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• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

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

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

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

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

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Introduction

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

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

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

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

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

Extensive analysis identifies relationships between chemical compounds in beer

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

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

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

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

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

Tasting panel assessments reveal sensorial relationships in beer

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

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

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

Tasting panel assessments of specific flavors correlate with chemical composition

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

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

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

Public consumer reviews complement expert panel data

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

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

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

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

Models can predict beer sensory profiles from chemical data

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Model validation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Beer selection

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

Chemical dataset

Sample preparation.

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

HS-GC-FID/FPD

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

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

HS-SPME-GC-MS

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

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

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

Discrete photometric and enzymatic analysis

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

NIR analyses

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

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

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

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

Online reviews from a public database

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

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

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

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

Beer price collection and processing

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

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

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

Model dissection

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

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

Validation of causal chemical properties

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

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

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

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

Reporting summary

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

Data availability

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

Code availability

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

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Acknowledgements

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

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

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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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Citrus limon (Lemon) Phenomenon—A Review of the Chemistry, Pharmacological Properties, Applications in the Modern Pharmaceutical, Food, and Cosmetics Industries, and Biotechnological Studies

This review presents important botanical, chemical and pharmacological characteristics of Citrus limon (lemon)—a species with valuable pharmaceutical, cosmetic and culinary (healthy food) properties. A short description of the genus Citrus is followed by information on the chemical composition, metabolomic studies and biological activities of the main raw materials obtained from C. limon (fruit extract, juice, essential oil). The valuable biological activity of C. limon is determined by its high content of phenolic compounds, mainly flavonoids (e.g., diosmin, hesperidin, limocitrin) and phenolic acids (e.g., ferulic, synapic, p-hydroxybenzoic acids). The essential oil is rich in bioactive monoterpenoids such as D-limonene, β-pinene, γ-terpinene. Recently scientifically proven therapeutic activities of C. limon include anti-inflammatory, antimicrobial, anticancer and antiparasitic activities. The review pays particular attention, with references to published scientific research, to the use of C. limon in the food industry and cosmetology. It also addresses the safety of use and potential phototoxicity of the raw materials. Lastly, the review emphasizes the significance of biotechnological studies on C. limon .

1. Introduction

Citrus limon (L.) Burm. f. is a tree with evergreen leaves and yellow edible fruits from the family Rutaceae . In some languages, C. limon is known as lemon (English), Zitrone (German), le citron (French), limón (Spanish), and níngméng, 檸檬 (Chinese).

The main raw material of C. limon is the fruit, particularly the essential oil and juice obtained from it. The C. limon fruit stands out as having well-known nutritional properties, but it is worth remarking that its valuable biological activities are underestimated in modern phytotherapy and cosmetology [ 1 ].

C. limon fruit juice (lemon juice) has traditionally been used as a remedy for scurvy before the discovery of vitamin C [ 2 ]. This common use of C. limon , known since ancient times, has nowadays been supported by numerous scientific studies. Other uses for lemon juice, known from traditional medicine, include treatment of high blood pressure, the common cold, and irregular menstruation. Moreover, the essential oil of C. limon is a known remedy for coughs [ 3 , 4 , 5 ].

In Romanian traditional medicine, C. limon essential oil was administered on sugar for suppressing coughs [ 3 ]. Aside from being rich in vitamin C, which assists in warding off infections, the juice is traditionally used to treat scurvy, sore throats, fevers, rheumatism, high blood pressure, and chest pain [ 6 ].

In Trinidad, a mixture of lemon juice with alcohol or coconut oil has been used to treat fever, coughs in the common cold, and high blood pressure. Moreover, the juice or grated skin, mixed with molasses, has been used to remove excess water from the body, and the juice mixed with olive oil has been administered for womb infection and kidney stones [ 4 ]. According to Indian traditional medicine, C. limon juice can induce menstruation; the recommended dose for this is two teaspoons consumed twice a day [ 5 ].

Currently, valuable scientific publications focus on the ever wider pharmacological actions of C. limon fruit extract, juice and essential oil. They include studies of, for example, antibacterial, antifungal, anti-inflammatory, anticancer, hepatoregenerating and cardioprotective activities [ 7 , 8 , 9 , 10 , 11 ].

The pharmacological potential of C. limon is determined by its rich chemical composition. The most important group of secondary metabolites in the fruit includes flavonoids and also other compounds, such as phenolic acids, coumarins, carboxylic acids, aminoacids and vitamins. The main compounds of essential oil are monoterpenoids, especially D-limonene. These valuable chemical components are the reason for the important position of C. limon in the food and cosmetics industries [ 12 , 13 , 14 ].

The aim of this overview is a systematic review of scientific works and in-depth analyses of the latest investigations and promotions related to C. limon as a valuable plant species, important in pharmacy, cosmetology and the food industry. Additionally, relevant biotechnological investigations are presented.

2. The Genus Citrus

The genus Citrus is one of the most important taxonomic subunits of the family Rutaceae . Fruits produced by the species belonging to this genus are called ‘citrus’ in colloquial language, or citrus fruits. Citrus fruits are commonly known for their valuable nutritional, pharmaceutical and cosmetic properties. The genus Citrus includes evergreen plants, shrubs or trees (from 3 to 15 m tall). Their leaves are leathery, ovoid or elliptical in shape. Some of them have spikes. The flowers grow individually in leaf axils. Each flower has five petals, white or reddish. The fruit is a hesperidium berry. The species belonging to the genus Citrus occurs naturally in areas with a warm and mild climate, mainly in the Mediterranean region. They are usually sensitive to frost [ 2 ].

One of the best known and most used species of the genus Citrus is the lemon— Citrus limon (L.) Burm. f. (Latin synonyms: C. × limonia , C. limonum ). Other important species included in this taxonomic unit are: Citrus aurantium ssp. aurantium —bitter orange, Citrus sinensis —Chinese orange, Citrus reticulata —mandarin, Citrus paradise —grapefruit, Citrus bergamia —bergamot orange, Citrus medica —citron, and many others. A team of scientists from the University of California (Oakland, California, USA) [ 15 ] analyzed the origin of several species of the genus Citrus , including C. limon . They found that C. limon was a plant that had formed as a result of the combination of two species— C. aurantium and C. medica . In the studies of scientists from Southwest University of China (Chongqing, China), the metabolite profiles of C. limon, C. aurantium and C. medica were evaluated using gas chromatography–mass spectrometry (GC-MS) and the partial least squares discriminant analysis (PLS-DA) score plot [ 16 ]. They proved that C. limon has a smaller distance between C. aurantium and C. medica in comparison with other Citrus species. These studies demonstrated that C. limon was likely a hybrid of C. medica and C. aurantium, as previously suspected [ 16 ].

Botanical classification of the species of the genus Citrus is very difficult due to the frequent formation of hybrids and the introduction of numerous cultivars through cross-pollination. Hybrids are produced to obtain fruit with valuable organoleptic and industrial properties, including seedless fruit, high juiciness, and the required taste. For older varieties, hybrids and cultivars, the latest molecular techniques are often needed to identify them. C. limon , like many other prolific citrus species, gives rise to numerous varieties, cultivars and hybrids, which are presented in Table 1 and Table 2 acc. to [ 17 ].

C. limon cultivars.

Hybrids of C. limon .

One of the oldest preserved botanical sources describing species of the genus Citrus is the “Monograph on the Oranges of Wên-chou” (in Chinese: 記 嘉 桔 錄, “Citrus records of Ji Jia”) by Han Yanzhi from 1178 [ 18 , 19 ]. Other historical works describing the species bearing citrus fruits are “Nürnbergische Hesperides” from 1708 and “Traité du Citrus” from 1811. Historically, one of the best known classifications of citrus species is “Histoire Naturelle des Orangers” from 1818. The American botanist Walter Tennyson Swingle (1871–1952) had a particularly significant impact on the present-day taxonomy of the genus Citrus . He is the author of as many as 95 botanical names of species of the genus Citrus . Currently, the systematics of the species of the genus Citrus are based on studies of molecular markers and other DNA analysis technologies still provide new information [ 20 ].

3. Botanical Characteristics and Occurrence of C. limon

Citrus limon (L.) Burm. f. (lemon) is a tree reaching 2.5–3 m in height. It has evergreen lanceolate leaves. Bisexual flowers are white with a purple tinge at the edges of the petals. They are gathered in small clusters or occur individually, growing in leaf axils. The fruit is an elongated, oval, pointed green berry that turns yellow during ripening. Inside, the berry is filled with a juicy pulp divided into segments (like an orange). The C. limon pericarp is made of a thin, wax-covered exocarp, under which there is the outer part of the mesocarp, also known as flavedo. This part contains oil vesicles and carotenoid dyes. The inner part of the mesocarp, also known as the albedo, is made of a spongy, white parenchyma tissue. The endocarp, or ‘fruit flesh’, is divided into segments by the spongy, white tissue of the mesocarp [ 2 ].

The C. limon tree prefers sunny places. It grows on loamy, well-drained, moist soils with a wide pH range [ 1 , 2 ].

The location of the original natural habitat of C. limon is not accurately known [ 1 , 21 ]. However, C. limon is considered to be native to North-Western or North-Eastern India [ 2 , 17 ].

