything that helps them strengthen their argument or resolve major stylistic issues.

Give the type of feedback that you would like to receive

No one likes being criticized, and it can be difficult to give honest feedback without sounding overly harsh or critical. One strategy you can use here is the “compliment sandwich,” where you “sandwich” your constructive criticism between two compliments.

Be sure you are giving concrete, actionable feedback that will help the author submit a successful final draft. While you shouldn’t tell them exactly what they should do, your feedback should help them resolve any issues they may have overlooked.

As a rule of thumb, your feedback should be:

• Easy to understand
• Constructive

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Below is a brief annotated research example. You can view examples of peer feedback by hovering over the highlighted sections.

Influence of phone use on sleep

Studies show that teens from the US are getting less sleep than they were a decade ago (Johnson, 2019) . On average, teens only slept for 6 hours a night in 2021, compared to 8 hours a night in 2011. Johnson mentions several potential causes, such as increased anxiety, changed diets, and increased phone use.

The current study focuses on the effect phone use before bedtime has on the number of hours of sleep teens are getting.

For this study, a sample of 300 teens was recruited using social media, such as Facebook, Instagram, and Snapchat. The first week, all teens were allowed to use their phone the way they normally would, in order to obtain a baseline.

The sample was then divided into 3 groups:

• Group 1 was not allowed to use their phone before bedtime.
• Group 2 used their phone for 1 hour before bedtime.
• Group 3 used their phone for 3 hours before bedtime.

All participants were asked to go to sleep around 10 p.m. to control for variation in bedtime . In the morning, their Fitbit showed the number of hours they’d slept. They kept track of these numbers themselves for 1 week.

Two independent t tests were used in order to compare Group 1 and Group 2, and Group 1 and Group 3. The first t test showed no significant difference ( p > .05) between the number of hours for Group 1 ( M = 7.8, SD = 0.6) and Group 2 ( M = 7.0, SD = 0.8). The second t test showed a significant difference ( p < .01) between the average difference for Group 1 ( M = 7.8, SD = 0.6) and Group 3 ( M = 6.1, SD = 1.5).

This shows that teens sleep fewer hours a night if they use their phone for over an hour before bedtime, compared to teens who use their phone for 0 to 1 hours.

Peer review is an established and hallowed process in academia, dating back hundreds of years. It provides various fields of study with metrics, expectations, and guidance to ensure published work is consistent with predetermined standards.

• Protects the quality of published research

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. Any content that raises red flags for reviewers can be closely examined in the review stage, preventing plagiarized or duplicated research from being published.

• Gives you access to feedback from experts in your field

Peer review represents an excellent opportunity to get feedback from renowned experts in your field and to improve your writing through their feedback and guidance. Experts with knowledge about your subject matter can give you feedback on both style and content, and they may also suggest avenues for further research that you hadn’t yet considered.

• Helps you identify any weaknesses in your argument

Peer review acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process. This way, you’ll end up with a more robust, more cohesive article.

While peer review is a widely accepted metric for credibility, it’s not without its drawbacks.

• Reviewer bias

The more transparent double-blind system is not yet very common, which can lead to bias in reviewing. A common criticism is that an excellent paper by a new researcher may be declined, while an objectively lower-quality submission by an established researcher would be accepted.

• Delays in publication

The thoroughness of the peer review process can lead to significant delays in publishing time. Research that was current at the time of submission may not be as current by the time it’s published. There is also high risk of publication bias , where journals are more likely to publish studies with positive findings than studies with negative findings.

• Risk of human error

By its very nature, peer review carries a risk of human error. In particular, falsification often cannot be detected, given that reviewers would have to replicate entire experiments to ensure the validity of results.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Thematic analysis
• Discourse analysis
• Cohort study
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias
• Social desirability bias

Peer review is a process of evaluating submissions to an academic journal. Utilizing rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication. For this reason, academic journals are often considered among the most credible sources you can use in a research project– provided that the journal itself is trustworthy and well-regarded.

In general, the peer review process follows the following steps: 

• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

A credible source should pass the CRAAP test  and follow these guidelines:

• The information should be up to date and current.
• The author and publication should be a trusted authority on the subject you are researching.
• The sources the author cited should be easy to find, clear, and unbiased.
• For a web source, the URL and layout should signify that it is trustworthy.

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George, T. (2023, June 22). What Is Peer Review? | Types & Examples. Scribbr. Retrieved April 2, 2024, from https://www.scribbr.com/methodology/peer-review/

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How to Know if an Article Is Peer Reviewed

Last Updated: May 16, 2023 Fact Checked

This article was co-authored by Shweta Sharma . Shweta Sharma is a Biologist with the U.S. Environmental Protection Agency (EPA). With nearly ten years of experience, she specializes in insect management, integrated pest management, insect behavior, resistance management, ecology, and biological control. She earned her PhD in Urban Entomology and her MS in Environmental Horticulture from the University of Florida. She also holds a BS in Agriculture from the Institute of Agriculture and Animal Sciences, Nepal. There are 7 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 66,242 times.

For an academic article to be peer-reviewed, journal editors send the article to researchers and scholars in the same field. The reviewers examine the article's research, data, and conclusions, and decide if the article deserves to be published. Peer-reviewed journal articles are more reliable, and should be your go-to for academic research. To determine if an article is peer-reviewed, you can look up the journal in an online database or search the journal's website.

Looking up the Journal in an Online Database

• If you found your article in a catalog, the journal name will be listed there.

• Some databases you can try are Academic Search Premiere, AcademicOneFile, or Ulrich's Periodical Directory.
• If you're not a student, check if your local library provides access. Some cities, counties, and states have libraries that buy access to databases. [3] X Research source

• Another word that is sometimes used is “refereed,” which means the same thing as peer-reviewed.

Finding out from the Journal Website

• If your article is in a paper journal, you can simply check the cover.
• If the article is from a newspaper or a blog, it's not an academic article!

• Peer review is also called “blind peer review,” “scholarly peer review,” and “refereed.” [8] X Research source
• Look for pages with titles like, “about us,” “submission guidelines,” and “editorial policies.”

Expert Q&A

• Dissertations aren't considered peer reviewed, because they are still student work. Thanks Helpful 0 Not Helpful 0

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Expert Interview

Thanks for reading our article! If you’d like to learn more about caring for insects, check out our in-depth interview with Shweta Sharma .

• ↑ https://www.angelo.edu/library/handouts/peerrev.php
• ↑ https://guides.library.oregonstate.edu/c.php?g=285842&p=1906145
• ↑ https://medium.com/a-wikipedia-librarian/youre-a-researcher-without-a-library-what-do-you-do-6811a30373cd
• ↑ https://guides.library.utoronto.ca/peer-review
• ↑ https://guides.library.uq.edu.au/how-to-find/peer-reviewed-articles/check
• ↑ https://bowvalleycollege.libguides.com/c.php?g=10229&p=52137
• ↑ https://academicguides.waldenu.edu/library/verifypeerreview

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Finding Scholarly Articles: Home

What's a Scholarly Article?

Your professor has specified that you are to use scholarly (or primary research or peer-reviewed or refereed or academic) articles only in your paper. What does that mean?

Scholarly or primary research articles are peer-reviewed , which means that they have gone through the process of being read by reviewers or referees  before being accepted for publication. When a scholar submits an article to a scholarly journal, the manuscript is sent to experts in that field to read and decide if the research is valid and the article should be published. Typically the reviewers indicate to the journal editors whether they think the article should be accepted, sent back for revisions, or rejected.

To decide whether an article is a primary research article, look for the following:

• The author’s (or authors') credentials and academic affiliation(s) should be given;
• There should be an abstract summarizing the research;
• The methods and materials used should be given, often in a separate section;
• There are citations within the text or footnotes referencing sources used;
• Results of the research are given;
• There should be discussion   and  conclusion ;
• With a bibliography or list of references at the end.

Caution: even though a journal may be peer-reviewed, not all the items in it will be. For instance, there might be editorials, book reviews, news reports, etc. Check for the parts of the article to be sure.   

You can limit your search results to primary research, peer-reviewed or refereed articles in many databases. To search for scholarly articles in  HOLLIS , type your keywords in the box at the top, and select  Catalog&Articles  from the choices that appear next.   On the search results screen, look for the  Show Only section on the right and click on  Peer-reviewed articles . (Make sure to  login in with your HarvardKey to get full-text of the articles that Harvard has purchased.)

Many of the databases that Harvard offers have similar features to limit to peer-reviewed or scholarly articles.  For example in Academic Search Premier , click on the box for Scholarly (Peer Reviewed) Journals  on the search screen.

Review articles are another great way to find scholarly primary research articles.   Review articles are not considered "primary research", but they pull together primary research articles on a topic, summarize and analyze them.  In Google Scholar , click on Review Articles  at the left of the search results screen. Ask your professor whether review articles can be cited for an assignment.

A note about Google searching.  A regular Google search turns up a broad variety of results, which can include scholarly articles but Google results also contain commercial and popular sources which may be misleading, outdated, etc.  Use Google Scholar  through the Harvard Library instead.

About Wikipedia .  W ikipedia is not considered scholarly, and should not be cited, but it frequently includes references to scholarly articles. Before using those references for an assignment, double check by finding them in Hollis or a more specific subject  database .

Still not sure about a source? Consult the course syllabus for guidance, contact your professor or teaching fellow, or use the Ask A Librarian service.

• Last Updated: Oct 3, 2023 3:37 PM
• URL: https://guides.library.harvard.edu/FindingScholarlyArticles

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How to know if an article is peer reviewed [6 key features]

Features of a peer reviewed article

How to find peer reviewed articles, frequently asked questions about peer reviewed articles, related articles.

