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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.   

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Filazzola and colleagues model how terrestrial wildlife within 60 Canadian and American cities will be affected by climate change.

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• 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.

Prevent plagiarism. Run a free check.

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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• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

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

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

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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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Contributions

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

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Correspondence to Kevin J. Verstrepen .

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

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Major depressive disorder: Validated treatments and future challenges

Rabie karrouri.

Department of Psychiatry, Moulay Ismaïl Military Hospital, Faculty of Medicine and Pharmacy, Sidi Mohamed Ben Abdellah University, Fez 30070, Morocco

Zakaria Hammani

Roukaya benjelloun.

Department of Psychiatry, Faculty of Medicine, Mohammed VI University of Health Sciences, Casablanca 20000, Morocco

Yassine Otheman

Department of Psychiatry, Moulay Ismaïl Military Hospital, Faculty of Medicine and Pharmacy, Sidi Mohamed Ben Abdellah University, Fez 30070, Morocco. [email protected]

Corresponding author: Yassine Otheman, MD, Associate Professor, Chief Doctor, Department of Psychiatry, Moulay Ismaïl Military Hospital, Faculty of Medicine and Pharmacy, Sidi Mohamed Ben Abdellah University, 1893, Km 2.2 road of Sidi Hrazem, Fez 30070, Morocco. [email protected]

Depression is a prevalent psychiatric disorder that often leads to poor quality of life and impaired functioning. Treatment during the acute phase of a major depressive episode aims to help the patient reach a remission state and eventually return to their baseline level of functioning. Pharmacotherapy, especially selective serotonin reuptake inhibitors antidepressants, remains the most frequent option for treating depression during the acute phase, while other promising pharmacological options are still competing for the attention of practitioners. Depression-focused psychotherapy is the second most common option for helping patients overcome the acute phase, maintain remission, and prevent relapses. Electroconvulsive therapy is the most effective somatic therapy for depression in some specific situations; meanwhile, other methods have limits, and their specific indications are still being studied. Combining medications, psychotherapy, and somatic therapies remains the most effective way to manage resistant forms of depression.

Core Tip: Depression is a persistent public health problem for which treatments must be codified and simplified to enhance current practice. Several therapies have been suggested worldwide, with varying levels of validity. This article explores effective and valid therapies for treating depression by addressing current and future research topics for different treatment categories.

INTRODUCTION

Depression is a common psychiatric disorder and a major contributor to the global burden of diseases. According to the World Health Organization, depression is the second-leading cause of disability in the world and is projected to rank first by 2030[ 1 ]. Depression is also associated with high rates of suicidal behavior and mortality[ 2 ].

Treatments administered during the acute phase of a major depressive episode aim to help the patient reach a remission state and eventually return to their baseline level of functioning[ 3 ]. Acute-phase treatment options include pharmacotherapy, depression-focused psychotherapy, combinations of medications and psychotherapy, and somatic therapies such as electroconvulsive therapy (ECT). Nevertheless, managing the acute phase of depression is only the first step in a long therapy process that aims to maintain remission and prevent relapses. In this article, we discuss various treatment options implemented by clinicians, highlighting the role that each option plays in actual psychiatric practice.

PHARMACOTHERAPY

While selective serotonin reuptake inhibitors (SSRIs) remain the gold-standard treatment for depression, new antidepressants are always being developed and tested. The ultimate goal is to discover a molecule that exhibits quick effectiveness with as few side effects as possible.

Daniel Bovet studied the structure of histamine (the causative agent in allergic responses) to find an antagonist, which was finally synthesized in 1937[ 4 ]. Since then, many researchers have studied the link between the structures and activities of different antihistaminic agents, contributing to the discovery of almost all antidepressants[ 5 ].

In the following subsections, we list the main classes of antidepressants in chronological order of apparition, highlighting the most widely used molecules in daily psychiatric practice.

Monoamine oxidase inhibitors

Iproniazid was the first drug defined as an antidepressant; it was later classified as a monoamine oxidase inhibitor (MAOI)[ 6 , 7 ]. Several other MAOIs have been introduced since 1957[ 8 ]. Due to their irreversible inhibition of monoamine oxidase, MOAIs have numerous side effects, such as hepatotoxicity and hypertensive crises, that can lead to lethal intracranial hemorrhages. Consequently, MAOIs have become less commonly used over time[ 9 ].