C. limon is mainly recognized as a cultivated species. It has been cultivated in southern Italy since the 3rd century AD, and in Iraq and Egypt since 700 AD. The Arabs introduced C. limon into Spain, where it has been cultivated since 1150. Marco Polo’s expeditions also brought C. limon to China in 1297. It was also one of the first new species that Christopher Columbus brought in the form of seeds to the North American continent in 1493. In the 19th century, worldwide commercial production of C. limon began in Florida and in California. Nowadays, the USA is the largest producer of C. limon . Italy, Spain, Argentina and Brazil also play a significant role [ 17 ].

4. C. limon Pharmacopoeial Monographs and Safety of Use

By cold-pressing the fresh outer parts of the C. limon pericarp (Lat. exocarpium ), an essential oil is obtained—the lemon oil (lat. Citrus limon aetheroleum , Limonis aetheroleum , Oleum Citri ). The oil is colourless or yellow, and has a characteristic, strong lemon scent [ 21 ]. It is considered a pharmacopoeial raw material. Its monographs, entitled ‘ Limonis aetheroleum ’, are present in the European Pharmacopoeia 9th [ 22 ], American Pharmacopoeia [ 23 ], and in the Ayurvedic Pharmacopoeia of India [ 24 ].

Another pharmacopoeial raw material obtained from C. limon is the outer part of the mesocarp —the flavedo . A monograph entitled ‘ Citrus limon flavedo ’ can be found in older editions of the French Pharmacopoeia, for example, in its 10th edition from 1998 [ 25 ].

The fresh fruit of C. limon is officially listed for use in phytotherapy and in homeopathy in Germany. According to the German Commission D Monographs for homeopathic medicines, C. limon fresh fruits can be used for treating gingival bleeding and debilitating diseases [ 26 ].

C. limon also has a positive recommendation in the European Commission’s Cosmetics Ingredients Database (CosIng Database) as a valuable plant for cosmetics’ production [ 27 ].

The European Food Safety Authority (EFSA) classified the pericarp, fruit, and leaves of C. limon as raw materials of plant origin, in which there is presence of naturally occurring ingredients that may pose a threat to human health when used in the production of food and dietary supplements. EFSA has remarked that the toxic substances in these raw materials are photosensitizing compounds belonging to the furanocoumarin group, including bergapten and oxypeucedanin ( Figure 1 ) [ 28 ].

Chemical structure of selected linear furanocoumarins, determining the photosensitizing effect of C. limon.

In the American Food and Drug Administration (FDA) list, C. limon essential oil and extracts are classified as safe products [ 29 ].

5. Chemical Composition of C. limon

The chemical composition of C. limon fruit is well known. It has not only been determined for the whole fruit but also separately for the pericarp, juice, pomace, and essential oil. The compositions of the leaves and the fatty oil extracted from C. limon seeds are also known. Due to the large number of C. limon varieties, cultivars and hybrids, various research centres undertake the task of analyzing the chemical composition of the raw materials obtained from them.

The most important group of bioactive compounds in both C. limon fruit and its juice, determining their biological activity, are flavonoids such as: flavonones—eriodictyol, hesperidin, hesperetin, naringin; flavones—apigenin, diosmin; flavonols—quercetin; and their derivatives ( Figure 2 ). In the whole fruit, other flavonoids are additionally detected: flavonols—limocitrin ( Figure 2 ) and spinacetin, and flavones—orientin and vitexin ( Table 3 and Table 4 ). Some flavonoids, such as neohesperidin, naringin and hesperidin ( Figure 2 ), are characteristic for C. limon fruit. In comparison to another Citrus species, C. limon has the highest content of eriocitrin ( Figure 2 ) [ 30 ].

Chemical structure of flavonoids characteristic of C. limon.

Composition of C. limon fruits extracts.

Composition of C. limon juice.

Phenolic acids are another important group of compounds found both in the juice and fruit. There are mainly two such compounds in the juice—ferulic acid and synapic acid, and their derivatives. In contrast, the presence of p-hydroxybenzoic acid has been confirmed in the fruit. In the fruit, there are also coumarin compounds, carboxylic acids, carbohydrates, as well as amino acids, a complex of B vitamins, and, importantly, vitamin C (ascorbic acid) ( Table 3 and Table 4 ) [ 1 , 12 , 13 , 31 , 32 , 33 , 34 , 35 , 36 ].

Another interesting group of compounds that are found in C. limon fruits are limonoids. Limonoids are highly oxidized secondary metabolites with polycyclic triterpenoid backbones. They mainly occur in citrus fruits, including lemons, in which they are found mainly in the seeds, pulp, and peel. There are predominantly two such compounds in C. limon fruits—limonin and nomilin ( Figure 3 ) [ 37 ]. Studies have shown that the concentrations of the compounds of this group are dependent on fruit growth and maturation stages. Young citrus fruits contain the highest amounts of these compounds, compared to ripe ones [ 38 ].

Chemical structure of limonoids characteristic of C. limon.

Analysis of macroelements in C. limon fruit showed the presence in pulp and peel of: calcium (Ca), magnesium (Mg), phosphorus (P), potassium (K) and sodium (Na) [ 36 ].

In C. limon seed oil, the main ingredients are fatty acids, such as arachidonic acid, behenic acid and linoleic acid, and also tocopherols and carotenoids ( Table 5 ) [ 33 , 35 ]. The latest studies showed that C. limon fruit pulp oil contains more fatty acids compared to other Citrus species, such as C. aurantium , C. reticulata and C. sinensis. The following fatty acids have been identified in C. limon pulp oil: behenic acid, erucic acid, gondoic acid, lauric acid, linoleic acid, α-linolenic acid, margaric acid, palmitic acid, palmitoleic acid, pentadecanoic acid, and stearic acid [ 39 ].

Composition of oil from C. limon seeds.

The main components of the C. limon essential oil are monoterpenoids. Among them, quantitatively dominant in the essential oil obtained from pericarp are: limonene (69.9%), β-pinene (11.2%), γ-terpinene (8.21%), ( Figure 4 ), sabinene (3.9%), myrcene (3.1%), geranial (E-citral, 2.9%), neral (Z-citral, 1.5%), linalool (1.41%). In addition to terpenoids, the essential oil also contains linear furanocoumarins (psoralens) and polymethoxylated flavones ( Table 6 ) [ 14 , 40 , 41 ].

Chemical structure of selected terpenoids characteristic of C. limon essential oil.

The chemical composition of the essential oil of the C. limon pericarp and leaf.

The essential oil of the C. limon leaf differs in composition from oil obtained from pericarp. Its main compounds include: limonene (31.5%), sabinene (15.9%), citronellal (11.6%), linalool (4.6%), neral (4.5%), geranial (4.5%), (E)-β-ocimene (3.9%), myrcene (2.9%), citronellol (2.3%), β-caryophyllene (1.7%), terpne-4-ol (1.4%), geraniol (1.3%) and α-pinene (1.2%) ( Table 6 ) [ 14 , 16 , 40 , 41 , 42 , 43 ].

6. Metabolomic Profile Studies

The team of Mucci et al. [ 35 ] investigated the metabolic profile of different parts of C. limon fruit. Flavedo, albedo, pulp, oil glands, and the seeds of lemon fruit and citron were studied through high resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy. The analyses were made directly on intact tissues without any physicochemical manipulation. In C. limon flavedo were detected: terpenoids (limonene, β-pinene and γ-terpinene), aminoacids (asparagine, arginine, glutamine, proline), organic acids (malic acid and quinic acid), osmolites (stachydrine), and fatty acid chains and sugars (glucose, fructose, β-fructofuranose, myoinositol, scylloinositol and sucrose) ( Table 3 ). The albedo of C. limon fruit showed the presence of low signals from: aminoacids (alanine, threonine, valine, glutamine), sugars (glucose, sucrose, β-fructofuranose, myoinosytol, scylloinositol and β-fructopyranose), and osmolites (stachydrine, β-hydroxybutyrate, ethanol) ( Table 3 ). In albedo, clear signals from flavonoids were detected, such as hesperidin and rutoside, that have been identified also by high performance liquid chromatography (HPLC) analyses. Oil glands’ HR-MAS NMR composition analysis showed the presence of terpenoids (limonene, γ-terpinene, β-pinene, α-pinene, geranial, neral, citronellal, myrcene, sabinene, α-thujene, nerol and geraniol esters) and sugars (glucose, sucrose, β-fructofuranose and β-fructopyranose). The analysis of C. limon pulp showed the presence of aminoacids (asparagine, proline, alanine, γ-aminobutyric acid (GABA), glutamine, threonine and valine), organic acids (citric acid and malic acid), sugars (glucose, sucrose, β-fructofuranose, β-fructopyranose, myoinosytol and scylloinosytol) and osmolites (stachydrine, ethanol and methanol) ( Table 3 ). HR-MAS NMR seeds analysis indicated that their composition is dominated by triglyceride signals (linoleic acid, linolenic acid and their derivatives), sugars (glucose and sucrose), osmolites (stachydrine) and trigonelline [ 35 ].

In another metabolomic study, the peel extracts of ripened C. limon fruit was characterized as containing nonfluorescent chlorophyll catabolites (NCCs) and dioxobilane-type nonfluorescent chlorophyll catabolite (DNCC) [ 44 ]. In the peels of C. limon fruit, four chlorophyll catabolites were detected: Cl-NCC1, Cl-NCC2, Cl-NCC3 and Cl-NCC4 [ 44 ].