A peer reviewed article refers to a work that has been thoroughly assessed, and based on its quality, has been accepted for publication in a scholarly journal. The aim of peer reviewing is to publish articles that meet the standards established in each field. This way, peer reviewed articles that are published can be taken as models of research practices.

A peer reviewed article can be recognized by the following features:

• It is published in a scholarly journal.
• It has a serious, and academic tone.
• It features an abstract at the beginning.
• It is divided by headings into introduction, literature review or background, discussion, and conclusion.
• It includes in-text citations, and a bibliography listing accurately all references.
• Its authors are affiliated with a research institute or university.

There are many ways in which you can find peer reviewed articles, for instance:

• Check the journal's features and 'About' section. This part should state if the articles published in the journal are peer reviewed, and the type of reviewing they perform.
• Consult a database with peer reviewed journals, such as Web of Science Master Journal List , PubMed , Scopus , Google Scholar , etc. Specify in the advanced search settings that you are looking for peer reviewed journals only.
• Consult your library's database, and specify in the search settings that you are looking for peer reviewed journals only.

➡️ Want to know if a source is scholarly? Check out our guide on scholarly sources.

➡️ Want to know if a source is credible? Find out in our guide on credible sources (+ how to find them).

A peer reviewed article refers to a work that has been thoroughly assessed, and based on its quality has been accepted to be published in a scholarly journal.

Once an article has been submitted for publication to a peer reviewed journal, the journal assigns the article to an expert in the field, who is considered the “peer”.

The easiest way to find a peer reviewed article is to narrow down the search in the "Advanced search" option. Then, mark the box that says "peer reviewed".

Consult a database with peer reviewed journals, such as Web of Science Master Journal List , PubMed , Scopus , etc.

There are many views on peer reviewed articles. Take a look at Peer Review in Scientific Publications: Benefits, Critiques, & A Survival Guide for more insight on this topic.

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I do not know if this article is scholarly or peer-reviewed

Scholarly articles (also known as  academic articles ) are written by experts in a discipline for other experts in that field. They're usually published by a professional association or academic press. Their content focuses on research, has citations (like a bibliography or footnotes), and are professional in appearance with no spelling or grammatical errors, advertisements, or unrelated images.

Some scholarly articles go a bit further to be peer-reviewed . All peer-reviewed articles are scholarly articles, but not all scholarly articles are peer-reviewed.

NOTE : An article can be from a peer reviewed journal and not actually be peer reviewed. Editorials, news items, and book reviews do not necessarily go through the same review process. A peer reviewed article should be longer than just a couple of pages and include a bibliography.

There are several ways to determine whether or not an article is peer reviewed  (also called  refereed ).

 1 .  If you found the article using OneSearch , it will have a peer-reviewed icon:

2.  If you found the article in a library database, there may be some indicator as to whether the article is peer reviewed.

3.  In the library databases, you might find that the journal name is a hyperlink as shown below. Clicking on it takes you to a page about the journal which should make it clear whether the journal is scholarly, academic, peer reviewed, or refereed.

4.  You can look up the journal name in the library database called Ulrichs Web: Global Serials Directory   (previously called Ulrichs Periodical Directory). Search for the journal title and find the correct entry in the results list. There may be multiple versions of the same journal--print, online, and microfilm formats--but there also may be two different journals with the same title.

Look to left of the title, and if you find a referee shirt icon , that means that the journal is peer-reviewed or refereed.  

5.  The publisher's website for the journal should indicate whether articles go through a peer review process. Find the instructions for authors page for this information.

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Finding Journal Articles 101

Peer-reviewed or refereed.

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What Does "Peer-reviewed" or "Refereed" Mean?

Peer review is a process that journals use to ensure the articles they publish represent the best scholarship currently available. When an article is submitted to a peer reviewed journal, the editors send it out to other scholars in the same field (the author's peers) to get their opinion on the quality of the scholarship, its relevance to the field, its appropriateness for the journal, etc.

Publications that don't use peer review (Time, Cosmo, Salon) just rely on the judgment of the editors whether an article is up to snuff or not. That's why you can't count on them for solid, scientific scholarship.

Note:This is an entirely different concept from " Review Articles ."

How do I know if a journal publishes peer-reviewed articles?

Usually, you can tell just by looking. A scholarly journal is visibly different from other magazines, but occasionally it can be hard to tell, or you just want to be extra-certain. In that case, you turn to Ulrich's Periodical Directory Online . Just type the journal's title into the text box, hit "submit," and you'll get back a report that will tell you (among other things) whether the journal contains articles that are peer reviewed, or, as Ulrich's calls it, Refereed.

Remember, even journals that use peer review may have some content that does not undergo peer review. The ultimate determination must be made on an article-by-article basis.

For example, the journal  Science  publishes  a mix  of peer-reviewed and non-peer-reviewed content. Here are two articles from the same issue of  Science . 

This one is not peer-reviewed:  https://science-sciencemag-org.ezproxy.lib.utexas.edu/content/303/5655/154.1  This one is a peer-reviewed research article:  https://science-sciencemag-org.ezproxy.lib.utexas.edu/content/303/5655/226

That is consistent with the Ulrichsweb  description of  Science , which states, "Provides news of recent international developments and research in all fields of science. Publishes original research results, reviews and short features."

Test these periodicals in Ulrichs :

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FAQ: How do I know if my articles are scholarly (peer-reviewed)?

How to identify a scholarly, peer-reviewed journal article, what are scholarly, peer-reviewed journal articles.

Scholarly articles are those that are reviewed by multiple experts from their related  field(s) and then published in academic journals. There are academic journals for every subject area. The primary purpose of scholarly journals is to represent and disseminate research and scholarly discussions among scholars (faculty, researchers, students) within, and across, different academic disciplines.  

Scholarly peer-reviewed journal articles can be identified by the following characteristics:

• Author(s): They are typically written by professors, researchers, or other scholars who specialize in the field and are often  identified by the academic institution at which they work.
• Purpose : They are published by professional associations, university publishers or other academic publishers  to  report research results or discuss ongoing research in detail.
• Language: They are highly specialized and may use technical language.
• Layout: They will cite their sources and include footnotes, endnotes, or parenthetical citations and/or a list of bibliographic references.
• Content : They may include graphs and tables and they undergo a peer review process before publication.  

Helpful tips for finding scholarly articles:

What is a Scholarly Journal Article?

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What are Peer-Reviewed Journals?

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• What are Peer-reviewed Articles and How Do I Find Them? From Capella University Libraries

Introduction

Peer-reviewed journals (also called scholarly or refereed journals) are a key information source for your college papers and projects. They are written by scholars for scholars and are an reliable source for information on a topic or discipline. These journals can be found either in the library's online databases, or in the library's local holdings. This guide will help you identify whether a journal is peer-reviewed and show you tips on finding them.

What is Peer-Review?

Peer-review is a process where an article is verified by a group of scholars before it is published.

When an author submits an article to a peer-reviewed journal, the editor passes out the article to a group of scholars in the related field (the author's peers). They review the article, making sure that its sources are reliable, the information it presents is consistent with the research, etc. Only after they give the article their "okay" is it published.

The peer-review process makes sure that only quality research is published: research that will further the scholarly work in the field.

When you use articles from peer-reviewed journals, someone has already reviewed the article and said that it is reliable, so you don't have to take the steps to evaluate the author or his/her sources. The hard work is already done for you!

Identifying Peer-Review Journals

If you have the physical journal, you can look for the following features to identify if it is peer-reviewed.

Masthead (The first few pages) : includes information on the submission process, the editorial board, and maybe even a phrase stating that the journal is "peer-reviewed."

Publisher: Peer-reviewed journals are typically published by professional organizations or associations (like the American Chemical Society). They also may be affiliated with colleges/universities.

Graphics:  Typically there either won't be any images at all, or the few charts/graphs are only there to supplement the text information. They are usually in black and white.

Authors: The authors are listed at the beginning of the article, usually with information on their affiliated institutions, or contact information like email addresses.

Abstracts: At the beginning of the article the authors provide an extensive abstract detailing their research and any conclusions they were able to draw.

Terminology:  Since the articles are written by scholars for scholars, they use uncommon terminology specific to their field and typically do not define the words used.

Citations: At the end of each article is a list of citations/reference. These are provided for scholars to either double check their work, or to help scholars who are researching in the same general area.

Advertisements: Peer-reviewed journals rarely have advertisements. If they do the ads are for professional organizations or conferences, not for national products.

Identifying Articles from Databases

When you are looking at an article in an online database, identifying that it comes from a peer-reviewed journal can be more difficult. You do not have access to the physical journal to check areas like the masthead or advertisements, but you can use some of the same basic principles.

Points you may want to keep in mind when you are evaluating an article from a database:

• A lot of databases provide you with the option to limit your results to only those from peer-reviewed or refereed journals. Choosing this option means all of your results will be from those types of sources.  
• When possible, choose the PDF version of the article's full text. Since this is exactly as if you photocopied from the journal, you can get a better idea of its layout, graphics, advertisements, etc.  
• Even in an online database you still should be able to check for author information, abstracts, terminology, and citations.
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The Peer Review Process

So you need to use scholarly, peer-reviewed articles for an assignment...what does that mean? 

Peer review  is a process for evaluating research studies before they are published by an academic journal. These studies typically communicate  original research  or analysis for other researchers. 

The Peer Review Process at a Glance:

Looking for peer-reviewed articles?  Try searching in OneSearch or a library database  and look for options to limit your results to scholarly/peer-reviewed or academic journals. Check out this brief tutorial to show you how:   How to Locate a Scholarly (Peer Reviewed) Article

Part 1: Watch the Video

Part 1: watch the video all about peer review (3 min.) and reflect on discussion questions..