Trials have demonstrated that MAOIs’ efficacy is comparable to that of tricyclic antidepressants (TCAs)[ 10 , 11 ]. However, considering MAOIs’ drug interactions, dietary restrictions, and potentially dangerous side effects, they are now almost exclusively prescribed for patients who have not responded to several other pharmacotherapies, including TCAs[ 9 ]. Furthermore, MAOIs have demonstrated specific efficacy in treating depression with atypical features, such as reactive moods, reverse neuro-vegetative symptoms, and sensitivity to rejection[ 12 ].

MAOIs are also a potential therapeutic option when ECT is contraindicated[ 13 ]. MAOIs’ effectiveness is still unclear for treating depression in patients who are resistant to multiple sequential trials with SSRIs and serotonin-norepinephrine reuptake inhibitors (SNRIs)[ 14 ]. Nevertheless, psychiatrists’ use of MAOIs has declined over the years[ 15 , 16 ]. The use of MAOIs is generally restricted to patients who do not respond to other treatments.

The first TCA was discovered and released for clinical use in 1957 under the brand name Tofranil[ 5 , 17 ]. Since then, TCAs have remained among the most frequently prescribed drugs worldwide[ 9 ]. TCAs-such as amitriptyline, nortriptyline, protriptyline, imipramine, desipramine, doxepin, and trimipramine-are about as effective as other classes of antidepressants-including SSRIs, SNRIs, and MAOIs-in treating major depression[ 18 , 19 ].

However, some TCAs can be more effective than SSRIs when used to treat hospitalized patients[ 20 ]. This efficacy can be explained by the superiority of TCAs over SSRIs for patients with severe major depressive disorder (MDD) symptoms who require hospitalization[ 21 - 24 ]. However, no differences have been detected in outpatients who are considered less severely ill[ 18 , 20 ]. In most cases, TCAs should generally be reserved for situations when first-line drug treatments have failed[ 25 ].

In December 1987, a series of clinical studies confirmed that an SSRI called fluoxetine was as effective as TCAs for treating depression while causing fewer adverse effects[ 26 ]. After being released onto the market, its use expanded more quickly than that of any other psychotropic in history. In 1994, it was the second-best-selling drug in the world[ 7 ].

Currently available SSRIs include fluoxetine, sertraline, paroxetine, fluvoxamine, citalopram, and escitalopram. They have elicited different tolerance rates and side effects-mostly sexual and digestive (nausea and loss of appetite), as well as irritability, anxiety, insomnia, and headaches[ 27 ]. Nevertheless, SSRIs have a good tolerability profile[ 28 ].

In most systematic reviews and meta-analyses, SSRIs have demonstrated comparable efficacy to TCAs[ 18 , 19 , 29 ], and there is no significant evidence indicating the superiority of any other class or agent over SSRIs[ 29 - 31 ]. Furthermore, studies show no differences in efficacy among individual SSRIs[ 29 , 31 - 34 ]. Therefore, most guidelines currently recommend SSRIs as the first-line treatment for patients with major depression[ 25 ].

Norepinephrine reuptake inhibitors

Other monoamine (norepinephrine, serotonin, and dopamine) neurotransmitter reuptake inhibitors called SNRIs emerged during the 1990s to protect patients against the adverse effects of SSRIs[ 35 ]. Currently available SNRIs are venlafaxine, desvenlafaxine (the principal metabolite of venlafaxine), and duloxetine. The extended-release form of venlafaxine is the most commonly used drug in this class. Clinical guidelines commonly recommend prescribing SNRI to patients who do not respond to SSRIs[ 25 ].

In individual studies, venlafaxine and duloxetine are generally considered effective as SSRIs[ 36 ]. Also, venlafaxine’s efficacy is comparable to that of TCAs[ 37 , 38 ].

According to some meta-analyses, reboxetine (a selective noradrenaline reuptake inhibitor) seems less efficacious than SSRIs[ 39 ]. However, these findings could be due to the relatively poor tolerance of reboxetine[ 40 ].

Other antidepressants

Trazodone is the oldest medication of the so-called “other antidepressants” group that is still in wide use[ 41 , 42 ]. It has been shown to be an effective antidepressant in placebo-controlled research. However, in contemporary practice, it is much more likely to be used in low doses as a sedative-hypnotic than as an antidepressant[ 41 , 42 ].