The metabolomic profile of C. limon leaf was investigated by Asai et al. [ 45 ]. The studies showed that C. limon leaves contain 26 different organic acids and their derivatives (aconitic acid, 2-aminobutyric acid, 4-aminobutyric acid, ascorbic acid, benzoic acid, citramalic acid, citric acid, p-coumaric acid, ferulic acid, fumaric acid, glucaric acid, glycolic acid, 3-hydroxybutyric acid, 2-isopropylmalic acid, malic acid, malonic acid, 3-methylglutaric acid, oxamic acid, D-3-phenyllacetic acid, pipecolic acid, pyruvic acid, quinic acid, shikimic acid, succinic acid, threonic acid, urocanic acid), 21 aminoacids (alanine, γ-aminobutyric acid, anthranilic acid, asparagine, aspartic acid, glutamic acid, glutamine, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, pyroglutamic acid, serine, threonine, tryptophan, tyrosine, valine), and 13 sugars and sugar alcohols (arabinose, fructose, galactose, glucose, glycerol, inositol, lyxose, maltose, rhamnose, ribose, sorbose, sucrose, xylitol). Additionally, studied leaves have been exposed to stress conditions (leaves were placed in such a way that the edge of the petiole was in contact with the bottom of a glass bottle, soaked with 0.2 mM jasmonic acid and salicylic acid aqueous solutions, and incubated at 25 °C for 24 h). The content of aminoacids, such as, tyrosine, tryptophan, phenylalanine, valine, leucine, isoleucine, lysine, methionine, threonine, histidine, and γ-aminobutyric acid, was increased after this stress treatment [ 45 ].

According to Mehl et al. [ 46 ], the identification of volatile and non-volatile metabolites in C. limon essential oil is dependent on geographic origin and the analytical methods used. To evaluate the potential of volatile and non-volatile fractions for classification purposes, volatile compounds of cold-pressed lemon oils were analyzed, using modern methods like gas chromatography-flame ionization detector-mass spectrometer (GC-FID/MS) and fourier transform mid-infrared spectroscopy (FT-MIR), while the non-volatile residues were studied using FT-MIR with proton nuclear magnetic resonance ( 1 H-NMR) and ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-TOF-MS). The studies lead to very good differentiation and classification of samples regarding their geographic origin and extraction process modalities. The essential oil from the Italian-originated C. limon fruit was enriched in α-thujene, α-pinene, α-terpinene, sesquiterpenoids (i.e., β-caryophyllene) and furocoumarins (i.e., bergamottin). The essential oil from Spanish and Argentinian C. limon fruit was characterized by significant terpene contents, such as limonene, but differed in imperatorin, and byakangelicol contents. The studies showed that essential oil from Spanish C. limon fruit contained more camphor and 4-terpineol, while Argentinian C. limon fruit contained more sabinene and cis-sabinene hydrate [ 46 ].

The studies performed by Jing et al. [ 16 ] focused on the identification of components in the essential oil of different Citrus species, including C. limon . In general, most of the studied essential oil components were identified as monoterpenoids. The major monoterpenes in C. limon essential oil were: limonene (70.37%), p-mentha-3,8-diene (18.00%), myrcene (4.40%), α-pinene (3.24%), α-thujene (1.05%) and terpinolene (0.90%) ( Table 6 ). Other monoterpenoids, which were identified as characteristic of C. limon, were: sabinene (0.28%), α-terpinene (0.22%), trans-muurola-4(14), 5-diene (0.18%), eucalyptol (0.12%), octanol acetate (0.03%), β-curcumene (0.03%), zonarene (0.03%), 7-epi-sesquithujene (0.02%), citronellyl acetate (0.02%), α-farnesene (0.01%) ( Table 6 ). The shown metabolite-based profiling model can be used to clearly discriminate the basic Citrus species. Limonene, α-pinene, sabinene and α-terpinene were the major characteristic components of the analyzed metabolomes of Citrus genotypes that contributed to their taxonomy [ 16 ].

Studies performed by Masson et al. [ 43 ] deal with furanocoumarin’s and coumarin’s metabolomic profile in essential oil from C. limon fruit peel. C. limon essential oil contained large amounts of both furanocoumarins and coumarins compared to another tested Citrus essential oils. In C. limon essential oil, 13 furanocoumarins were detected (bergamottin, bergapten, byakangelicol, byakangelicin, epoxybergamottin, 8-geranyloxypsoralen, heraclenin, imperatorin, isoimperatorin, isopimpinellin, oxypeucedanin, oxypeucedanin hydrate, phellopterin) and two coumarins (citropten and herniarin) ( Table 6 ) [ 43 ].

7. Biological Activity of C. limon Raw Materials

7.1. anticancer activity.

C. limon nanovesicles have been isolated from the fruit juice using the ultracentrifugation method and purification on a 30% sucrose gradient, using an in vitro approach. The study showed that isolated nanovesicles (20 µg/mL) inhibited cancer cell proliferation in different tumour cell lines, by activating a TRAIL-mediated apoptotic cell death. Furthermore, C. limon nanovesicles suppress chronic myeloid leukemia (CML) tumour growth in vivo by specifically reaching the tumour site and by activating TRAIL-mediated apoptotic cell processes ( Table 7 ) [ 47 ].

Biological activity of C. limon fruit extracts confirmed by scientific research.

Another study has shown that an 80:20 methanol:water extract from lemon seeds induces apoptosis in human breast adenocarcinoma (MCF-7) cells, leading to the inhibition of proliferation. This extract showed the highest (29.1%) inhibition of MCF-7 cells in an MTT assay (Cell Proliferation Kit), compared to ethyl acetate, acetone and methanol extracts. The results suggest that aglycones and glycosides of the limonoids and flavonoids present in the 80:20 methanol:water extract may potentially serve as a chemopreventive agent for breast cancer ( Table 7 ) [ 9 ].

7.2. Antioxidant Activity

It has been shown that the antioxidant activity of the flavonoids from C. limon —hesperidin and hesperetin—was not only limited to their radical scavenging activity but also augmented the antioxidant cellular defences via the ERK/Nrf2 signalling pathway ( Table 7 ) [ 8 ].

In addition, vitamin C prevents the formation of free radicals and protects DNA from mutations. Studies have also shown a reduction in lipid peroxidation in seizures and status epilepticus was induced by pilocarpine in adult rats [ 48 ].

7.3. Anti-Inflammatory Activity

Various in vitro and in vivo studies have been conducted to evaluate hesperidin metabolites, or their synthetic derivatives, at their effectiveness in reducing inflammatory targets including NF-κB, iNOS, and COX-2, and the markers of chronic inflammation ( Table 7 ) [ 8 ].

The essential oil from C. limon (30 or 10 mg/kg p.o .) exhibited anti-inflammatory effects in mice under formalin test by reducing cell migration, cytokine production and protein extravasation induced by carrageenan. These effects were also obtained with similar amounts of pure D-limonene. The anti-inflammatory effect of C. limon essential oil is probably due to the high concentration of D-limonene ( Table 8 ) [ 49 ].

Biological activity of C. limon essential oil confirmed by scientific research.

Studies by Mahmoud et al. [ 50 ] have shown the protective effects of limonin on experimentally induced hepatic ischemia reperfusion (I/R) injury in rats. The mechanism of these hepatoprotective effects was related to the antioxidant and anti-inflammatory potential of limonin mediated by the down-regulation of the TLR-signaling pathway [ 50 ].

In studies with the essential oil administered at a dose of 10 mg/kg p.o. , D-limonene induced a significant reduction in intestinal inflammatory scores, comparable to that induced by ibuprofen. The studies documented that D-limonene-fed rats had significantly lowered serum concentrations of TNF-α compared to untreated TNBS-colitis rats. The anti-inflammatory effect of D-limonene also involved the inhibition of TNFα-induced NF-κB translocation in fibroblast cultures. The application of D-limonene in colonic HT-29/B6 cell monolayers increased epithelial resistance. The study found evidence that IL-6 markedly decreased during dietary supplementation with D-limonene [ 51 ]. Another study showed that the oil moderately inhibited soybean 5-lipoxygenase (5-LOX) with an IC 50 value of 32.05 μg/mL ( Table 8 ) [ 52 ].

7.4. Antimicrobial Activity

Acetone extracts from C. limon fruits have shown inhibitory activity against the Gram-positive bacteria Enterococcus faecalis (MIC 0.01 mg/mL) and Bacillus subtilis (MIC 0.01 mg/mL), and the Gram-negative Salmonella typhimurium (MIC 0.01 mg/mL) and Shigella sonnei (MIC 0.01 mg/mL) ( Table 7 ) [ 7 ].

Moreover, under another study, C. limon essential oil showed antibacterial activity against Gram-positive bacteria ( Bacillus subtilis (MIC 2 mg/mL), Staphylococcus capitis (MIC 4 mg/mL), Micrococcus luteus (MIC 4 mg/mL)), and Gram-negative ( Pseudomonas fluorescens (MIC 4 mg/mL), Escherichia coli (100% inhibition)) ( Table 8 ) [ 52 , 53 ].

The C. limon essential oil exhibits inhibitory activity against Staphylococcus mutans (MIC 4.5 mg/mL) and effectively reduced the adherence of S. mutans on a glass surface, with adherence inhibition rates (AIR) from 98.3% to 100%, and on a saliva-coated enamel surface, for which the AIRs were from 54.8% to 79.2%. It effectively reduced the activity of glucosyltransferase (Gtf) and the transcription of Gtf in a dose-dependent manner ( Table 8 ) [ 54 ].