Discussion Questions

After watching the video, reflect on the following questions:

• According to the video, what are some of the pros and cons of the peer review process?
• Why is the peer review process important to scholarship?
• Do you think peer reviewers should be paid for their work? Why or why not?

Part 2: Practice

Part 2: take an interactive tutorial on reading a research article for your major..

Includes a certification of completion to download and upload to Canvas.

Social Sciences

(e.g. Psychology, Sociology)

(e.g. Health Science, Biology)

Arts & Humanities

(e.g. Visual & Media Arts, Cultural Studies, Literature, History)

Click on the handout to view in a new tab, download, or print.

For Instructors

• Teaching Peer Review for Instructors

In class or for homework, watch the video “All About Peer Review” (3 min.) .

Video discussion questions:

• According to the video, what are some of the pros and cons of the peer review process

Assignment Ideas

• Ask students to conduct their own peer review of an important journal article in your field. Ask them to reflect on the process. What was hard to critique?
• Have students examine a journals’ web page with information for authors. What information is given to the author about the peer review process for this journal?
• Assign this reading by CSUDH faculty member Terry McGlynn, "Should journals pay for manuscript reviews?" What is the author's argument? Who profits the most from published research? You could also hold a debate with one side for paying reviewers and the other side against.
• Search a database like Cabell’s for information on the journal submission process for a particular title or subject. How long does peer review take for a particular title? Is it is a blind review? How many reviewers are solicited? What is their acceptance rate?
• Assign short readings that address peer review models. We recommend this issue of Nature on peer review debate and open review and this Chronicle of Higher Education article on open review in Shakespeare Quarterly .

Proof of Completion

Mix and match this suite of instructional materials for your course needs!

Questions about integrating a graded online component into your class, contact the Online Learning Librarian, Rebecca Nowicki ( [email protected] ).

Example of a certificate of completion:

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Frequently Asked Questions

What is the difference between peer-reviewed (scholarly) articles and everything else.

Peer-reviewed articles, also known as scholarly articles, are published based on the approval of a board of professional experts in the discipline relating to the article topic.

For instance, a paper discussing the psychological effects of homeschooling a child would need to be reviewed by a board of psychology scholars and professional psychologists in order to be approved for publication in a psychology journal.

Scholarly/peer-reviewed articles differ from other easily available print sources because the review process gives them more authority than, for example, a newspaper or magazine article.

Newspaper or popular magazine articles are written by journalists (not specialists in any field except journalism).

They are reviewed only by the magazine/newspaper editors (also not specialists in any field except editing).

For more information, see:  https://wrtg150.lib.byu.edu/finding-sources .

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Evaluating Resources: Research Articles

Research articles.

A research article is a journal article in which the authors report on the research they did. Research articles are always primary sources. Whether or not a research article is peer reviewed depends on the journal that publishes it.

Published research articles follow a predictable pattern and will contain most, if not all, of the sections listed below. However, the names for these sections may vary.

• Title & Author(s)
• Introduction
• Methodology

To learn about the different parts of a research article, please view this tutorial:

Short video: How to Read Scholarly Articles

Learn some tips on how to efficiently read scholarly articles.

Video: How to Read a Scholarly Article

(4 min 16 sec) Recorded August 2019 Transcript 

More information

The Academic Skills Center and the Writing Center both have helpful resources on critical and academic reading that can further help you understand and evaluate research articles.

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• Writing Center Podcast: WriteCast Episode 5: Five Strategies for Critical Reading

If you'd like to learn how to find research articles in the Library, you can view this Quick Answer.

• Quick Answer: How do I find research articles?
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What is Peer Review?

If an article is peer reviewed , it was reviewed by scholars who are experts in related academic or professional fields before it was published. Those scholars assessed the quality of the article's research, as well as its overall contribution to the literature in their field. 

When we talk about peer-reviewed journals , we're referring to journals that use a peer-review process.

Related terms you might hear include: 

• Academic: Intended for academic use, or an academic audience. 
• Scholarly:  Intended for scholarly use, or a scholarly audience. 
• Refereed: Refers to a specific kind of peer-review process. 

National University Library System. (2018). "Find Articles: How to Find Scholarly/Peer-Reviewed Articles". Retrieved from: http://nu.libguides.com/articles/PR.

How Do I Know If a Journal is Peer-Reviewed?

The easiest way to find out if a journal is peer-reviewed is to search for the title in a serials directory like UlrichsWeb:

• UlrichsWeb Global Serials Directory Includes in each record: ISBN, title, publisher, country of publication, status (Active, ceased, etc.), start year, frequency, refereed (Yes/No), media, language, price, subject, Dewey #, circulation, editor(s), email, URL, brief description Also known as: Ulrichs
• Type the name of the journal in the search bar and click the search button. NOTE: you need to use the full name of the journal, not an abbreviation.

• Locate the journal in the results list. You may see multiple entries for one journal because Ulrichs lists print, electronic, and international version separately.

How Do I Know If an Article is Peer-Reviewed?

Even if an article was published in a peer-reviewed journal, it may not necessarily be peer-reviewed itself; for example, a commentary article may undergo editorial review instead, meaning it was only reviewed by the journal editor.

There are some clues you can look for to help you identify if an article is peer-reviewed:

• Does the abstract discuss the author's/authors' research process?
• Does the abstract include a variation of the phrase "This study..."?
• Is there a Methodology or Data header in the text of the article?
• Does the paper discuss related research in a literature review?
• Is there an analysis of a need for further research, or gaps in the literature?
• Are the references for scholarly articles and books?

If an article published in a verified peer-reviewed journal includes these elements, it is most likely a peer-reviewed article.

• National University Library: Scholarly Checklist Use this printable checklist to help you identify scholarly, research-based articles

Peer Reviewed Material in PubMed & MEDLINE

Good news! Most of the journals in Medline/PubMed are peer reviewed.  Generally speaking, if you find a journal citation in Medline/PubMed you should be just fine. However, there is no way to limit your results within the PubMed or the Medline on EBSCO interface to knock out the few publications that are not considered refereed titles.

However, EBSCO (a third-party vendor) does provide a list of all titles within Medline and lets you see which titles are considered peer reviewed. You can check if your journal is OK - see the "peer Review" tab in the report below to see the very small list of titles that don't make the cut.

• Medline: List of Full-Text Journals These journals cover a wide range of subjects within the biomedical and health fields containing information needed by doctors, nurses, health professionals, and researchers engaged in clinical care, public health, and health policy development. Information on peer-reviewed status available within table of titles.

Peer Reviewed Material in CINAHL and PsycINFO

Both The Cumulative Index to Nursing and Allied Health Literature (CINAHL) & PsycInfo databases, feature a Peer Reviewed subset.  You can limit your search from the main search screen by checking the "Peer Review" box.

• What is the definition of peer review for the CINAHL products?
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Peer review guidance: a primer for researchers

Olena zimba.

1 Department of Internal Medicine No. 2, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine

Armen Yuri Gasparyan

2 Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, West Midlands, UK

The peer review process is essential for quality checks and validation of journal submissions. Although it has some limitations, including manipulations and biased and unfair evaluations, there is no other alternative to the system. Several peer review models are now practised, with public review being the most appropriate in view of the open science movement. Constructive reviewer comments are increasingly recognised as scholarly contributions which should meet certain ethics and reporting standards. The Publons platform, which is now part of the Web of Science Group (Clarivate Analytics), credits validated reviewer accomplishments and serves as an instrument for selecting and promoting the best reviewers. All authors with relevant profiles may act as reviewers. Adherence to research reporting standards and access to bibliographic databases are recommended to help reviewers draft evidence-based and detailed comments.

Introduction

The peer review process is essential for evaluating the quality of scholarly works, suggesting corrections, and learning from other authors’ mistakes. The principles of peer review are largely based on professionalism, eloquence, and collegiate attitude. As such, reviewing journal submissions is a privilege and responsibility for ‘elite’ research fellows who contribute to their professional societies and add value by voluntarily sharing their knowledge and experience.

Since the launch of the first academic periodicals back in 1665, the peer review has been mandatory for validating scientific facts, selecting influential works, and minimizing chances of publishing erroneous research reports [ 1 ]. Over the past centuries, peer review models have evolved from single-handed editorial evaluations to collegial discussions, with numerous strengths and inevitable limitations of each practised model [ 2 , 3 ]. With multiplication of periodicals and editorial management platforms, the reviewer pool has expanded and internationalized. Various sets of rules have been proposed to select skilled reviewers and employ globally acceptable tools and language styles [ 4 , 5 ].

In the era of digitization, the ethical dimension of the peer review has emerged, necessitating involvement of peers with full understanding of research and publication ethics to exclude unethical articles from the pool of evidence-based research and reviews [ 6 ]. In the time of the COVID-19 pandemic, some, if not most, journals face the unavailability of skilled reviewers, resulting in an unprecedented increase of articles without a history of peer review or those with surprisingly short evaluation timelines [ 7 ].

Editorial recommendations and the best reviewers

Guidance on peer review and selection of reviewers is currently available in the recommendations of global editorial associations which can be consulted by journal editors for updating their ethics statements and by research managers for crediting the evaluators. The International Committee on Medical Journal Editors (ICMJE) qualifies peer review as a continuation of the scientific process that should involve experts who are able to timely respond to reviewer invitations, submitting unbiased and constructive comments, and keeping confidentiality [ 8 ].