Nefazodone’s structure is analogous to that of trazodone, though it has different pharmacological properties[ 43 ]. Its efficacy and overall tolerability are comparable to those of SSRIs, as indicated by comparative trials[ 43 ]. However, its use is associated with rare (but fatal) cases of clinical idiosyncratic hepatotoxicity[ 44 ].

Bupropion’s mechanism of action remains unclear, though it is classified as a norepinephrine and dopamine reuptake inhibitor[ 45 ]. It appears to have a more activating profile than SSRIs that are modestly superior to bupropion in patients with MDD[ 46 ]. However, for individuals with low to moderate levels of anxiety, the efficacy of bupropion in treating MDD is comparable to that of SSRIs[ 46 ]. Moreover, bupropion has a better tolerability profile than SSRIs, with minimal weight gain (or even leading to weight loss)[ 46 ]. In addition, bupropion is more likely than some SSRIs to improve symptoms of fatigue and sleepiness[ 47 ].

Mirtazapine and mianserin are tetracyclic compounds believed to increase the availability of serotonin or norepinephrine (or both), at least initially. Mirtazapine’s ability to antagonize serotoninergic subtypes receptors, <5-HT2A> and <5-HT2C>, could also increase norepinephrine and dopamine release in cortical regions[ 25 ]. Mirtazapine is about as effective as SSRIs[ 48 ].

Recently, drugs have been developed that block serotonin reuptake while affecting a variety of 5-HT receptor subtypes. The advantages of these agents ( e.g. , vilazodone and vortioxetine) over SSRIs are not fully clear. However, they appear to produce less sexual dysfunction and, in the specific case of vortioxetine, have particular benefits in depression-related cognitive impairment[ 49 ]. Indeed, vortioxetine is a very recent antidepressant with a multimodal mechanism that is thought to have a high affinity for serotonin transporters and 5-HT3, 5HT1A, 5HT7 receptors. Such a specific profile seems to indicate a level of efficacy to other antidepressants with a specific action on cognitive impairments[ 50 , 51 ].

In conclusion, no significant differences have been found between different classes of antidepressants in terms of their efficacy[ 52 ], though some drugs show some weak-to-moderate evidence indicating they are more effective than some other drugs[ 53 ]. Concerning the acceptability of these drugs, citalopram, escitalopram, fluoxetine, sertraline, and vortioxetine have been deemed more tolerable than other antidepressants, whereas amitriptyline, clomipramine, duloxetine, fluvoxamine, trazodone, and venlafaxine had the highest dropout rates[ 53 ] because of their more frequent and severe side effects. Nausea and vomiting were the most common reasons for treatment discontinuation; sexual dysfunction, sedation, priapism, and cardiotoxicity were also reported[ 31 , 41 ].

Ketamine and related molecules

In intravenous sub-anesthetic doses, ketamine has very quick effects on resistant unipolar (and, possibly, bipolar) depression and acute suicidal ideation[ 54 , 55 ]. The antidepressant effect of ketamine can persist for several days but eventually wanes. A few reports are have cited oral and intranasal formulations of ketamine for treatment-resistant depression[ 56 , 57 ], but there is still no data about the potential link between the onset of action and the route of administration.

Common adverse effects of ketamine include dizziness, neurotoxicity, cognitive dysfunction, blurred vision, psychosis, dissociation, urological dysfunction, restlessness, headache, nausea, vomiting, and cardiovascular symptoms[ 58 ]. Such adverse effects tend to be brief in acute, low-dose treatments[ 36 ], whereas prolonged exposure may predispose patients to neurotoxicity and drug dependence[ 56 ]. Lastly, since ketamine is associated with a higher risk of drug abuse and addiction, it cannot be recommended in daily clinical practice[ 59 , 60 ].

Ketamine is not a miracle drug, and many important factors still need to be defined, such as the most effective dose and the optimal administration route[ 61 , 62 ]. The current lack of guidelines about the therapeutic monitoring of ketamine treatment for depression further complicates the expanding use of this treatment[ 56 ]. Even though ketamine might never reach the market, it has stimulated research in the neurobiology of depression, including studies on potential fast and long-lasting antidepressants.

Ketamine has an active metabolite (hydroxynorketamine) that can produce rapid and sustained glutamatergic stimulation. It also seems to be free of many of the safety problems associated with ketamine and, thus, should be studied.