Ethanol and acetone extracts from fruits of C. limon were active against Candida glabrata (MIC 0.02 mg/mL) ( Table 7 ) [ 7 ]. On the other hand, C. limon essential oil ingredients, such as D-limonene, β-pinene and citral, have shown inhibitory activity against Aspergillus niger (MIC 90 µL/mL at 70 °C), Saccharomyces cerevisiae (MIC 4 mg/mL) and Candida parapsilosis (MIC 8 mg/mL) ( Table 8 ) [ 52 , 55 ]. Another study confirmed that C. limon essential oil promoted a 100% reduction in the growth of C. albicans [ 56 ].

Moreover, other studies have shown that C. limon essential oil at a concentration of 0.05% inhibits Herpes simplex replication to the extent of 33.3% ( Table 8 ) [ 57 ].

7.5. Antiparasitic Effect

The effect of C. limon essential oil on Sarcoptes scabiei var. cuniculi has been evaluated in vitro and in vivo. The infected parts of rabbits were treated topically once a week for four successive weeks. In vitro application results showed that C. limon essential oil (10% and 20%, diluted in water) caused mortality in 100% of mites after 24 h post-application. In vivo application of 20% lemon oil on naturally infected rabbits showed complete recovery from clinical signs and absence of mites in microscopic examination from the second week of treatment ( Table 8 ) [ 58 ].

7.6. Anti-Allergic Effect

Aqueous extracts from the peel of C. limon fruits have been used to investigate their effects on the release of histamine from rat peritoneal exudate cells (PECs). The extracts inhibited the release of histamine from rat PECs induced by the calcium ionophore A23187. Heating the extracts at 100 °C for 10 min. enhanced the inhibition of histamine release. Histamine release was inhibited to the extent of 80%. The extracts potentially suppressed inflammation in mice cavity, like indometacin, a well-known anti-inflammatory drug ( Table 7 ) [ 59 ].

7.7. Hepatoregenerating Effect

An ethanolic extract of C. limon fruits has been evaluated for its effects on experimental liver damage induced by carbon tetrachloride (CCl 4 ), and the ethyl acetate soluble fraction of the extract has been evaluated for its effect on the HepG2 cell line (human liver cancer cell line). The ethanolic extract (150 mg/mL) normalized the levels of aspartate aminotransferase (ASAT), alanine aminotransferase (ALAT), alkaline phosphatase (ALP), and total direct bilirubin, which had been altered due to CCl 4 intoxication in rats. After treatment with the extract, the level of malondialdehyde in the liver tissue was significantly reduced, hence the lipid peroxidation, and raised the level of the antioxidant enzymes superoxide dismutase and catalase. It improved the reduced glutathione levels in the treated rats in comparison with CCl 4 -intoxicated rats. The effect seen was dose dependent, and the effect of the highest dose was almost equal to the standard—silymarin. In an investigation carried out on a human liver-derived HepG2 cell line, a significant reduction in cell viability was observed in cells exposed to CCl 4 ( Table 7 ) [ 10 ].

Studies with C. limon essential oil have also shown the stimulation of liver detoxification by the activation of cytochrome P 450 and liver enzymes (glutathione S-transferase) in chronic liver poisoning ( Table 8 ) [ 21 ].

7.8. Antidiabetic Effect

Ethanol extracts from C. limon peel were administered orally at a dose of 400 mg/kg daily for 12 days to diabetic rats in which diabetes had been induced by the use of streptozotocin. The study showed a reduction in blood glucose, a reduction in wound healing time, and an increase in tissue growth rate, collagen synthesis, and protein and hydroxyproline levels ( Table 7 ) [ 60 ].

Another study evaluated the antidiabetic effect of D-limonene in streptozotocin-induced diabetic rats. D-limonene was administered orally at doses of 50, 100 and 200 mg/kg body weight, and glibenclamide at a dose of 600 µg/kg body weight, daily for 45 days. The administration of D-limonene for 45 days gradually decreased the blood glucose level, and the maximum effect was observed at a dose of 100 mg/kg body weight. The activities of gluconeogenic enzymes, such as glucose 6-phosphatase and fructose 1,6-bisphosphatase, were increased, and the activity of the glycolytic enzyme, glucokinase, was decreased, along with liver glycogen, in the diabetic rats. The effect of D-limonene was more pronounced at the dose of 100 mg/kg body weight than at the two smaller doses. The antidiabetic effect of D-limonene was comparable with that of glibenclamide ( Table 8 ) [ 61 ].

7.9. Anti-Obesity Activity

In a study, lemon juice was used in a low-calorie diet (‘lemon detox diet’). The diet consisted of 2 L of lemon detox juice containing 140 g ‘Neera’ syrup, 140 g lemon juice, and 2 L water per day. The study showed that C. limon juice caused a reduction in serum high-sensitive C-reactive protein (hs-CRP) in comparison with the placebo and normal diet group. Haemoglobin and haematocrit levels remained stable in the group on the lemon detox diet, while they decreased in the placebo and normal diet groups ( Table 7 ) [ 62 ].

Studies have shown that D-limonene is beneficial to people with dyslipidaemia and hyperglycaemia. D-limonene at a dose of 400 mg/kg per day for 30 days promotes in male rats a decrease in LDL-cholesterol, prevents the accumulation of lipids, and affects the blood sugar level. Its antioxidant action enhances these effects. Dietary supplementation with D-limonene would restore pathological alteration of the liver and pancreas. It could help in the prevention of obesity ( Table 8 ) [ 21 ].

7.10. Effects on the Digestive System

Studies have shown that D-limonene increases gastric motility and causes a reduction in nausea, neutralization of stomach acids, and relief of gastric reflux ( Table 8 ) [ 21 ].

7.11. Effects on the Cardiovascular System

A study has indicated that daily intake of C. limon juice has a beneficial effect on blood pressure. The study was conducted on 100 middle-aged women in an island area nearby Hiroshima. Instances of lemon juice ingestion and the number of steps walked had been recorded for five months. The results indicated that daily lemon juice intake and walking were effective in reducing high blood pressure because both showed significant negative correlations with systolic blood pressure ( Table 7 ) [ 63 ].

In vitro and in vivo studies have confirmed that C. limon juice (0.4 mL/kg) has a significant impact on blood pressure and on coagulation and anticoagulation factors in rabbits. In vitro tests revealed a highly significant increase in thrombin time and activated partial thromboplastin time by C. limon , whereas fibrinogen concentration was significantly reduced in comparison with the control; prothrombin time, however, was not affected significantly. Significant changes were observed in haematological parameters, such as amounts of erythrocytes and haemoglobin and mean corpuscular haemoglobin concentrations, in in vivo testing of C. limon . Bleeding time and thrombin time were significantly prolonged, and there was an increase in protein C and thrombin–antithrombin complex levels ( Table 7 ) [ 11 ].

7.12. Influence on the Nervous System

The influence of C. limon juice on the memory of mice has been investigated using Harvard Panlab Passive Avoidance response apparatus, controlled through the LE2708 Programmer. Passive Avoidance is a fear-motivated test used to assess the short- or long-term memory of small animals, which measures the latency in entering a black compartment. Animals that were fed C. limon juice (0.2, 0.4 and 0.6 mL/kg) showed, in comparison with the control, a highly significant or a significant increase in latency before entering a black compartment after 3 and 24 h, respectively ( Table 7 ) [ 64 ].

Studies have also shown that the main compound of C. limon essential oil—D-limonene—in concentrations of 0.5% and 1.0%, administered to mice by inhalation, has a significant calming and anxiolytic effect by activating serotonin and dopamine receptors. In addition, D-limonene has an inhibitory effect on pain receptors, similar to that of indomethacin and hyoscine ( Table 8 ) [ 65 ].

7.13. Influence on Skeletal System

Studies have shown the potential use of nomilin for the inhibition of osteoclastogenesis in vitro. Cell viability of the mouse RAW264.7 macrophage cell line and mouse primary bone-marrow-derived macrophages (BMMs) with the Cell Counting Kit (Dojindo Laboratories, Kumamoto, Japan) was measured. Nomilin caused significantly decreased TRAP-positive multinucleated cell numbers (a measure of osteoclast cell numbers) when compared with the control. Moreover, the non-toxic concentrations of the compound decreased bone resorption activity and down regulated osteoclast-specific genes (NFATc1 and TRAP mRNA levels), coupled with suppression of the MAPK signaling pathway. Studies have shown the therapeutic potential of nomilin for the prevention of bone metabolic diseases such as osteoporosis [ 66 ].

7.14. C. limon as Corrigent in Pharmacy

In addition to the very important uses mentioned above, the oil is used in pharmacy and cosmetic formulations as a flavour and aroma corrigent, as well as a natural preservative, due to its confirmed antibacterial and fungistatic effects [ 21 ].

8. C. limon in the Food Industry

Due to the rich chemical composition of C. limon fruit and other lemon-derived raw materials, they have applications in the food industry and in food processing. The lemon fruit is used mainly as a fresh fruit, but it is also processed to make juices, jams, jellies, molasses, etc. [ 41 ]. Fresh lemon fruit can be kept for several months, maintaining their levels of juice, vitamins, minerals, fibre, and carbohydrates. The vitamin C (ascorbic acid) content in lemon fruits and juices decreases during storage and industrial processing. The factors lowering this content are: oxygen, heat, light, time, storage temperature and storage duration. To prevent the reduction in the ascorbic acid levels and antioxidant capacity of both the lemon fruit and lemon juice, they should be kept at 0–5 °C and protected from water loss by proper packaging, with high relative humidity during distribution. Under such conditions, lemon products show a good retention of vitamin C and antioxidant capacity [ 41 , 74 ].