The reviewer roles and responsibilities are listed in the updated recommendations of the Council of Science Editors (CSE) [ 9 ] where ethical conduct is viewed as a premise of the quality evaluations. The Committee on Publication Ethics (COPE) further emphasizes editorial strategies that ensure transparent and unbiased reviewer evaluations by trained professionals [ 10 ]. Finally, the World Association of Medical Editors (WAME) prioritizes selecting the best reviewers with validated profiles to avoid substandard or fraudulent reviewer comments [ 11 ]. Accordingly, the Sarajevo Declaration on Integrity and Visibility of Scholarly Publications encourages reviewers to register with the Open Researcher and Contributor ID (ORCID) platform to validate and publicize their scholarly activities [ 12 ].

Although the best reviewer criteria are not listed in the editorial recommendations, it is apparent that the manuscript evaluators should be active researchers with extensive experience in the subject matter and an impressive list of relevant and recent publications [ 13 ]. All authors embarking on an academic career and publishing articles with active contact details can be involved in the evaluation of others’ scholarly works [ 14 ]. Ideally, the reviewers should be peers of the manuscript authors with equal scholarly ranks and credentials.

However, journal editors may employ schemes that engage junior research fellows as co-reviewers along with their mentors and senior fellows [ 15 ]. Such a scheme is successfully practised within the framework of the Emerging EULAR (European League Against Rheumatism) Network (EMEUNET) where seasoned authors (mentors) train ongoing researchers (mentees) how to evaluate submissions to the top rheumatology journals and select the best evaluators for regular contributors to these journals [ 16 ].

The awareness of the EQUATOR Network reporting standards may help the reviewers to evaluate methodology and suggest related revisions. Statistical skills help the reviewers to detect basic mistakes and suggest additional analyses. For example, scanning data presentation and revealing mistakes in the presentation of means and standard deviations often prompt re-analyses of distributions and replacement of parametric tests with non-parametric ones [ 17 , 18 ].

Constructive reviewer comments

The main goal of the peer review is to support authors in their attempt to publish ethically sound and professionally validated works that may attract readers’ attention and positively influence healthcare research and practice. As such, an optimal reviewer comment has to comprehensively examine all parts of the research and review work ( Table I ). The best reviewers are viewed as contributors who guide authors on how to correct mistakes, discuss study limitations, and highlight its strengths [ 19 ].

Structure of a reviewer comment to be forwarded to authors

Some of the currently practised review models are well positioned to help authors reveal and correct their mistakes at pre- or post-publication stages ( Table II ). The global move toward open science is particularly instrumental for increasing the quality and transparency of reviewer contributions.

Advantages and disadvantages of common manuscript evaluation models

Since there are no universally acceptable criteria for selecting reviewers and structuring their comments, instructions of all peer-reviewed journal should specify priorities, models, and expected review outcomes [ 20 ]. Monitoring and reporting average peer review timelines is also required to encourage timely evaluations and avoid delays. Depending on journal policies and article types, the first round of peer review may last from a few days to a few weeks. The fast-track review (up to 3 days) is practised by some top journals which process clinical trial reports and other priority items.

In exceptional cases, reviewer contributions may result in substantive changes, appreciated by authors in the official acknowledgments. In most cases, however, reviewers should avoid engaging in the authors’ research and writing. They should refrain from instructing the authors on additional tests and data collection as these may delay publication of original submissions with conclusive results.

Established publishers often employ advanced editorial management systems that support reviewers by providing instantaneous access to the review instructions, online structured forms, and some bibliographic databases. Such support enables drafting of evidence-based comments that examine the novelty, ethical soundness, and implications of the reviewed manuscripts [ 21 ].

Encouraging reviewers to submit their recommendations on manuscript acceptance/rejection and related editorial tasks is now a common practice. Skilled reviewers may prompt the editors to reject or transfer manuscripts which fall outside the journal scope, perform additional ethics checks, and minimize chances of publishing erroneous and unethical articles. They may also raise concerns over the editorial strategies in their comments to the editors.

Since reviewer and editor roles are distinct, reviewer recommendations are aimed at helping editors, but not at replacing their decision-making functions. The final decisions rest with handling editors. Handling editors weigh not only reviewer comments, but also priorities related to article types and geographic origins, space limitations in certain periods, and envisaged influence in terms of social media attention and citations. This is why rejections of even flawless manuscripts are likely at early rounds of internal and external evaluations across most peer-reviewed journals.

Reviewers are often requested to comment on language correctness and overall readability of the evaluated manuscripts. Given the wide availability of in-house and external editing services, reviewer comments on language mistakes and typos are categorized as minor. At the same time, non-Anglophone experts’ poor language skills often exclude them from contributing to the peer review in most influential journals [ 22 ]. Comments should be properly edited to convey messages in positive or neutral tones, express ideas of varying degrees of certainty, and present logical order of words, sentences, and paragraphs [ 23 , 24 ]. Consulting linguists on communication culture, passing advanced language courses, and honing commenting skills may increase the overall quality and appeal of the reviewer accomplishments [ 5 , 25 ].

Peer reviewer credits

Various crediting mechanisms have been proposed to motivate reviewers and maintain the integrity of science communication [ 26 ]. Annual reviewer acknowledgments are widely practised for naming manuscript evaluators and appreciating their scholarly contributions. Given the need to weigh reviewer contributions, some journal editors distinguish ‘elite’ reviewers with numerous evaluations and award those with timely and outstanding accomplishments [ 27 ]. Such targeted recognition ensures ethical soundness of the peer review and facilitates promotion of the best candidates for grant funding and academic job appointments [ 28 ].

Also, large publishers and learned societies issue certificates of excellence in reviewing which may include Continuing Professional Development (CPD) points [ 29 ]. Finally, an entirely new crediting mechanism is proposed to award bonus points to active reviewers who may collect, transfer, and use these points to discount gold open-access charges within the publisher consortia [ 30 ].

With the launch of Publons ( http://publons.com/ ) and its integration with Web of Science Group (Clarivate Analytics), reviewer recognition has become a matter of scientific prestige. Reviewers can now freely open their Publons accounts and record their contributions to online journals with Digital Object Identifiers (DOI). Journal editors, in turn, may generate official reviewer acknowledgments and encourage reviewers to forward them to Publons for building up individual reviewer and journal profiles. All published articles maintain e-links to their review records and post-publication promotion on social media, allowing the reviewers to continuously track expert evaluations and comments. A paid-up partnership is also available to journals and publishers for automatically transferring peer-review records to Publons upon mutually acceptable arrangements.

Listing reviewer accomplishments on an individual Publons profile showcases scholarly contributions of the account holder. The reviewer accomplishments placed next to the account holders’ own articles and editorial accomplishments point to the diversity of scholarly contributions. Researchers may establish links between their Publons and ORCID accounts to further benefit from complementary services of both platforms. Publons Academy ( https://publons.com/community/academy/ ) additionally offers an online training course to novice researchers who may improve their reviewing skills under the guidance of experienced mentors and journal editors. Finally, journal editors may conduct searches through the Publons platform to select the best reviewers across academic disciplines.

Peer review ethics

Prior to accepting reviewer invitations, scholars need to weigh a number of factors which may compromise their evaluations. First of all, they are required to accept the reviewer invitations if they are capable of timely submitting their comments. Peer review timelines depend on article type and vary widely across journals. The rules of transparent publishing necessitate recording manuscript submission and acceptance dates in article footnotes to inform readers of the evaluation speed and to help investigators in the event of multiple unethical submissions. Timely reviewer accomplishments often enable fast publication of valuable works with positive implications for healthcare. Unjustifiably long peer review, on the contrary, delays dissemination of influential reports and results in ethical misconduct, such as plagiarism of a manuscript under evaluation [ 31 ].

In the times of proliferation of open-access journals relying on article processing charges, unjustifiably short review may point to the absence of quality evaluation and apparently ‘predatory’ publishing practice [ 32 , 33 ]. Authors when choosing their target journals should take into account the peer review strategy and associated timelines to avoid substandard periodicals.

Reviewer primary interests (unbiased evaluation of manuscripts) may come into conflict with secondary interests (promotion of their own scholarly works), necessitating disclosures by filling in related parts in the online reviewer window or uploading the ICMJE conflict of interest forms. Biomedical reviewers, who are directly or indirectly supported by the pharmaceutical industry, may encounter conflicts while evaluating drug research. Such instances require explicit disclosures of conflicts and/or rejections of reviewer invitations.

Journal editors are obliged to employ mechanisms for disclosing reviewer financial and non-financial conflicts of interest to avoid processing of biased comments [ 34 ]. They should also cautiously process negative comments that oppose dissenting, but still valid, scientific ideas [ 35 ]. Reviewer conflicts that stem from academic activities in a competitive environment may introduce biases, resulting in unfair rejections of manuscripts with opposing concepts, results, and interpretations. The same academic conflicts may lead to coercive reviewer self-citations, forcing authors to incorporate suggested reviewer references or face negative feedback and an unjustified rejection [ 36 ]. Notably, several publisher investigations have demonstrated a global scale of such misconduct, involving some highly cited researchers and top scientific journals [ 37 ].

Fake peer review, an extreme example of conflict of interest, is another form of misconduct that has surfaced in the time of mass proliferation of gold open-access journals and publication of articles without quality checks [ 38 ]. Fake reviews are generated by manipulating authors and commercial editing agencies with full access to their own manuscripts and peer review evaluations in the journal editorial management systems. The sole aim of these reviews is to break the manuscript evaluation process and to pave the way for publication of pseudoscientific articles. Authors of these articles are often supported by funds intended for the growth of science in non-Anglophone countries [ 39 ]. Iranian and Chinese authors are often caught submitting fake reviews, resulting in mass retractions by large publishers [ 38 ]. Several suggestions have been made to overcome this issue, with assigning independent reviewers and requesting their ORCID IDs viewed as the most practical options [ 40 ].