Research on the S-enantiomer of ketamine (S-ketamine, or esketamine, especially intranasal) could also be valuable, as it has a 3 to 4 times greater affinity than ketamine for the N-methyl-D-aspartate (NMDA) receptor[ 40 ]. It was approved by the United States Food and Drug Administration in March 2019 for treatment-resistant depression. However, current knowledge about the effects of prolonged esketamine therapy is still preliminary. In addition, regarding the potential risk of abuse, esketamine use must be carefully monitored[ 63 - 65 ].

Other glutamate receptor modulators have been evaluated in small studies as monotherapy agents or as adjuncts to other antidepressants. Examples include noncompetitive NMDA receptor antagonists (memantine, dextromethorphan/quinidi-ne, dextromethorphan/bupropion, and lanicemine), NR2B subunit-specific NMDA receptor antagonists (traxoprodil), NMDA receptor glycine site partial agonists (D-cycloserine, rapastinel), and metabotropic glutamate receptor antagonists (basimglurant, declogurant)[ 66 - 68 ] (Table ​ (Table1). 1 ).

Main classes of antidepressants with their date of approval, contributions, and disadvantages

NMDA: N-methyl-D-aspartate; SSRI: DSelective serotonin reuptake inhibitors; MDD: Major depressive disorder; MAOI: Monoamine oxidase inhibitor.

Perspectives

A purely neurotransmitter-based explanation for antidepressant drug action-especially serotonin-inhibiting drugs-is challenged by the significant percentage of patients who never achieve full remission[ 6 ] and the delayed clinical onset, which varies from two to four weeks. Moreover, studies show an acute increase in monoamines in the synaptic cleft immediately following treatment[ 69 ], even when the depletion of tryptophan (serotonin’s precursor) does not induce depressive-like behavior in healthy humans[ 70 , 71 ].

This finding shows that research on the pharmacological options for treating depression must go beyond monoaminergic neurotransmission systems. Research on the development of new antidepressants should explore several mechanisms of action on several types of receptors: Antagonism, inhibition of the reuptake of neurotransmitters, and modulators of glutamate receptors, as well as interactions with α-amino-3-acid receptors, hydroxy-5-methyl-4-isoxazolepropionic, brain-derived neurotrophic factor, tyrosine kinase B receptor (the mechanistic target of rapamycin), and glycogen synthase kinase-3[ 72 ].

Identifying the cellular targets of rapid-acting agents like ketamine could help practitioners develop more effective antidepressant molecules by revealing other receptors involved in gamma-aminobutyric acid regulation and glutamate transmission[ 73 ].

PSYCHOTHERAPY

Psychotherapeutic interventions are widely used to treat and prevent most psychiatric disorders. Such interventions are common in cases of depression, psychosocial difficulties, interpersonal problems, and intra-psychic conflicts. The specific psychotherapy approach chosen for any given case depends on the patient’s preference, as well as on the clinician’s background and availability[ 74 ] . Psychotherapy for patients with depression strengthens the therapeutic alliance and enables the patient to monitor their mood, improve their functioning, understand their symptoms better, and master the practical tools they need to cope with stressful events[ 75 ]. The following subsections briefly describe psychotherapeutic interventions that have been designed specifically for patients with depression.

Overview of psychotherapy in depression

Depression-focused psychotherapy is typically considered the initial treatment method for mild to moderate MDD. Based on significant clinical evidence, two specific psychotherapeutic methods are recommended: Cognitive-behavioral therapy (CBT) and interpersonal therapy (IPT). Supportive therapy (ST) and psychoeducational intervention (PEI) have also been recommended, those the evidence supporting these methods s not as strong. In more cases of severe depression, ST and PEI are used only to augment pharmacological treatments.

After remission, CBT, PEI, and mindfulness-based cognitive therapy (MBCT) are proposed to maintain and prevent depression. However, when psychotherapy has been effective during the initial phases of a depressive episode, it should be continued to maintain remission and prevent relapses while reducing the frequency of sessions[ 25 , 75 , 76 ].

Specific and intensive psychotherapeutic support is recommended for patients with chronic depression because of high rates of comorbidity with personality disorders, early trauma, and attachment deficits. The European Psychiatric Association recommends using the Cognitive Behavioral Analysis System of Psychotherapy (CBASP) for treating chronic depression and utilizing specific approaches suited to each patient’s preferences[ 77 ]. All these therapeutic options are summarized in Figure ​ Figure1 1 .

Overview of psychotherapy in different clinical situations of depression. MDD: Major depressive disorder; CBT: Cognitive-behavioral therapy; IPT: Interpersonal therapy; ST: Supportive therapy; PEI: Psycho-educational intervention; MBCT: Mindfulness based cognitive therapy; SIPS: Specific and intensive psychotherapeutic support; CBASP: Cognitive Behavioral Analysis System of Psychotherapy.