C. limon peel is rich in pectin, which is used in a wide range of food industrial processes as a gelling agent, including the production of jams and jellies, and as thickener, texturizer, emulsifier and stabilizer in dairy products. Due to its jellifying properties, the pectin is also used in pharmaceutical, dental and cosmetic formulations [ 75 ].

Lemon juice is used as an ingredient in beverages, particularly lemonade and soft drinks, and in other foods, such as salad dressings, sauces, and baked products. Lemon juice is a natural flavouring and preservative, and it is also used to add an acidic, or sour, taste to foods and soft drinks [ 41 , 76 ].

C. limon is the most suitable, being free from pesticide residues, raw material for enhancing the flavour of liqueurs, e.g., “limoncello”, the traditional liqueur of Sicily. It is made by the maceration of lemon peel in ethanol, water and sugar [ 41 , 76 ].

Currently, the essential oil from lemon, i.e., pure isolated linalol and citral, are used mainly as a flavouring and natural preservative due to their functional properties (antimicrobial, antifungal, etc.) [ 52 , 53 ]. In particular, they are often used to extend the short shelf-life of seafood products and in the production of some types of cheese because they significantly reduces populations of microorganisms, especially those from the family Enterobacteriaceae [ 41 , 76 ].

9. Cosmetological Applications

C. limon fruit extracts and essential oil, as well as the active compounds isolated from these raw materials, have become the object of numerous scientific studies aimed at proving the possibility of their use in cosmetology. Lemon-derived products have long been credited with having a positive effect on acne-prone skin that is easily affected by sunburn or mycosis. In this regard, traditional uses of this raw materials are known in various parts of the world. In Tanzania, the fruit juice of C. limon is mixed with egg albumin, honey and cucumber, and applied to the skin every day at night to smooth the facial skin and treat acne [ 77 ]. Juice from freshly squeezed fruit of C. limon mixed with olive oil is used as a natural remedy for the treatment of hair and scalp disorders in the West Bank in Palestine [ 78 ]. Currently, knowledge of the cosmetic activity of C. limon is constantly expanding.

C. limon essential oil shows antibiotic and flavouring properties, and for this reason it is used in formulations of shampoos, toothpaste, disinfectants, topical ointments and other cosmetics [ 41 ].

Scientific studies have shown a significant antioxidant effect of C. limon fruit extracts, which is the reason they are recommended for use in anti-ageing cosmetics [ 8 , 48 ]. The use of different carriers for C. limon extracts (e.g., hyalurosomes, glycerosomes) in cosmetics production technology contributes to an even greater inhibition of oxidative stress in skin-building structures, including keratinocytes and fibroblasts ( Table 9 ) [ 79 ]. In addition, vitamin C from C. limon is used as an ingredient in specialized dermocosmetics. Its external use increases collagen production, which makes the skin smoother and more tense. It is used in anti-aging products, to reduce shallow wrinkles, and as a synergistic antioxidant in combination with vitamin E [ 48 ].

Biological activity of C. limon fruit extracts, essential oil and its ingredients compounds significant from the cosmetics point of view, confirmed by scientific research.

The ingredients of C. limon essential oil (including citral, β-pinene, D-limonene), due to the inhibiting activity of tyrosinase and the inhibition of L-dihydroxyphenylalanine (L-DOPA) oxidation, have a depigmenting effect [ 80 ]. In addition, the essential oil has been proven to support the penetration of lipids as well as water-soluble vitamins. It can be used as a promoter of penetration of active substances through the skin [ 81 ]. Moreover, besides the direct effect on the skin, the essential oil can also be used as a natural preservative and as a corrigent in cosmetic products. Studies have confirmed its antibacterial and fungistatic effects ( Table 9 ) [ 7 , 52 , 53 ].

Furthermore, C. limon pericarp extracts, too, exhibit scientifically proven activity that helps to accelerate the regeneration of diabetic wounds. In addition, the essential oil derived from C. limon pericarp has shown anti-inflammatory, anti-allergic and slimming properties [ 49 , 59 , 60 , 62 ].

According to the CosIng Database (Cosmetic Ingredient Database), C. limon can be used in twenty-three forms. It can be used in cosmetics in the form of oils obtained from various organs, in the form of extracts, hydrolates, powdered parts of the plant, wax and juice [ 27 ]. The most common activity defined by CosIng for the raw material of this species is to keep the skin in good condition, to improve the odour of cosmetic products, and to mask the smell of other ingredients of cosmetic preparations [ 27 ]. The approved forms of raw materials and their potential effects, as well as their use as corrigents, presented in the CosIng Database, are summarized in Table 10 [ 27 ].

C. limon in cosmetic products according to CosIng.

C. limon essential oil has been used since the 18th century in the production of the famous ‘Eau de Cologne’. In aromatherapy, it is used to treat numerous diseases and lifestyle-related ailments: hypertension, neurosis, anxiety, varicose veins, arthritis, rheumatism and mental heaviness. It also alleviates symptoms characteristic of menopause. C. limon essential oil is also used in aromatherapy massages to relax muscles, and for calming down and deep relaxation [ 21 ].

C. limon fruit extracts and essential oil should not be used in high concentrations in baths or directly on the skin. Recent studies have shown that D-limonene contained in the oil has an allergenic and irritating effect on the skin. It may cause cross-allergy with balsam of Peru. After applying cosmetics containing C. limon oil, it is forbidden to expose the skin to sunlight . C. limon essential oil contains photosensitizing compounds belonging to the linear furanocoumarin group. The lemon pericarp contains: bergapten, phellopterin, 5- and 8-geranoxypsoralen, and the essential oil contains: bergapten, imperatorin, isopimpinellin, xanthotoxin, oxypeucedanin and psoralen [ 21 , 82 ].

The International Fragrance Association (IFRA) has issued restrictions on the use of C. limon essential oil. In preparations remaining on the skin, the concentration of that oil should not exceed 2%. In addition, C. limon essential oil should not be used in preparations remaining on skin exposed to UV rays. They should not contain more than 15 ppm of bergapten [ 83 ].

10. Plant Biotechnological Studies on C. limon

Plant biotechnology creates opportunities for the potential use of plant in vitro cultures in the pharmaceutical, cosmetics and food industries. In vitro cultures can be a good alternative to plants growing in vivo. Plant biotechnology enables control and optimization of the conditions for conducting in vitro cultures to increase the accumulation of active compounds. It facilitates, among other things, optimization of the culture medium, including the concentration of plant growth and development regulators, the use of elicitors (stressors), the selection of highly productive cell lines and genetic transformations. In vitro cultures can also be used in plant propagation (micro-propagation process) [ 84 ].

C. limon cultures in vitro have thus far been the subject of research concerned with the development of micropropagation protocols. They have focused on the selection of plant growth regulators (PGRs) that induced shoot and root production in in vitro cultures. In 2012, biotechnological research on the micropropagation of C. limon was performed by Goswami et al. [ 85 ] from SKN Rajasthan Agricultural University in India. Shoot cultures were propagated from plant nodes on a Murashige and Skoog (MS) medium [ 86 ] containing different types and concentrations of PGRs. The maximum number of shoots and shoot regenerations was observed at a low level of 6-benzyladenine (BA) −0.1 mg/L, or kinetin −0.5 mg/L. Shoot proliferiation was also observed in combinations of PGRs such as BA and 1-naphthaleneacetic acid in concentrations of 0.1 mg/L each. With an increase in BA concentration in MS medium, shoot proliferation decreased. Regenerated shoots showed root induction on MS basal medium or on MS medium containing 1.0 mg/L of indole-3-butyric acid.

Another biotechnological study on C. limon was carried out in the Department of Citriculture in Murcia (Spain) [ 87 ]. The researchers studied organogenesis and made histological characterization of mature nodal explants of two important cultivars of C. limon —‘Verna 51’ and ‘Fino 49’. The highest number of buds per regenerating explant was obtained on the MS medium in comparison with the Woody plant medium [ 88 ]. The presence of 1–3 mg/L BA, in combination with 1 mg/L of 1-gibberellic acid (GA) in the culture medium, was essential for the development of adventitious buds. The lowest extent of organogenesis was observed when BA was used in the medium without GA [ 87 ].

11. Conclusions

The presented review proves that C. limon is a very attractive object of different scientific studies. The C. limon fruit is a raw material that can be used in different forms, e.g., extracts, juice and essential oil. The rich chemical composition of this species determines a wide range of its biological activity and its being recommended for use in phytopharmacology. The studies have focused on the essential oil and its main active compound—D-limonene. Extracts from C. limon fruits are rich in flavonoids such as naringenin and hesperetin.

Current pharmacological studies have confirmed the health-promoting activities of C. limon , especially its anti-cancer and antioxidant properties. C. limon also finds increasing application in cosmetology and food production.

There has been some biotechnological research aimed at developing effective in vitro micropropagation protocols for C. limon .

Author Contributions

Conceptualization, A.S., H.E. and M.K.-S.; data curation, A.S. and M.K.-S.; writing—original draft preparation, A.S. and M.K.-S.; writing—review and editing, A.S., M.K.-S. and H.E.; supervision, A.S. and H.E. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Conflicts of Interest

The authors declare no conflicts of interest.