Conclusions

The peer review process is regulated by publishers and editors, enforcing updated global editorial recommendations. Selecting the best reviewers and providing authors with constructive comments may improve the quality of published articles. Reviewers are selected in view of their professional backgrounds and skills in research reporting, statistics, ethics, and language. Quality reviewer comments attract superior submissions and add to the journal’s scientific prestige [ 41 ].

In the era of digitization and open science, various online tools and platforms are available to upgrade the peer review and credit experts for their scholarly contributions. With its links to the ORCID platform and social media channels, Publons now offers the optimal model for crediting and keeping track of the best and most active reviewers. Publons Academy additionally offers online training for novice researchers who may benefit from the experience of their mentoring editors. Overall, reviewer training in how to evaluate journal submissions and avoid related misconduct is an important process, which some indexed journals are experimenting with [ 42 ].

The timelines and rigour of the peer review may change during the current pandemic. However, journal editors should mobilize their resources to avoid publication of unchecked and misleading reports. Additional efforts are required to monitor published contents and encourage readers to post their comments on publishers’ online platforms (blogs) and other social media channels [ 43 , 44 ].

The authors declare no conflict of interest.

Evaluating Information Sources: What Is A Peer-Reviewed Article?

• Should I Trust Internet Sources?

What Is A Peer-Reviewed Article?

Anali Perry, a librarian from Arizona State University Libraries, gives a quick definition of a peer-reviewed article.

The Library Minute: Academic Articles from ASU Libraries on Vimeo .

How Do Peer-Reviewed Articles Differ From Popular Ones?

This 3 minute video from the Peabody Library at Vanderbilt University talks about the differences between popular and scholarly articles.  It also mentions trade publications. 

What Is Peer Review?

In academic publishing, the goal of peer review is to assess the quality of articles submitted for publication in a scholarly journal. Before an article is deemed appropriate to be published in a peer-reviewed journal, it must undergo the following process:

• The author of the article must submit it to the journal editor who forwards the article to experts in the field. Because the reviewers specialize in the same scholarly area as the author, they are considered the author’s peers (hence “peer review”).
• These impartial reviewers are charged with carefully evaluating the quality of the submitted manuscript.
• The peer reviewers check the manuscript for accuracy and assess the validity of the research methodology and procedures.
• If appropriate, they suggest revisions. If they find the article lacking in scholarly validity and rigor, they reject it.

Because a peer-reviewed journal will not publish articles that fail to meet the standards established for a given discipline, peer-reviewed articles that are accepted for publication exemplify the best research practices in a field.

Features of a Peer-Reviewed Article

When you are determining whether or not the article you found is a peer-reviewed article, you should consider the following.

Does the article have the following features?

Also consider...

• Is the journal in which you found the article published or sponsored by a professional scholarly society, professional association, or university academic department? Does it describe itself as a peer-reviewed publication? (To know that, check the journal's website). 
• Did you find a citation for it in one of the  databases that includes scholarly publications? (Academic Search Complete, PsycINFO, etc.)?  Read the database description to see if it includes scholarly publications.
• In the database, did you limit your search to scholarly or peer-reviewed publications? (See video tutorial below for a demonstration.)
• Is the topic of the article narrowly focused and explored in depth ?
• Is the article based on either original research or authorities in the field (as opposed to personal opinion)?
• Is the article written for readers with some prior knowledge of the subject?
• If your field is social or natural science, is the article divided into sections with headings such as those listed below?

How Do I Find Peer-Reviewed Articles?

The easiest and fastest way to find peer-reviewed articles is to search the online library databases , many of which include peer-reviewed journals. To make sure your results come from peer-reviewed (also called "scholarly" or "academic") journals, do the following:

• Read the database description to determine if it features peer-reviewed articles.
• When you search for articles, choose the Advanced Search option. On the search screen, look for a check-box that allows you to limit your results to peer-reviewed only.
• If you didn't check off the "peer-reviewed articles only" box, try to see if your results can organized by source . For example, the database Criminal Justice Abstracts will let you choose the tab "Peer-Reviewed Journals."

Video tutorial

Watch this video through to the end. It will show you how to use a library database and how to narrow your search results down to just peer-reviewed articles.

• << Previous: Should I Trust Internet Sources?
• Last Updated: Aug 21, 2019 2:00 PM
• URL: https://guides.lib.jjay.cuny.edu/evaluatingsources

• Earl K. Long Library
• Library Guides
• ENGL 1158--Sullivan Spring 2024
• Evaluating Your Sources

ENGL 1158--Sullivan Spring 2024: Evaluating Your Sources

• Narrowing a Topic
• Citation Help
• Last Updated: Apr 3, 2024 12:27 PM
• URL: https://libguides.uno.edu/sullivan_1158

Tools You Can Use to Evaluate Your Sources

• Did I Find a Peer-Reviewed Article?
• The CRAAP Test
• Determining Relevance & Reliability of Sources

Popular vs Scholarly aka Peer-Reviewed

One of the big differences between the research you did last year in high school and what you're expected to do during your college career can be summed in one phrase  you will see and hear frequently, "Please use 3-5 scholarly peer-reviewed sources in your paper". 

What does this mean?!

Universities are special places. Shipyards build ships, we build educated minds. One of the skills you will develop in college is how to read the literature that is produced by the scholars (and future scholars) in your discipline: biology, English, sociology, and more. It takes a lot of practice to learn to read thoroughly, carefully, and then analyze what you're reading. THEN you're asked to synthesize what you've read and heard in classes with what you think in order to produce an essay or other project.  This is the path to becoming a scholar--someone with depth of knowledge who can write clearly in their subject area with a curiosity for exploring ideas. This training helps produce an 'educated mind'. 

So, learning to distinguish between the everyday kind of knowledge we all consume--tv news, Newsweek, the Times-Picayune from  an article about an experiment on rats detailing the effects of food additives with possible effects on human nutrition becomes a worthwhile skill to have. 

Compare and Contrast

One of the key functions of scholarship is to  evaluate information . information (books, journals, websites, etc.) is often classified as  scholarly  or  popular..

Scholarly information is most often produced by scholars for other scholars. The vocabulary used  is specialized and expert. The intended audience for this material generally has or is acquiring advanced knowledge. The author of such work is an expert in his/her field; usually with advanced degrees, most commonly a PhD. The publication has been  peer-reviewed. And the sources for the work are always cited. 

Popular works are intended for a general audience of readers. Advanced knowledge is not usually required to read and use this material. The author may not be a recognized expert or practitioner in her/her field. The work has not gone through a rigorous peer-review process. The sources for such a work may not be cited at the the end of the work.

Check out these two examples below to see the differences in action. Read the abstracts. To the left of the abstracts, you will see pdfs; click on each to see the complete article. Which one is scholarly and which one is popular?

Article A 

Scholarly vs Peer-Reviewed Articles

Quizlet on Peer Review

  Here's a quiz that will let you know if you can recognize peer-reviewed materials . No sweat, you got this!

• << Previous: Narrowing a Topic
• Next: Citation Help >>

• Policy & Compliance
• Peer Review Policies and Practices
• Simplifying Review of Research Project Grant Applications
• Applicant Guidance For Simplifying The Review Framework For Most Research Project Grants

Applicant Guidance for Simplifying the Review Framework for Most Research Project Grants

Although the simplified review framework has little impact on what is included in an application, it does have significant impact on the funding opportunities used to apply. This page provides practical guidance for applicants navigating funding opportunities through this transition.

How to Tell if Your Application Will be Impacted

Simplified peer review applies to most, but not all research project grants (RPGs). For example, none of our small business or complex, multi-project grants are included in this initiative.

Applicability

• Activity codes: R01, R03, R15, R16, R21, R33, R34, R36, R61, RC1, RC2, RC4, RF1, RL1, RL2, U01, U34, U3R, UA5, UC1, UC2, UC4, UF1, UG3, UH2, UH3, UH5, (including the following phased awards: R21/R33, UH2/UH3, UG3/UH3, R61/R33).
• Applications submitted to due dates on or after January 25, 2025.

Tips for Applicants

• Funding opportunities with a mix of due dates before and after January 25, 2025 may be reissued and/or expired early since a single opportunity cannot accommodate two sets of review information.

Ensuring You Are Applying to the Right Funding Opportunity Using the Correct Forms

Need to move an existing application to apply to a different funding opportunity.

Take advantage of copy features in ASSIST, Grants.gov Workspace, and many institutional submission systems.

ASSIST Online Help: Copy Application

Grants.gov Online Help: Copy Workspace

As NIH implements the simplified review framework, you will find funding opportunities being expired and/or reissued to ensure applicants are presented with the correct review information for their target due date.

While there are no application form changes associated with this initiative, NIH is moving to new application forms (FORMS-I) to support other initiatives for due dates on or after January 25, 2025 ( NOT-OD-24-086 ). Therefore, all funding opportunities with the new review framework will also include updated forms.

Using the correct funding opportunity and application form version for your due date is critical to success. Applications submitted using a funding opportunity that is no longer available for a specific due date or submitted using the incorrect form version will be withdrawn and removed from funding consideration.

Tips for Applicants Applying to Impacted Funding Opportunities for Due Dates on or after January 25, 2025

• The simplified review framework applies.
• As always, be sure to be responsive to the application requirements in Section IV. Application and Submission Information as well as the review criteria in Section V. Application Review Information of the funding opportunity when preparing your application.
• You must apply to a funding opportunity that includes the simplified review framework in Section V. Application Review Information of the funding opportunity (i.e., review criteria are organized into three factors).
• See Do I Have the Right Forms for My Application? for help identifying the competition id.