Structured psychotherapies

Cognitive and behavioral therapies: Based on robust evidence, CBT is one of the most well-documented and validated psychotherapeutic methods available. Interventional strategies are based on modifying dysfunctional behaviors and cognitions[ 77 ]. CBT targets depressed patients’ irrational beliefs and distorted cognitions that perpetuate depressive symptoms by challenging and reversing them[ 3 ]. Thus, CBT is a well-known effective treatment method for MDD[ 78 ] and has been recommended in most guidelines as a first-line treatment[ 79 - 81 ].

However, the effectiveness of CBT depends on patient’s capacity to observe and change their own beliefs and behaviors. Some simple techniques were developed to overcome this issue, especially in primary care management. Behavioral activation is one such technique, consisting of integrating pleasant activities into daily life to increase the number and intensity of the positive interactions that the patient has with their environment[ 82 , 83 ].

Acceptance and commitment therapy is another form of CBT. This type of therapy, which is based on functional contextualism, can help patients accept and adjusting to persistent problems. It appears to be effective in reducing depressive symptoms and preventing relapses[ 77 , 84 ].

Another form of CBT is computerized CBT (CCBT), implemented via a computer with a CD-ROM, DVD, or online CCBT, allowing patients to benefit from this therapy under conditions of reduced mobility, remoteness, confinement, or quarantine[ 79 ] .

CCBT and guided bibliotherapy based on CBT could be considered for self-motivated patients with mild to moderate major depression or as a complementary treatment to pharmacotherapy[ 25 ]. CBT is also recommended for patients with resistant depression in combination with antidepressants[ 85 ].

Schema therapy is another CBT-derived therapy that can be used in patients who have failed classical CBT, like patients with personality disorder comorbidity. Schema therapy is about as effective as CBT for treating depression[ 86 ]. In adolescent patients with depression, CBT is also a recommended option with plenty of evidence from multiple trials. Meanwhile, it remains the first-line treatment in children despite mixed findings across trials[ 87 ] . CBT is also a promising option for elderly depressed patients, though substantial evidence is still lacking because of the limited data on the subject[ 88 ] .

IPT: The goal of IPT is to identify the triggers of depressive symptoms or episodes. These triggers may include losses, social isolation, or difficulties in social interactions. The role of the intervention is to facilitate mourning (in the case of bereavement), help the patient recognize their own affect, and resolve social interaction dysfunction by building their social skills and social supports[ 89 ]. IPT, like CBT, is a first-line treatment for mild to moderate major depressive episodes in adults; it is also a well-established intervention for adolescents with depression[ 25 ] .

Problem-solving therapy: The problem-solving therapy (PST) approach combines cognitive and interpersonal elements, focusing on negative assessments of situations and problem-solving strategies. PST has been used in different clinical situations, like preventing depression among the elderly and treating patients with mild depressive symptoms, especially in primary care. Despite its small effect sizes, PST is comparable to other psychotherapeutic methods used to treat depression[ 88 , 90 ].

Marital and family therapy: Marital and family therapy (MFT) is effective in treating some aspects of depression. Family therapy has also been used to treat severe forms of depression associated with medications and hospitalization[ 91 ]. Marital and family problems can make people more vulnerable to depression, and MFT addresses these issues[ 92 ]. Marital therapy includes both members of the couple, as depression is considered in an interpersonal context in such cases. Some of the goals of this therapy are to facilitate communication and resolve different types of marital conflict. Family therapy uses similar principles as other forms of therapy while involving all family members and considering depression within the context of pathological family dynamics[ 93 ].

ST: Although ST is not as well-structured or well-evaluated as CBT or IPT, it is still commonly used to support depressed patients. In addition to sympathetic listening and expressing concern for the patient’s problems, ST requires emotionally attuned listening, empathic paraphrasing, explaining the nature of the patient’s suffering, and reassuring and encouraging them. These practices allow the patient to ventilate and accept their feelings, increase their self-esteem, and enhance their adaptive coping skills[ 94 ].

Psychodynamic therapy: Psychodynamic therapy encompasses a range of brief to long-term psychological interventions derived from psychoanalytic theories. This type of therapy focuses on intrapsychic conflicts related to shame, repressed impulses, problems in early childhood with one’s emotional caretakers that lead to low self-esteem and poor emotional self-regulation[ 93 , 95 ]. Psychodynamic therapy’s efficacy in the acute phase of MDD is well-established compared to other forms of psychotherapy.