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• Yuxuan Li   ORCID: orcid.org/0009-0000-4409-4510 1 ,
• Luiza C. Campos   ORCID: orcid.org/0000-0002-2714-7358 1 &
• Yukun Hu   ORCID: orcid.org/0000-0002-7480-4250 1  

This manuscript presents a scientometric review of recent advances in microwave pretreatment processes for sewage sludge, systematically identifying existing gaps and prospects. For this purpose, 1763 papers on the application of microwave technology to sludge pretreatment were retrieved from the Web of Science (WoS) using relevant keywords. These publications were then analyzed using diverse scientometric indices. The results show that research in this field encompasses applications based on the non-thermal effects of microwaves, enhanced effectiveness of anaerobic digestion (AD), and the energy balance of this pretreatment system. Overcoming existing technical challenges, such as the cleavage of extracellular polymers, reducing microwave energy consumption, understanding the non-thermal effects of microwaves, promoting AD of sludge in combination with other chemical and physical methods, and expanding the application of the technology, are the main scientific focuses. Additionally, this paper thoroughly examines both the constraints and potential of microwave pretreatment technology for wastewater treatment.

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Introduction

In treating domestic wastewater using the activated sludge method, approximately 7–10 kg of bioactive sludge, mixing of secondary sludge and waste activated sludge (WAS), is generated as a by-product for every 3 m 3 of treated wastewater (Bozkurt and Apul 2020 ). Due to rapid urbanization and population growth, residual sludge production is increasing in many countries. For instance, the annual production of sewage sludge in 15 EU countries increased by almost 50%, from 6.5 million tonnes of dry solids in 1992 to 9.8 million tonnes in 2005 (Kelessidis and Stasinakis 2012 ). In the US, around 6 million tonnes of dried residual sludge have been generated each year since 2015, with expectations of continued growth (Zhen et al. 2017 ). China produced 6.25 million tonnes of dry solids in 2013, with projections to reach 39.78 million tonnes by 2020 (Lishan et al. 2018 ). Anaerobic digestion is a prevalent method for stabilizing sludge, where organic components are transformed into methane through biological processes in an oxygen-deprived environment. However, even with prolonged retention periods (specifically, between 10 and 40 days), a significant portion of organic material (Tyagi and Lo 2011 ), around 35–45%, exits anaerobic digesters without undergoing digestion (Yuan and Zhu 2016 ). This issue primarily stems from the properties of WAS, which is a blend containing microbial cells, synthetic and natural organic materials, minerals, and heavy metals, all bound within a polymeric structure of extracellular polymeric substances (EPS) and cations (Gil et al. 2018 ). The EPS envelops the flocs, while robust cell walls protect the intracellular organic content during AD processing, leading to a deceleration in the hydrolysis stage and resulting in inadequate substrate utilization (Gil et al. 2019 ).

Before subjecting WAS to anaerobic digestion, hydrolysis can be expedited through various pretreatment approaches such as mechanical (Serrano et al. 2016 ), chemical (Bougrier et al. 2006 ; Chu et al. 2009 ), biological (Barjenbruch and Kopplow 2003 ; Carvajal et al. 2013 ), thermal (Eskicioglu et al. 2006 ; Kuglarz et al. 2013 ), or a combination of these methods (Toreci et al. 2009 ; Yu et al. 2010 ). Among these preparatory techniques, microwave irradiation stands out as an energy-efficient and targeted heating method that can swiftly induce hydrolysis, making it a feasible choice (Tyagi and Lo 2013 ). Microwave pretreatment, a derivative of traditional thermal pretreatment (Climent et al. 2007 ) is an emerging technology that has been developed as researchers have studied the biological effects of microwave radiation in depth (Banik et al. 2003 ). Despite the comprehensive understanding of microwave sludge pretreatment technology in recent years, there are still a number of issues that need to be addressed in its current development.

Numerous studies in the existing literature have documented the positive effects of using microwave pretreatment technology on sewage sludge. Recently, bibliometric analyses (Davarazar et al. 2020 ) have gained prominence as a method for monitoring and assessing research progress, as well as the contributions of researchers, countries, academic institutions, and global universities in specific domains (Gandia et al. 2019 ; Hoang et al. 2021 ). The significance of these crucial and decisive investigations is underscored by the notable increase in the quantity of scientometric research conducted across various scientific disciplines (Olawumi and Chan 2018 ; Saranya et al. 2018 ). This paper presents a comprehensive assessment of microwave pretreatment technology for sewage sludge. It conducts a critical review of scientometrics and summarizes all published articles from 1980 to 2023 on the microwave pretreatment of sewage sludge. Additionally, it offers an overview of the technology’s history and current developments in terms of time, region, and research area and highlights both its shortcomings and development prospects in sewage sludge treatment.

Methodology

This section details the scientometric analysis methods and specific literature screening tools utilized in this thesis, encompassing both scientometric and content analysis. The first part delineates the process of using keywords to search the literature in the Web of Science (WoS) core collection database, including how to filter out less relevant literature records by combining different keywords. The second section elucidates the logic behind the search strategy employed in this paper, critically discussing all the literature obtained in the field using three distinct bibliometric tools: CiteSpace, Scientopy, and WoS analysis.

Scientometric analysis

Scientometrics, a branch of informatics, involves the quantitative analysis of scientific literature to discern patterns, emerging trends, and the overall knowledge framework within research domains (Azam et al. 2021 ). Scientific mapping tools typically process scientific publications as input, producing interactive visual representations of complex structures for statistical examination and visual exploration (Yigitcanlar et al. 2020 ). A variety of scientific mapping tools are available for quantitative analysis, including HistCite (Garfield 2009 ), VOSviewer (Sgambati and Gargiulo 2022 ), Scientopy (Ruiz-Rosero et al. 2019 ), and CiteSpace (Chen et al. 2012 ). These tools share a common feature: they all generate scientific maps that depict relationships between various elements, offering a spatial representation of how disciplines, fields, participants (authors, institutions, and countries), and individual papers interconnect (Wagner et al. 2011 ). In this paper, two such scientific mapping software, Scientopy and CiteSpace, are employed to analyze the application of microwave pretreatment to sewage sludge, ensuring a clear and accurate presentation of the results.

The literature on microwave pretreatment of sewage sludge was sourced from the Web of Science (WoS) core collection database using carefully chosen keywords based on a primary literature survey (Table  1 ). The advanced search function in WoS centered on keywords related to wastewater treatment (Table  1 ), and the results from each keyword set were combined using the AND operator to pinpoint relevant literature within the field (Table  1 ). On May 23, 2023, a compilation of English language papers published between 1981 and 2023 was generated based on this search query. The focus was primarily on identifying papers that included the specified keywords in their titles. A meticulous selection process was employed to ensure precision in the analysis, discarding any irrelevant literature. The relevant records were then tagged within the WoS platform and exported in both ‘tab’ and ‘plain text’ formats for further examination using Scientopy and CiteSpace, respectively. This review included an evaluation of the following metrics: publication year, publication type, contributing country, keywords, authors, cited authors, cited journals, and subject area.

The table includes an asterisk to indicate the inclusion of additional potential keywords, aiming to expand the search scope and reduce the likelihood of missing any pertinent keywords denoted by various letters.

Content analysis

A comprehensive exploration of the published papers necessitates an in-depth scientometric examination. This endeavour aims to unveil past and present research trends concerning microwave pretreatment in sewage sludge treatment. The scientometric tools ‘Web of Science,’ ‘Scientopy,’ and ‘CiteSpace’ serve as foundational resources to facilitate insightful discourse on prevailing and forthcoming research focal points, as well as existing gaps that await resolution through future investigations. The research design of this paper is visually depicted in Fig.  1 . Utilizing three distinct bibliometric analyses, this study will scrutinize the entire assemblage of literature records in this domain, dissecting aspects such as publication year, authors, countries, research domains, and keywords. Based on these findings, the paper will identify prevailing deficiencies and chart potential pathways for the evolution of microwave pretreatment technology in sewage sludge treatment.

A schematic of the research design

Results and discussion

The results obtained are discussed and analyzed in this section in three parts. The analysis of the documents includes a categorization of the obtained documentary records in terms of chronological order and document type. Changes in research focus and format over time are summarized, and a conclusion of the highly cited literature in the field is presented to reveal hotspots in microwave pretreatment of sewage sludge. Regarding contributions, the paper discusses the input made to the field by authors, national and research institutions, and journals, respectively. The results of various levels of contribution to the research in this field are visualized using various scientometric tools. Finally, by analyzing trends in different research areas and keywords over time, two major challenges are identified: the mechanism of microwave non-thermal effects and the high energy consumption.