• Application forms and associated instructions will be added at least 30 days and, frequently 60 days or more, prior to the first due date.
• You can begin drafting your application attachments (Specific Aims, Research Plan, etc.) using funding opportunity and current (FORMS-H) application guide instructions and make any needed adjustments for other initiatives once FORMS-I instructions are available.

Tips for Applicants Submitting to a Due Date on or before January 24, 2025

• The simplified review framework does not apply.
• You must apply to a funding opportunity that includes the legacy five stand-alone criteria in Section V. Application Review Information.
• Some opportunities may be extended to allow additional due dates prior to January 25.

Applicants are encouraged to contact a NIH Program Official if they still have questions or need additional clarification.

This page last updated on: April 4, 2024

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• Open access
• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

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

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

46k Accesses

805 Altmetric

Metrics details

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

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

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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.

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

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Correspondence to Kevin J. Verstrepen .

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

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Using the consolidated Framework for Implementation Research to integrate innovation recipients’ perspectives into the implementation of a digital version of the spinal cord injury health maintenance tool: a qualitative analysis

• John A Bourke 1 , 2 , 3 ,
• K. Anne Sinnott Jerram 1 , 2 ,
• Mohit Arora 1 , 2 ,
• Ashley Craig 1 , 2 &
• James W Middleton 1 , 2 , 4 , 5  

BMC Health Services Research volume  24 , Article number:  390 ( 2024 ) Cite this article

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Despite advances in managing secondary health complications after spinal cord injury (SCI), challenges remain in developing targeted community health strategies. In response, the SCI Health Maintenance Tool (SCI-HMT) was developed between 2018 and 2023 in NSW, Australia to support people with SCI and their general practitioners (GPs) to promote better community self-management. Successful implementation of innovations such as the SCI-HMT are determined by a range of contextual factors, including the perspectives of the innovation recipients for whom the innovation is intended to benefit, who are rarely included in the implementation process. During the digitizing of the booklet version of the SCI-HMT into a website and App, we used the Consolidated Framework for Implementation Research (CFIR) as a tool to guide collection and analysis of qualitative data from a range of innovation recipients to promote equity and to inform actionable findings designed to improve the implementation of the SCI-HMT.

Data from twenty-three innovation recipients in the development phase of the SCI-HMT were coded to the five CFIR domains to inform a semi-structured interview guide. This interview guide was used to prospectively explore the barriers and facilitators to planned implementation of the digital SCI-HMT with six health professionals and four people with SCI. A team including researchers and innovation recipients then interpreted these data to produce a reflective statement matched to each domain. Each reflective statement prefaced an actionable finding, defined as alterations that can be made to a program to improve its adoption into practice.

Five reflective statements synthesizing all participant data and linked to an actionable finding to improve the implementation plan were created. Using the CFIR to guide our research emphasized how partnership is the key theme connecting all implementation facilitators, for example ensuring that the tone, scope, content and presentation of the SCI-HMT balanced the needs of innovation recipients alongside the provision of evidence-based clinical information.

Conclusions

Understanding recipient perspectives is an essential contextual factor to consider when developing implementation strategies for healthcare innovations. The revised CFIR provided an effective, systematic method to understand, integrate and value recipient perspectives in the development of an implementation strategy for the SCI-HMT.

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Injury to the spinal cord can occur through traumatic causes (e.g., falls or motor vehicle accidents) or from non-traumatic disease or disorder (e.g., tumours or infections) [ 1 ]. The onset of a spinal cord injury (SCI) is often sudden, yet the consequences are lifelong. The impact of a SCI is devastating, with effects on sensory and motor function, bladder and bowel function, sexual function, level of independence, community participation and quality of life [ 2 ]. In order to maintain good health, wellbeing and productivity in society, people with SCI must develop self-management skills and behaviours to manage their newly acquired chronic health condition [ 3 ]. Given the increasing emphasis on primary health care and community management of chronic health conditions, like SCI, there is a growing responsibility on all parties to promote good health practices and minimize the risks of common health complications in their communities.

To address this need, the Spinal Cord Injury Health Maintenance Tool (SCI-HMT) was co-designed between 2018 and 2023 with people living with SCI and their General Practitioners (GPs) in NSW, Australia [ 4 ] The aim of the SCI-HMT is to support self-management of the most common and arguably avoidable potentially life-threatening complications associated with SCI, such as mental health crises, autonomic dysreflexia, kidney infections and pressure injuries. The SCI-HMT provides comprehensible information with resources about the six highest priority health areas related to SCI (as indicated by people with SCI and GPs) and was developed over two phases. Phase 1 focused on developing a booklet version and Phase 2 focused on digitizing this content into a website and smartphone app [ 4 , 5 ].

Enabling the successful implementation of evidence-based innovations such as the SCI-HMT is inevitably influenced by contextual factors: those dynamic and diverse array of forces within real-world settings working for or against implementation efforts [ 6 ]. Contextual factors often include background environmental elements in which an intervention is situated, for example (but not limited to) demographics, clinical environments, organisational culture, legislation, and cultural norms [ 7 ]. Understanding the wider context is necessary to identify and potentially mitigate various challenges to the successful implementation of those innovations. Such work is the focus of determinant frameworks, which focus on categorising or classing groups of contextual determinants that are thought to predict or demonstrate an effect on implementation effectiveness to better understand factors that might influence implementation outcomes [ 8 ].

One of the most highly cited determinant frameworks is the Consolidated Framework for Implementation Research (CFIR) [ 9 ], which is often posited as an ideal framework for pre-implementation preparation. Originally published in 2009, the CFIR has recently been subject to an update by its original authors, which included a literature review, survey of users, and the creation of an outcome addendum [ 10 , 11 ]. A key contribution from this revision was the need for a greater focus on the place of innovation recipients, defined as the constituency for whom the innovation is being designed to benefit; for example, patients receiving treatment, students receiving a learning activity. Traditionally, innovation recipients are rarely positioned as key decision-makers or innovation implementers [ 8 ], and as a consequence, have not often been included in the application of research using frameworks, such as the CFIR [ 11 ].

Such power imbalances within the intersection of healthcare and research, particularly between those receiving and delivering such services and those designing such services, have been widely reported [ 12 , 13 ]. There are concerted efforts within health service development, health research and health research funding, to rectify this power imbalance [ 14 , 15 ]. Importantly, such efforts to promote increased equitable population impact are now being explicitly discussed within the implementation science literature. For example, Damschroder et al. [ 11 ] has recently argued for researchers to use the CFIR to collect data from innovation recipients, and that, ultimately, “equitable population impact is only possible when recipients are integrally involved in implementation and all key constituencies share power and make decisions together” (p. 7). Indeed, increased equity between key constituencies and partnering with innovation recipients promotes the likelihood of sustainable adoption of an innovation [ 4 , 12 , 14 ].

There is a paucity of work using the updated CFIR to include and understand innovation recipients’ perspectives. To address this gap, this paper reports on a process of using the CFIR to guide the collection of qualitative data from a range of innovation recipients within a wider co-design mixed methods study examining the development and implementation of SCI-HMT. The innovation recipients in our research are people living with SCI and GPs. Guided by the CFIR domains (shown in the supplementary material), we used reflexive thematic analysis [ 16 ]to summarize data into reflective summaries, which served to inform actionable findings designed to improve implementation of the SCI-HMT.

The procedure for this research is multi-stepped and is summarized in Fig.  1 . First, we mapped retrospective qualitative data collected during the development of the SCI-HMT [ 4 ] against the five domains of the CFIR in order to create a semi-structured interview guide (Step 1). Then, we used this interview guide to collect prospective data from health professionals and people with SCI during the development of the digital version of the SCI-HMT (Step 2) to identify implementation barriers and facilitators. This enabled us to interpret a reflective summary statement for each CFIR domain. Lastly, we developed an actionable finding for each domain summary. The first (RESP/18/212) and second phase (2019/ETH13961) of the project received ethical approval from The Northern Sydney Local Health District Human Research Ethics Committee. The reporting of this study was conducted in line with the consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines [ 17 ]. All methods were performed in accordance with the relevant guidelines and regulations.

Procedure of synthesising datasets to inform reflective statements and actionable findings. a Two health professionals had a SCI (one being JAB); b Two co-design researchers had a SCI (one being JAB)

Step one: retrospective data collection and analysis

We began by retrospectively analyzing the data set (interview and focus group transcripts) from the previously reported qualitative study from the development phase of the SCI-HMT [ 4 ]. This analysis was undertaken by two team members (KASJ and MA). KASJ has a background in co-design research. Transcript data were uploaded into NVivo software (Version 12: QSR International Pty Ltd) and a directed content analysis approach [ 18 ] was applied to analyze categorized data a priori according to the original 2009 CFIR domains (intervention characteristics, outer setting, inner setting, characteristics of individuals, and process of implementation) described by Damschroder et al. [ 9 ]. This categorized data were summarized and informed the specific questions of a semi-structured interview guide. The final output of step one was an interview guide with context-specific questions arranged according to the CFIR domains (see supplementary file 1). The interview was tested with two people with SCI and one health professional.

Step two: prospective data collection and analysis

In the second step, semi-structured interviews were conducted by KASJ (with MA as observer) with consenting healthcare professionals who had previously contributed to the development of the SCI-HMT. Healthcare professionals included GPs, Nurse Consultants, Specialist Physiotherapists, along with Health Researchers (one being JAB). In addition, a focus group was conducted with consenting individuals with SCI who had contributed to the SCI-HMT design and development phase. The interview schedule designed in step one above guided data collection in all interviews and the focus group.