Group therapy: The application of group therapy (GT) to MDD remains limited. Some data support the efficacy of specific types of GT inspired by CBT and IPT[ 96 - 98 ]. Group CBT for patients with subthreshold depression is an effective post-depressive-symptomatology treatment but not during the follow-up period[ 99 ]. Supportive GT and group CBT reduce depressive symptoms[ 96 ], especially in patients with common comorbid conditions[ 100 ]. However, studies are still lacking in this domain.

MBCT: MBCT is a relatively recent technique that combines elements of CBT with mindfulness-based stress reduction[ 101 ]. Studies have shown that eight weeks of MBCT treatment during remission reduces relapse. Thus, it is a potential alternative to reduce, or even stop, antidepressant treatment without increasing the risk of depressive recurrence, especially for patients at a high risk of relapse ( i.e. , patients with more than two previous episodes and patients who have experienced childhood abuse or trauma)[ 102 ].

Other psycho-interventions

Psycho-education: This type of intervention educates depressed patients and (with their permission) family members involved in the patient’s life about depression symptoms and management. This education should be provided in a language that the patient understands. Issues such as misperceptions about medication, treatment duration, the risk of relapse, and prodromes of depression should be addressed. Moreover, patients should be encouraged to maintain healthy lifestyles and enhance their social skills to prevent depression and boost their overall mental health. Many studies have highlighted the role of psycho-education in improving the clinical course, treatment adherence, and psychosocial functioning in patients with depression[ 103 ].

Physical exercise: Most guidelines for treating depression, including the National Institute for Health and Care Excellence, the American Psychiatric Association, and the Royal Australian and New Zealand College of Psychiatrists, recommend that depressed patients perform regular physical activity to alleviate symptoms and prevent relapses[ 104 ] . Exercise also promotes improvements in one’s quality of life in general[ 105 ] . However, exercise is considered an adjunct to other anti-depressive treatments[ 25 ] .

Although psychotherapy is effective for treating depression and improving patients’ quality of life, its direct actions against depressive symptoms are not fully understood[ 106 ]. Identifying factors ( e.g. , interpersonal variables) linked to treatment responses can help therapists choose the right therapeutic strategy for each patient and guide research to modify existing therapies and develop new ones[ 107 ].

Since depression is a primary care problematic, simplifying psychotherapy procedures will increase the use of psychological interventions for depression, especially in general practice. Brief forms (six to eight sessions) of CBT and PST have already shown their effectiveness for treating depression[ 108 ]. Nevertheless, simpler solutions must be made available to practitioners to help them manage and prevent depression.

SOMATIC TREATMENTS

In many situations, depression can also be managed via somatic treatments. ECT is the most well-known treatment for resistant depression, and solid evidence supports its effectiveness and safety. In recent decades, various innovative techniques have been proposed, such as repetitive transcranial magnetic stimulation (rTMS), transcranial direct current stimulation (tDCS), vagus nerve stimulation (VNS), deep brain stimulation (DBS), and magnetic seizure therapy, with varying efficiency levels[ 109 ].

ECT is arguably the most effective treatment modality in psychiatry, and its superiority over pharmacotherapy for major unipolar depression is widely supported[ 110 ]. ECT reduces the number of hospital readmissions and lightens the burden of depression, leading to a better quality of life[ 111 , 112 ].

Moreover, ECT is considered safe[ 113 ]. Advances in anesthesia and ECT techniques have decreased complications related to ECT while also improving cognitive outcomes and patient satisfaction.

However, the stigma surrounding ECT limits its use. Most misconceptions date back to early ECT techniques (when it was performed without muscle relaxants or anesthesia). Nevertheless, some people still consider ECT as the last option for treating depression, even though most studies indicate that ECT is more beneficial in patients with fewer pharmacological treatments[ 114 - 116 ].

ECT is typically recommended for patients with severe and psychotic depression, a high risk of suicide, or Parkinson’s disease, as well as pregnant patients[ 117 - 119 ]. The maintenance ECT also appears to prevent relapses[ 120 ]. The current practice of ECT continues to improve as protocols become more advanced, mainly owing to bioinformatics, and as more research is carried out in this domain[ 121 - 125 ].