Analysis of documents

Figure  2 a illustrates the annual number of publications on the application of microwave pretreatment of sewage sludge. The publications in this field arranged chronologically can be divided into three stages. The first stage marks the inception of microwave pretreatment technology (Stage 1), with the earliest publication dating back to 1981 by Atsuya and Akatsuka ( 1981 ). In this article, microwave technology was employed to enhance the accuracy of trace element determination in sludge, using microwave pretreatment to expedite the digestion time of sewage sludge and achieving 92–101% recovery for arsenic determination. Colombini et al. ( 1998 ) conducted a study where sludge samples underwent digestion in a microwave oven using an alkaline persulfate solution, followed by analysis for total phosphorus and nitrogen via ion chromatography without preliminary sample treatment. The results demonstrated notable reproducibility and accuracy within the standard concentration range used for wastewater assessment. While not specifically using microwave technology as a pretreatment to enhance anaerobic digestion results, the application of microwave treatment to improve sludge solubility and reduce digestion time has been widely utilized (Pérez-Cid et al. 1999 ; Santos et al. 2000 ).

a The count of papers pertaining to the application of microwave pretreatment technology for wastewater sludge treatment per annum within the span of 1980 to 2023. b The accumulated tally of publications encompassing the interval from 1980 to 2023

Since 2000 (Stage 2), researchers have increasingly focused on microwave technology as a pretreatment prior to anaerobic digestion (AD), comparing it with other sludge pretreatment technologies. Hong et al. ( 2004 ) investigated the effect of microwave radiation versus external heating on pathogen destruction in sewage sludge. They found that cell membranes are damaged with increasing intensity and temperature of microwave radiation, and at the same temperature, microwave radiation more effectively reduces cell activity than external heating, almost ceasing bacterial activity at temperatures above 68 °C. A similar study by Pino-Jelcic et al. ( 2006 ) concluded that microwave/digested sludge showed fewer faecal coliforms and Salmonella spp. compared to conventional addition, noting that microwave heating enhanced biodegradability under thermophilic AD and improved the dewatering rate of digested sludge. Eskicioglu et al. ( 2007b ) investigated various sludge concentrations under microwave irradiation at low temperatures (50–96 °C), focusing on the dissolution of activated sludge and cumulative biogas generation through anaerobic digestion. Their findings indicated a significant increase in soluble total chemical oxygen demand (SCOD/TCOD) for high and low sludge concentrations, alongside improved dewatering efficiency of microwave-treated sludge after anaerobic digestion. Zheng et al. ( 2009 ) demonstrated that microwave pretreatment at 90 °C for primary sludge with a total solids concentration of 4% led to a 37% increase in the biogas production rate compared to non-pretreated sludge, with their model indicating an increase in biogas yield factor as microwave pretreatment temperature increased.

After 2010 (Stage 3), the publication of papers on microwave pretreatment, particularly for AD and sludge dewatering, increased rapidly. Researchers focused on enhancing microwave energy efficiency and the factors influencing microwave pretreatment. Jackowiak et al. ( 2011 ) aimed to optimize microwave pretreatment of wheat straw, with findings indicating a 28% increase in methane yield at 150 °C compared to untreated samples. They also highlighted the need for a positive energy balance, suggesting that microwave equipment power consumption should not exceed 2.65 kJ/g tVS. Uma Rani et al. ( 2013 ) found that microwave irradiation reduced the initial lag time of AD, with the best energy-efficient pretreatment lasting 12 min at 70% intensity. Studies also explored microwave pretreatment’s role in different substrates. For instance, Passos et al. ( 2014 ) reported a 30% increase in methane production from microalgal biomass, and Tyagi et al. ( 2014 ) documented a significant enhancement in sludge from pulp and paper mills following alkali-enhanced microwave pretreatment. Srinivasan et al. ( 2016 ) demonstrated high treatment efficiency and low energy requirements in a dairy manure treatment system using microwave and hydrogen peroxide pretreatment. In terms of main influencing factors, Tas et al. ( 2018 ) noted microwave pretreatment’s advantages over ultrasonic in methane yield improvement, highlighting economic considerations and the need for large-scale experiment data. Alizadeh et al. ( 2018 ) used a mathematical model to fit methane yield and solubilization efficiency, showing that microwave pretreatment enhances AD of kitchen waste by destroying its recalcitrant structure, thereby increasing biogas production, with irradiation time and temperature as key factors.

It is noteworthy that the accumulation of publications within this scientific domain follows a Sigmoidal growth trajectory, as clearly depicted in Fig.  2 b, with a high coefficient of determination ( R 2 = 0.9987). This pattern suggests that the field has yet to reach a definitive stage of maturity, indicating that ongoing research is still actively seeking to fill the existing gaps in knowledge regarding microwave pretreatment for wastewater treatment.

Figure  3 provides a detailed overview of the categories of publications related to the use of microwaves in water and wastewater treatment. It is apparent that research articles are the most common type, accounting for 83.7% of publications, followed by reviews (11.2%), conference abstracts (2.9%), book chapters (1.2%), and other relevant documents such as patents and technical notes. Table 2 summarizes the key conclusions and findings derived from the analyzed papers.

Types of published papers on the use of microwave pretreatment technology for sewage sludge treatment

Table 2 summarizes key findings from numerous studies on microwave pretreatment in sewage sludge treatment, encompassing aspects such as effects on sludge decomposition, pathogen destruction, methane production, dewatering performance, and nutrient recovery. The table reveals that microwave pretreatment can enhance methane recovery, improve dewatering, and inactivate pathogens, although the non-thermal effect of microwave and the mechanism remain a subject of controversy. Comparative analysis of microwave pretreatment’s efficiency with methods like ultrasonic pretreatment underscores significant energy and cost considerations.

Given these findings, it is apparent that microwave pretreatment offers considerable potential for sewage sludge treatment, outperforming traditional methods in several respects. Nevertheless, the associated high energy requirements and operational costs pose substantial challenges. The ongoing debate concerning the non-thermal effects and mechanisms of microwaves highlights an urgent need for further research, especially in the context of scaling these effects for industrial-scale applications. Future studies should aim to integrate microwave pretreatment with other emerging technologies to boost its efficiency and feasibility. Additionally, a thorough exploration of the economic and environmental implications of adopting this technology on a larger scale is essential for its practical application and to ensure alignment with sustainability objectives.

Scientometric analysis of authors, countries, and journal contributions

In this section, a comprehensive analysis of various publications related to the use of microwave pretreatment in sewage sludge treatment is presented. This analysis includes an examination of the authors, countries of origin, affiliated organisations, and sources of the publications. Analyzing these elements can reveal insights into evolving trends and the formation of international partnerships in the development of microwave pretreatment research technology globally (Guiling et al. 2022 ). Furthermore, it provides data to support further quantitative analysis (Rosokhata et al. 2021 ) for a general overview of the field’s development.

Contribution of authors

Figure  4 illustrates the frequency of publications on microwave pretreatment of sewage sludge. The CiteSpace and Scientopy results (shown in Fig.  4 a, b, respectively) identify major contributors in the field such as Lo, K.V. (Lo et al. 2015 ) and Liao, P.H. (Lo et al. 2018 ) from Canada, with 42 papers; Banu, JR (Ebenezer et al. 2015 ) from India, with 41 papers; Liu, Y. (Yang et al. 2013 ) with 33 papers; and Lo, S.L (Tyagi and Lo 2013 ) from China, with 31 papers. Figure  4 a (left) highlights the papers published in the last five years, indicating these significant contributors have remained active (Ruiz-Rosero et al. 2019 ). Additionally, there are several authors who have published multiple articles recently. For instance, Dai et al. (Zhang et al. 2017 ) studied microwave pyrolysis of textile printing and dyeing sludge, Fu et al. (Ao et al. 2018 ) reviewed the efficiency of activated carbon preparation under microwave radiation, Ma et al. (Ma et al. 2017 ) explored the impact of catalysts at varying temperatures on the conversion of organic matter and biofuel production by microwave pyrolysis of sludge, and Wei et al. (Niu et al. 2019 ) investigated microwave pretreatment combined with zero-valent iron technology to enhance AD. This demonstrates the popularity and growth of microwave pretreatment of sludge as a research area.

a The roles and contributions of authors in scientific publications concerning microwave pretreatment of wastewater sludge, as gathered through Scientopy. b CiteSpace outcomes reveal collaborative efforts among diverse authors in this field. The font size corresponds to the extent of an author’s contribution, with larger fonts indicating greater involvement

Apart from the volume of publications, the number of citations an author receives is another metric reflecting their influence on advancing scientific knowledge within the field (Zhang et al. 2022 ). As depicted in Fig.  5 , Cigdem Eskicioglu and collaborators (Kor-Bicakci et al. 2020 ) are the most cited researchers in this field. While extensive research on microwave pretreatment has been conducted, its application remains predominantly at the laboratory scale. Greater adoption will necessitate further pilot investigations to address existing challenges. For example, Appels et al. ( 2013 ) examined the effects of microwave pretreatment using a pilot-scale semi-continuous digester set-up, finding a 50% higher average biogas yield compared to a blank test, though they also highlighted considerations of energy efficiency. Thompson et al. ( 2019 ) demonstrated that microwave pretreatment improved the efficiency of saccharification and fermentation using brown algae but noted the need for substantial capital investment and energy input, with plans for future pilot-scale studies. Atelge et al. ( 2020 ) also emphasized that while microwave radiation is still in the developmental stage and applied at batch or pilot scale, transitioning to full scale requires transforming the technology from research to a mature technology.

Author’s contributions to the number of citations for publications on microwave pretreatment of sewage sludge

The comprehensive analysis of contributions in the field of microwave pretreatment of sewage sludge underscores a significant trend towards innovative and efficient waste management techniques. While the prolific output of key researchers such as Lo K.V., Banu JR, and Liu Y. demonstrates a robust academic interest, the prevailing focus on laboratory-scale studies signals a gap in the translation of this research into practical, large-scale applications. The high citation rates of works by Cigdem Eskicioglu and others reflect the academic community’s recognition of their valuable insights, yet the field appears to be at a critical juncture. The transition from laboratory to pilot-scale studies, as explored by Appels et al. and Thompson et al., is a crucial step that demands not only scientific rigor but also considerations of economic feasibility and energy efficiency. The future of microwave pretreatment in sewage sludge management hinges on bridging these gaps, transforming research into mature technology capable of addressing real-world environmental challenges. This necessitates a multidisciplinary approach, combining scientific innovation with practical engineering solutions, policy support, and sustainable economic models to realize the full potential of this technology in contributing to environmental sustainability.