The focus group and interviews were conducted online, audio recorded, transcribed verbatim and uploaded to NVivo software (Version 12: QSR International Pty Ltd). All data were subject to reflexive, inductive and deductive thematic analysis [ 16 , 19 ] to better understand participants’ perspectives regarding the potential implementation of the SCI-HMT. First, one team member (KASJ) read transcripts and began a deductive analysis whereby data were organized into CFIR domains-specific dataset. Second, KASJ and JAB analyzed this domain-specific dataset to inductively interpret a reflective statement which served to summarise all participant responses to each domain. The final output of step two was a reflective summary statement for each CFIR domain.

Step three: data synthesis

In the third step we aimed to co-create an actionable finding (defined as tangible alteration that can be made to a program, in this case the SCI-HMT [ 20 ]) based on each domain-specific reflective statement. To achieve this, three codesign researchers (KAS and JAB with one person with SCI from Step 2 (deidentified)) focused on operationalising each reflective statement into a recommended modification for the digital version of the SCI-HMT. This was an iterative process guided by the specific CFIR domain and construct definitions, which we deemed salient and relevant to each reflective statement (see Table  2 for example). Data synthesis involved line by line analysis, group discussion, and repeated refinement of actionable findings. A draft synthesis was shared with SCI-HMT developers (JWM and MA) and refinement continued until consensus was agreed on. The final outputs of step three were an actionable finding related to each reflective statement for each CFIR domain.

The characteristics of both the retrospective and prospective study participants are shown in Table  1 . The retrospective data included data from a total of 23 people: 19 people with SCI and four GPs. Of the 19 people with SCI, 12 participated in semi-structured interviews, seven participated in the first focus group, and four returned to the second focus group. In step 2, four people with SCI participated in a focus group and six healthcare professionals participated in one-on-one semi-structured interviews. Two of the healthcare professionals (a GP and a registrar) had lived experience of SCI, as did one researcher (JAB). All interviews and focus groups were conducted either online or in-person and ranged in length between 60 and 120 min.

In our overall synthesis, we actively interpreted five reflective statements based on the updated CFIR domain and construct definitions by Damschroder et al. [ 11 ]. Table  2 provides a summary of how we linked the updated CFIR domain and construct definitions to the reflective statements. We demonstrate this process of co-creation below, including illustrative quotes from participants. Importantly, we guide readers to the actionable findings related to each reflective statement in Table  2 . Each actionable statement represents an alteration that can be made to a program to improve its adoption into practice.

Participants acknowledged that self-management is a major undertaking and very demanding, as one person with SCI said, “ we need to be informed without being terrified and overwhelmed”. Participants felt the HMT could indeed be adapted, tailored, refined, or reinvented to meet local needs. For example, another person with SCI remarked:

“Education needs to be from the get-go but in bite sized pieces from all quarters when readiness is most apparent… at all time points , [not just as a] a newbie tool or for people with [long-term impairment] ” (person with SCI_02).

Therefore, the SCI-HMT had to balance complexity of content while still being accessible and engaging, and required input from both experts in the field and those with lived experience of SCI, for example, a clinical nurse specialist suggested:

“it’s essential [the SCI-HMT] is written by experts in the field as well as with collaboration with people who have had a, you know, the lived experience of SCI” (healthcare professional_03).

Furthermore, the points of contact with healthcare for a person with SCI can be challenging to navigate and the SCI-HMT has the potential to facilitate a smoother engagement process and improve communication between people with SCI and healthcare services. As a GP suggested:

“we need a tool like this to link to that pathway model in primary health care , [the SCI-HMT] it’s a great tool, something that everyone can read and everyone’s reading the same thing” (healthcare professional_05).

Participants highlighted that the ability of the SCI-HMT to facilitate effective communication was very much dependent on the delivery format. The idea of digitizing the SCI-HMT garnered equal support from people with SCI and health care professionals, with one participant with SCI deeming it to be “ essential” ( person with SCI_01) and a health professional suggesting a “digitalized version will be an advantage for most people” (healthcare professional_02).

Outer setting

There was strong interest expressed by both people with SCI and healthcare professionals in using the SCI-HMT. The fundamental premise was that knowledge is power and the SCI-HMT would have strong utility in post-acute rehabilitation services, as well as primary care. As a person with SCI said,

“ we need to leave the [spinal unit] to return to the community with sufficient knowledge, and to know the value of that knowledge and then need to ensure primary healthcare provider [s] are best informed” (person with SCI_04).

The value of the SCI-HMT in facilitating clear and effective communication and shared decision-making between healthcare professionals and people with SCI was also highlighted, as shown by the remarks of an acute nurse specialist:

“I think this tool is really helpful for the consumer and the GP to work together to prioritize particular tests that a patient might need and what the regularity of that is” (healthcare professional_03).

Engaging with SCI peer support networks to promote the SCI-HMT was considered crucial, as one person with SCI emphasized when asked how the SCI-HMT might be best executed in the community, “…peers, peers and peers” (person with SCI_01). Furthermore, the layering of content made possible in the digitalized version will allow for the issue of approachability in terms of readiness for change, as another person with SCI said:

“[putting content into a digital format] is essential and required and there is a need to put summarized content in an App with links to further web-based information… it’s not likely to be accessed otherwise” (person with SCI_02).

Inner setting

Participants acknowledged that self-management of health and well-being is substantial and demanding. It was suggested that the scope, tone, and complexity of the SCI-HMT, while necessary, could potentially be resisted by people with SCI if they felt overwhelmed, as one person with SCI described:

“a manual that is really long and wordy, like, it’s [a] health metric… they maybe lack the health literacy to, to consume the content then yes, it would impede their readiness for [self-management]” (person with SCI_02).

Having support from their GPs was considered essential, and the HMT could enable GP’s, who are under time pressure, to provide more effective health and advice to their patients, as one GP said:

“We GP’s are time poor, if you realize then when you’re time poor you look quickly to say oh this is a patient tool - how can I best use this?” (healthcare professional_05).

Furthermore, health professional skills may be best used with the synthesis of self-reported symptoms, behaviors, or observations. A particular strength of a digitized version would be its ability to facilitate more streamlined communication between a person with SCI and their primary healthcare providers developing healthcare plans, as an acute nurse specialist reflected, “ I think that a digitalized version is essential with links to primary healthcare plans” (healthcare professional_03).

Efficient communication with thorough assessment is essential to ensure serious health issues are not missed, as findings reinforce that the SCI-HMT is an educational tool, not a replacement for healthcare services, as a clinical nurse specialist commented, “ remember, things will go wrong– people end up very sick and in acute care “ (healthcare professional_02).

The SCI-HMT has the potential to provide a pathway to a ‘hope for better than now’ , a hope to ‘remain well’ and a hope to ‘be happy’ , as the informant with SCI (04) declared, “self-management is a long game, if you’re keeping well, you’ve got that possibility of a good life… of happiness”. Participants with SCI felt the tool needed to be genuine and

“acknowledge the huge amount of adjustment required, recognizing that dealing with SCI issues is required to survive and live a good life” (person with SCI_04).

However, there is a risk that an individual is completely overwhelmed by the scale of the SCI-HMT content and the requirement for lifelong vigilance. Careful attention and planning were paid to layering the information accordingly to support self-management as a ‘long game’, which one person with SCI reflected in following:

“the first 2–3 year [period] is probably the toughest to get your head around the learning stuff, because you’ve got to a stage where you’re levelling out, and you’ve kind of made these promises to yourself and then you realize that there’s no quick fix” (person with SCI_01).

It was decided that this could be achieved by providing concrete examples and anecdotes from people with SCI illustrating that a meaningful, healthy life is possible, and that good health is the bedrock of a good life with SCI.

There was universal agreement that the SCI-HMT is aspirational and that it has the potential to improve knowledge and understanding for people with SCI, their families, community workers/carers and primary healthcare professionals, as a GP remarked:

“[different groups] could just read it and realize, ‘Ahh, OK that’s what that means… when you’re doing catheters. That’s what you mean when you’re talking about bladder and bowel function or skin care” (healthcare professional_04).

Despite the SCI-HMT providing an abundance of information and resources to support self-management, participants identified four gaps: (i) the priority issue of sexuality, including pleasure and identity, as one person with SCI remarked:

“ sexuality is one of the biggest issues that people with SCI often might not speak about that often cause you know it’s awkward for them. So yeah, I think that’s a that’s a serious issue” (person with SCI_03).

(ii) consideration of the taboo nature of bladder and bowel topics for indigenous people, (iii) urgent need to ensure links for SCI-HMT care plans are compatible with patient management systems, and (iv) exercise and leisure as a standalone topic taking account of effects of physical activity, including impact on mental health and wellbeing but more especially for fun.

To ensure longevity of the SCI-HMT, maintaining a partnership between people with SCI, SCI community groups and both primary and tertiary health services is required for liaison with the relevant professional bodies, care agencies, funders, policy makers and tertiary care settings to ensure ongoing education and promotion of SCI-HMT is maintained. For example, delivery of ongoing training of healthcare professionals to both increase the knowledge base of primary healthcare providers in relation to SCI, and to promote use of the tools and resources through health communities. As a community nurse specialist suggested:

“ improving knowledge in the health community… would require digital links to clinical/health management platforms” (healthcare professional_02).

In a similar vein, a GP suggested:

“ our common GP body would have continuing education requirements… especially if it’s online, in particular for the rural, rural doctors who you know, might find it hard to get into the city” (healthcare professional_04).

The successful implementation of evidence-based innovations into practice is dependent on a wide array of dynamic and active contextual factors, including the perspectives of the recipients who are destined to use such innovations. Indeed, the recently updated CFIR has called for innovation recipient perspectives to be a priority when considering contextual factors [ 10 , 11 ]. Understanding and including the perspectives of those the innovation is being designed to benefit can promote increased equity and validation of recipient populations, and potentially increase the adoption and sustainability of innovations.