This method, which is a type of biological stimulation that affects brain metabolism and neuronal electrical activity, has been widely used in research on depression[ 126 ]. Recent literature shows a significant difference between rTMS and fictitious stimulation regarding its improvements in depressive symptoms[ 127 ]. Preliminary research has revealed synergistic ( e.g. , rTMS/quetiapine) and antagonizing ( e.g. , rTMS/cannabinoid receptor (CB1) antagonist) interactions between neuro-modulation and pharmacotherapy[ 128 ]. Treatments combining rTMS and antidepressants are significantly more effective than placebo conditions, with mild side effects and good acceptability[ 129 ]. Although these results are encouraging, they remain inconsistent due to differences in rTMS treatment frequencies, parameters, and stimulation sites[ 129 ]. Therefore, clinical trials with large sample sizes are needed to specify which factors promote favorable therapeutic responses. Also, additional preclinical research should investigate the synergistic effects of other pharmacological molecules and guide integrated approaches (rTMS plus pharmacotherapy).

This technique delivers weak currents to the brain via electrodes placed on the scalp[ 130 ]. It is easy to use, safe, and tolerable[ 131 ]. The tDCS technique significantly outperforms the simulator in terms of the rate of response and remission[ 132 ]. However, its effect remains lower than that of antidepressants[ 133 ] and rTMS[ 134 ]. It can be used as a complementary intervention or as monotherapy to reduce depressive symptoms in unipolar or bipolar depression patients[ 135 ]. The antidepressant effects of tDCS may involve long-term neuroplastic changes that continue to occur even after the acute phase of treatment, which explains its delayed efficacy[ 135 ].

Recently, neurophysiological studies have shown that the clinical effects of tDCS do not have a direct linear relationship with the dose of stimulation[ 136 ]. tDCS, as a relatively simple and portable technology, is well-suited for remote supervised treatment and assessment at home, thus facilitating long treatment durations[ 136 ].

Since the optimal clinical effects of tDCS are delayed, future clinical trials should use longer evaluation periods and aim to identify responsive patients using algorithms[ 137 ].

VNS is a therapeutic method that has been used for the last sixteen years to treat resistant unilateral or bipolar depression. However, despite several clinical studies attesting to its favorable benefit-risk ratio and its approval by the Food Drug Administration in 2005, it is not used very often[ 138 ].

VNS involves the implantation of a pacemaker under the collarbone that is connected to an electrode surrounding the left vagus nerve. The left vagus nerve is preferred because it exposes the patient to fewer potential adverse cardiac effects. Indeed, most cardiac afferent fibers originate from the right vagus nerve[ 139 ]. Since the turn of the century, numerous studies have demonstrated the efficacy of VNS in resistant depression[ 140 - 142 ].

However, only one randomized, double-blind, controlled trial comparing VNS with usual medical treatment has been conducted over a short period of 10 wk[ 141 ]. Moreover, the results of this study did not indicate that the combination of VNS with typical medical treatments was better than the typical medical treatment on its own.

However, VNS has demonstrated progressively increasing improvements in depressive symptoms, with significant positive outcomes observed after six to 12 mo; these benefits can last for up to two years[ 143 ].

More long-term studies are needed to fully determine the predictors of the correct response.

According to the literature, DBS of the subgenual cingulate white matter (Brodmann area = BA 25) elicited a clinical response in 60% of resistant depression patients after six months and clinical remission in 35% of patients, with benefits maintained for over 12 mo[ 144 ]. The stimulation of other targets, in particular the nucleus accumbens, to treat resistant depression has gained interest recently. Behavioral effects indicate the quick and favorable impact of stimulation on anhedonia, with significant effects on mood appearing as early as week one after treatment begins[ 145 ].

Magnetic seizure therapy

Magnetic seizure therapy involves inducing a therapeutic seizure by applying magnetic stimulation to the brain while the patient is under anesthesia. This technique is still being investigated as a viable alternative to ECT to treat many psychiatric disorders. Evidence supporting its effectiveness on depressive symptoms continues to grow, and it appears to induce fewer neurocognitive effects than ECT[ 146 , 147 ].

Luxtherapy (phototherapy)

The first description of reduced depression symptoms due to intense light exposure was presented in 1984[ 148 ]. Optimal improvements were obtained with bright light exposure of 2500 Lux for two hours per day, with morning exposure shown to be superior to evening exposure[ 149 ].

A review and meta-analysis[ 150 ] showed that more intense (but shorter) exposures (10000 Lux for half an hour per day or 6000 Lux for 1.5 h per day) have the same efficacy. Importantly, this treatment method is effective both for those with seasonal and non-seasonal depression. Benefits of phototherapy related to sleep deprivation and drug treatments have also been reported[ 151 ].

Neuro-modulation treatments offer a range of treatment options for patients with depression. ECT remains the most documented and effective method in this category[ 151 ]. rTMS is an interesting technique as well, as it offers a well-tolerated profile[ 85 ], while tDCS offers encouraging but varying results that depend on the study’s design and the techniques used[ 130 ].

More investigations are needed to specify which indications are the best for each method according to the clinical and biological profiles of patients. The uses of such methods are expanding, probably, with their efficiency increasing when they are tailored to the patient. Furthermore, somatic interventions for depression need to be regularly assessed and integrated into psychiatrists’ therapeutic arsenals.

Treating depression is still a significant challenge. Finding the best option for each patient is the best way to obtaining short- and long-term effectiveness. The three principal methods available to caregivers are antidepressants, specifically structured psychotherapies, and somatic approaches. Research on depression pharmacotherapy continues to examine new molecules implicated in gamma-aminobutyric acid regulation and glutamate transmission. Also, efforts to personalize and simplify psychotherapeutic interventions are ongoing. Protocols using somatic interventions need to be studied in more depth, and their indications must be specified. ECT is the only somatic treatment with confirmed indications for certain forms of depression. Combinations of medications, psychotherapy, and somatic therapies remain the most effective ways to manage resistant forms of depression.

Conflict-of-interest statement: All authors declare that they have no conflict of interest related to this article.

Manuscript source: Invited manuscript

Peer-review started: March 31, 2021

First decision: June 5, 2021

Article in press: October 11, 2021

Specialty type: Medicine, research and experimental

Country/Territory of origin: Morocco

Peer-review report’s scientific quality classification

Grade A (Excellent): 0

Grade B (Very good): 0

Grade C (Good): 0

Grade D (Fair): D

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P-Reviewer: Narumiya K S-Editor: Fan JR L-Editor: A P-Editor: Fan JR

Contributor Information

Rabie Karrouri, Department of Psychiatry, Moulay Ismaïl Military Hospital, Faculty of Medicine and Pharmacy, Sidi Mohamed Ben Abdellah University, Fez 30070, Morocco.

Zakaria Hammani, Department of Psychiatry, Moulay Ismaïl Military Hospital, Faculty of Medicine and Pharmacy, Sidi Mohamed Ben Abdellah University, Fez 30070, Morocco.

Roukaya Benjelloun, Department of Psychiatry, Faculty of Medicine, Mohammed VI University of Health Sciences, Casablanca 20000, Morocco.

Yassine Otheman, Department of Psychiatry, Moulay Ismaïl Military Hospital, Faculty of Medicine and Pharmacy, Sidi Mohamed Ben Abdellah University, Fez 30070, Morocco. [email protected] .

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Enhanced hydrogen uptake of dihydrogen complex via porous materials support †.

* Corresponding authors

a Department of Chemistry, Graduate School of Science, Tohoku University, Sendai, Miyagi 980-8578, Japan E-mail: [email protected]

b Tokyo Metropolitan Industrial Technology Research Institute, 2-4-10 Aomi, Koto, Tokyo 135-0064, Japan

c Department of Chemistry, Faculty of Science, Tohoku University, 6-3 Aza-Aoba, Aramaki, Sendai 980-8578, Japan

d Physical and Chemical Research Infrastructure Group, RIKEN SPring-8 Center, RIKEN, Sayo, Hyogo, Japan

This research focuses on enhancing H 2 adsorption by using the [Mo(PCy 3 ) 2 (CO) 3 ] complex supported on porous materials such as silica gel and mesoporous carbon. The study reports a significant increase in hydrogen adsorption capacity, reaching up to 9.3 times that of the bulk complex. This improvement suggests that using mesoporous materials as supports for the [Mo(PCy 3 ) 2 (CO) 3 ] complex enhances the accessibility of H 2 gas to its open-metal sites.

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Enhanced hydrogen uptake of dihydrogen complex via porous materials support

K. Uchida, S. Tanaka, S. Adachi, H. Iguchi, R. Sakamoto and S. Takaishi, RSC Adv. , 2024,  14 , 11452 DOI: 10.1039/D4RA01182A

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