In this paper, we have presented research using the recently updated CFIR to guide the collection of innovation recipients’ perspectives (including people with SCI and GPs working in the community) regarding the potential implementation barriers and facilitators of the digital version of the SCI-HMT. Collected data were synthesized to inform actionable findings– tangible ways in which the SCI-HMT could be modified according of the domains of the CFIR (e.g., see Keith et al. [ 20 ]). It is important to note that we conducted this research using the original domains of the CFIR [ 9 ] prior to Damschroder et al. publishing the updated CFIR [ 11 ]. However, in our analysis we were able to align our findings to the revised CFIR domains and constructs, as Damschroder [ 11 ] suggests, constructs can “be mapped back to the original CFIR to ensure longitudinal consistency” (p. 13).

One of the most poignant findings from our analyses was the need to ensure the content of the SCI-HMT balanced scientific evidence and clinical expertise with lived experience knowledge. This balance of clinical and experiential knowledge demonstrated genuine regard for lived experience knowledge, and created a more accessible, engaging, useable platform. For example, in the innovation and individual domains, the need to include lived experience quotes was immediately apparent once the perspective of people with SCI was included. It was highlighted that while the SCI-HMT will prove useful to many parties at various stages along the continuum of care following onset of SCI, there will be those individuals that are overwhelmed by the scale of the content. That said, the layering of information facilitated by the digitalized version is intended to provide an ease of navigation through the SCI-HMT and enable a far greater sense of control over personal health and wellbeing. Further, despite concerns regarding e-literacy the digitalized version of the SCI-HMT is seen as imperative for accessibility given the wide geographic diversity and recent COVID pandemic [ 21 ]. While there will be people who are challenged by the technology, the universally acceptable use of the internet is seen as less of a barrier than printed material.

The concept of partnership was also apparent within the data analysis focusing on the outer and inner setting domains. In the outer setting domain, our findings emphasized the importance of engaging with SCI community groups, as well as primary and tertiary care providers to maximize uptake at all points in time from the phase of subacute rehabilitation onwards. While the SCI-HMT is intended for use across the continuum of care from post-acute rehabilitation onwards, it may be that certain modules are more relevant at different times, and could serve as key resources during the hand over between acute care, inpatient rehabilitation and community reintegration.

Likewise, findings regarding the inner setting highlighted the necessity of a productive partnership between GPs and individuals with SCI to address the substantial demands of long-term self-management of health and well-being following SCI. Indeed, support is crucial, especially when self-management is the focus. This is particularly so in individuals living with complex disability following survival after illness or injury [ 22 ], where health literacy has been found to be a primary determinant of successful health and wellbeing outcomes [ 23 ]. For people with SCI, this tool potentially holds the most appeal when an individual is ready and has strong partnerships and supportive communication. This can enable potential red flags to be recognized earlier allowing timely intervention to avert health crises, promoting individual well-being, and reducing unnecessary demands on health services.

While the SCI-HMT is an educational tool and not meant to replace health services, findings suggest the current structure would lead nicely to having the conversation with a range of likely support people, including SCI peers, friends and family, GP, community nurses, carers or via on-line support services. The findings within the process domain underscored the importance of ongoing partnership between innovation implementers and a broad array of innovation recipients (e.g., individuals with SCI, healthcare professionals, family, funding agencies and policy-makers). This emphasis on partnership also addresses recent discussions regarding equity and the CFIR. For example, Damschroder et al. [ 11 ] suggests that innovation recipients are too often not included in the CFIR process, as the CFIR is primarily seen as a tool intended “to collect data from individuals who have power and/or influence over implementation outcomes” (p. 5).

Finally, we feel that our inclusion of innovation recipients’ perspectives presented in this article begins to address the notion of equity in implementation, whereby the inclusion of recipient perspectives in research using the CFIR both validates, and increases, the likelihood of sustainable adoption of evidence-based innovations, such as the SCI-HMT. We have used the CFIR in a pragmatic way with an emphasis on meaningful engagement between the innovation recipients and the research team, heeding the call from Damschroder et al. [ 11 ], who recently argued for researchers to use the CFIR to collect data from innovation recipients. Adopting this approach enabled us to give voice to innovation recipient perspectives and subsequently ensure that the tone, scope, content and presentation of the SCI-HMT balanced the needs of innovation recipients alongside the provision of evidence-based clinical information.

Our research is not without limitations. While our study was successful in identifying a number of potential barriers and facilitators to the implementation of the SCI-HMT, we did not test any implementation strategies to impact determinants, mechanisms, or outcomes. This will be the focus of future research on this project, which will investigate the impact of implementation strategies on outcomes. Focus will be given to the context-mechanism configurations which give rise to particular outcomes for different groups in certain circumstances [ 7 , 24 ]. A second potential concern is the relatively small sample size of participants that may not allow for saturation and generalizability of the findings. However, both the significant impact of secondary health complications for people with SCI and the desire for a health maintenance tool have been established in Australia [ 2 , 4 ]. The aim our study reported in this article was to achieve context-specific knowledge of a small sample that shares a particular mutual experience and represents a perspective, rather than a population [ 25 , 26 ]. We feel our findings can stimulate discussion and debate regarding participant-informed approaches to implementation of the SCI-HMT, which can then be subject to larger-sample studies to determine their generalisability, that is, their external validity. Notably, future research could examine the interaction between certain demographic differences (e.g., gender) of people with SCI and potential barriers and facilitators to the implementation of the SCI-HMT. Future research could also include the perspectives of other allied health professionals working in the community, such as occupational therapists. Lastly, while our research gave significant priority to recipient viewpoints, research in this space would benefit for ensuring innovation recipients are engaged as genuine partners throughout the entire research process from conceptualization to implementation.

Employing the CFIR provided an effective, systematic method for identifying recipient perspectives regarding the implementation of a digital health maintenance tool for people living with SCI. Findings emphasized the need to balance clinical and lived experience perspectives when designing an implementation strategy and facilitating strong partnerships with necessary stakeholders to maximise the uptake of SCI-HMT into practice. Ongoing testing will monitor the uptake and implementation of this innovation, specifically focusing on how the SCI-HMT works for different users, in different contexts, at different stages and times of the rehabilitation journey.

Data availability

The datasets supporting the conclusions of this article are available available upon request and with permission gained from the project Steering Committee.

Abbreviations

spinal cord injury

HMT-Spinal Cord Injury Health Maintenance Tool

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Acknowledgements

Authors of this study would like to thank all the consumers with SCI and healthcare professionals for their invaluable contribution to this project. Their participation and insights have been instrumental in shaping the development of the SCI-HMT. The team also acknowledges the support and guidance provided by the members of the Project Steering Committee, as well as the partner organisations, including NSW Agency for Clinical Innovation, and icare NSW. Author would also like to acknowledge the informant group with lived experience, whose perspectives have enriched our understanding and informed the development of SCI-HMT.

The SCI Wellness project was a collaborative project between John Walsh Centre for Rehabilitation Research at The University of Sydney and Royal Rehab. Both organizations provided in-kind support to the project. Additionally, the University of Sydney and Royal Rehab received research funding from Insurance and Care NSW (icare NSW) to undertake the SCI Wellness Project. icare NSW do not take direct responsibility for any of the following: study design, data collection, drafting of the manuscript, or decision to publish.

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John Walsh Centre for Rehabilitation Research, Northern Sydney Local Health District, St Leonards, NSW, Australia

John A Bourke, K. Anne Sinnott Jerram, Mohit Arora, Ashley Craig & James W Middleton

The Kolling Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia

Burwood Academy Trust, Burwood Hospital, Christchurch, New Zealand

John A Bourke

Royal Rehab, Ryde, NSW, Australia

James W Middleton

State Spinal Cord Injury Service, NSW Agency for Clinical Innovation, St Leonards, NSW, Australia

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Contributions

Project conceptualization: KASJ, MA, JWM; project methodology: JWM, MA, KASJ, JAB; data collection: KASJ and MA; data analysis: KASJ, JAB, MA, JWM; writing—original draft preparation: JAB; writing—review and editing: JAB, KASJ, JWM, MA, AC; funding acquisition: JWM, MA. All authors contributed to the revision of the paper and approved the final submitted version.

Corresponding author

Correspondence to John A Bourke .

Ethics declarations

Ethics approval and consent to participate.

The first (RESP/18/212) and second phase (2019/ETH13961) of the project received ethical approval from The Northern Sydney Local Health District Human Research Ethics Committee. All participants provided informed, written consent. All data were to be retained for 7 years (23rd May 2030).

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Not applicable.

Competing interests

MA part salary (from Dec 2018 to Dec 2023), KASJ part salary (July 2021 to Dec 2023) and JAB part salary (Jan 2022 to Aug 2022) was paid from the grant monies. Other authors declare no conflicts of interest.

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Bourke, J.A., Jerram, K.A.S., Arora, M. et al. Using the consolidated Framework for Implementation Research to integrate innovation recipients’ perspectives into the implementation of a digital version of the spinal cord injury health maintenance tool: a qualitative analysis. BMC Health Serv Res 24 , 390 (2024). https://doi.org/10.1186/s12913-024-10847-x

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Received : 14 August 2023

Accepted : 11 March 2024

Published : 28 March 2024

DOI : https://doi.org/10.1186/s12913-024-10847-x

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• Spinal Cord injury
• Self-management
• Innovation recipients
• Secondary health conditions
• Primary health care
• Evidence-based innovations
• Actionable findings
• Consolidated Framework for implementation research

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