Contribution of countries and research organizations

Figure  6 illustrates the delineation of contributions from various countries and organizations to the domain of microwave pretreatment in sewage sludge treatment. According to Fig.  6 a, China (591 records), Spain (175 records), and Canada (148 records) have published the highest number of papers in this field. In addition, the scientific output of leading national organizations, as shown in Fig.  6 b, has increased significantly since 2018, and new national organisations have started to gradually join the research in this field in recent years. For example, the Harbin Institute of Technology (China) and the Chinese Academy of Sciences (China) have published 55% and 49% of the scientific literature related to microwave pretreatment of sludge in the last five years, respectively. Anna University (India) has also made a significant contribution in the field of microwave pretreatment of sewage sludge in the last 5 years, with 55% of the published literature. Furthermore, according to the information provided in Fig.  6 , Chinese and Indian research and development organizations are currently the main players in this research area. Also, through publications in recent years, it can be understood that the main research topics of these organizations are currently focused on the use of different catalysts to synergistically increase the heating rate of microwaves (Li et al. 2022b ; Xie et al. 2022 ; Lu et al. 2023 ), the use of microwave-assisted technology to enhance the energy conversion rate of bio-waste (Yang et al. 2022 ; Chandrasekaran and Chithra 2022 ; Usmani et al. 2023 ), and microwave pretreatment technology to enhance hydrogen production from sludge (Dinesh Kumar et al. 2020 ; Zhao et al. 2023 ). This bodes well for the future development of sludge pretreatment in favor of industrialization and energy efficiency.

a The involvement of countries and research organizations in the implementation of microwave pretreatment for sewage sludge is depicted. b Examination utilizing Scientopy provides further insights. Contribution of journals

In terms of publication sources, an analysis via WoS indicates that ‘Bioresource Technology,’ ‘Water Research,’ and ‘Journal of Hazardous Materials’ are the leading journals in this area, with 248, 241, and 242 papers, respectively. Figure  7 visually represents the contributions of scientific journals to the research landscape in microwave pretreatment technology. However, it is important to note that the correlation between the number of published papers and citations received is not always direct. In some cases, journals may publish fewer papers but receive a considerable number of citations. For example, ‘Environmental Science and Technology’ has published 131 papers on microwave pretreatment, yet it holds the highest average citations per article (101.79) among all journals in this field.

Contribution of various journals to the number of citations of publications on the use of microwave pretreatment of sewage sludge

The international landscape of microwave pretreatment research, dominated by Chinese and Indian organizations, underscores the global recognition of the importance of sustainable waste management. The significant involvement of countries and organizations in advancing this technology highlights a collaborative effort towards environmental sustainability. The focus on innovative research topics like catalyst use for increased heating rates, energy conversion enhancement, and hydrogen production from sludge reflects a progressive approach towards addressing energy and environmental challenges. This trend towards specialisation in microwave pretreatment indicates a promising future for this technology, potentially leading to industrial-scale applications. However, the successful transition from laboratory research to industrial application will require a concerted effort in overcoming technical, economic, and regulatory challenges. This necessitates not only continuous scientific innovation but also the need for international collaboration, policy development, and investment in pilot projects to facilitate the practical implementation of these research findings. The integration of these diverse elements will be crucial in realizing the full potential of microwave pretreatment technology in contributing to a sustainable future.

Trends in microwave pretreatment technology

Keyword and subject area analysis reveals that microwave pretreatment is a major trend in sewage sludge applications (Macías-Quiroga et al. 2021 ). Figure  8 illustrates the diverse nature of research in this domain, covering a range of disciplines, including engineering, environmental science, energy, chemistry, biotechnology, agriculture, water resources, and, to a lesser extent, materials science and biochemistry.

Subject area for wastewater treatment using microwave pretreatment

Figure  9 a identifies key keywords in papers on microwave pretreatment of sewage sludge, highlighting hot topics such as enhancing anaerobic digestion (AD), microwave pyrolysis mechanisms, combined catalyst use, and biogas production. These topics are consistent with previous findings on national and research institute contributions, indicating a coherent research trend (refer to “Contribution of countries and research organizations”). The keyword search results using CiteSpace (Fig.  9 b) offer an intuitive insight into current hotspots in microwave pretreatment research (López-Serrano et al. 2020 ). In addition to the previously mentioned topics, new keywords like optimization, degradation, and behavior have emerged. These are related to research on improving biogas purity through microwave pretreatment (Wang et al. 2022 ; Luo et al. 2023 ), enhancing sludge hydrolysis post-microwaving (Cheng et al. 2023 ), and the destruction of cellulose in sludge (Kazawadi et al. 2022 ).

The most important keywords found in published documents on microwave pretreatment of sewage. a Analysis results from Scientopy. b Analysis results from Citespace

The timeline visualization is designed to illustrate the evolution of key trends in the research domain. It includes aspects such as the emergence of keywords within clusters, the timing of their introduction, the changing significance of clusters over time, and the identification of symbolic keywords, particularly those with high and medium centrality (Zhang et al. 2023 ). This visualization aids in comprehending the developmental trajectory and current focus areas within the field.

In the context of keyword analysis focused on microwave-pre-treated sludge, Fig.  10 illustrates the development of the initial 10 clusters. Among these, clusters such as (#0) microwave pyrolysis, (#1) anaerobic digestion, and (#2) microwave digestion have consistently garnered significant attention since their introduction in this field. Emerging themes like microwave co-pretreatment, sludge drying, and the immobilization of heavy metals in sludge have evolved from earlier clusters. Notably, from 1981 until approximately 2014, sewage sludge was the primary focus of microwave pretreatment research, with a gradual shift towards exploring reaction mechanisms and optimising effects in more recent years. Table 3 summarizes the latest advancements in microwave pretreatment for sludge.

Trends in the occurrence of keywords obtained using CiteSpace for microwave pretreatment of sewage sludge

The keyword and subject area analysis of microwave pretreatment research reveals a multidisciplinary convergence, highlighting the field’s complexity and its far-reaching implications. The evolution from a focus on sewage sludge to exploring various reaction mechanisms and optimization techniques indicates a maturing research area. As new keywords like ‘optimisation,’ ‘degradation,’ and ‘behavior’ emerge, they reflect the shifting focus towards enhancing operational efficiencies and understanding deeper scientific processes. However, as the field advances, it faces the challenge of integrating these diverse scientific insights into practical, scalable solutions. This integration requires a balance between innovative research and the pragmatic challenges of implementation, including economic viability and environmental impact. The future direction of this research, therefore, hinges on a synergistic approach that combines scientific discovery with real-world applicability, ensuring that the benefits of microwave pretreatment extend beyond theoretical research to tangible environmental and societal impacts.

Microwave non-thermal effects

Microwaves can interact with flocculants in sludge, releasing bound organic matter into solution and breaking down extracellular polymers (Kuglarz et al. 2013 ). They shield the cell wall within the microflocculant assembly and release intracellular organic matter through three pathways: thermal (Tang et al. 2010 ), non-thermal (ESKICIOGLU et al. 2007b ), and catalytic oxidation (Quan et al. 2007 ). Thermal effects include solubilization of organic matter, such as denaturing membrane proteins and releasing intracellular organelles, and exceeding the boiling point of intracellular fluids, potentially leading to cell wall rupture (Atkinson et al. 2019 ). High temperatures can reduce the solubility of gases, forming gas domains (bubbles) in the slurry that may exert additional pressure on cell walls upon bursting. Conversely, high temperatures (70–180 °C) can lead to the polymerisation of low molecular weight sugars and amino acids through the Maillard reaction, resulting in the formation of recalcitrant polymeric organic compounds, potentially reducing the anaerobic digestibility of the resultant product (Eskicioglu et al. 2007a ; Toreci et al. 2011 ).

Non-thermal effects, such as specific effects of electromagnetic radiation, are caused by the rapid oscillation of polar and polarizable molecules or polarized side chains of macromolecules attempting to align with incident electromagnetic waves (Yeneneh et al. 2015 ). Microwave energy converts to heat through internal rotational resistance, potentially leading to bond breakdown and reorientation. Despite postulations in the literature about non-thermal microwave decomposition pathways, conclusive evidence remains scarce. Experimentally isolating non-thermal effects is challenging, as conventional heating principles differ from microwave radiation, and internal molecular-level temperature monitoring is not straightforward (Kostas et al. 2017 ). Rao et al. ( 2022 ) explored microwave non-thermal effects by examining sludge cake pore structures and analyzing fractal dimensions, suggesting improvements in sludge dewatering were due to non-thermal effects on drainage pore structure and moisture distribution. Conversely, Park and Ahn ( 2011 ) and Dai et al. ( 2013 ) found no substantial non-thermal effects when comparing microwave pyrolysis with conventional heating in terms of biogas yield, SCOD/TCOD ratios, and emissions of PCDD/Fs. Sólyom et al. ( 2011 ) also observed similar biogas production results with both heating methods, indicating a lack of non-thermal microwave effects. Therefore, using microwave pretreatment alongside conventional heating methods to validate the existence of non-thermal effects might not be reliable. More conclusive evidence could potentially be obtained through microstructural observations of sludge or a combination of simulation and experimentation.

• Energy consumption

Microwave pretreatment of sewage sludge, especially at the laboratory scale, demonstrates significant potential for enhancing biogas production when integrated with anaerobic digesters. This enhancement is attributed to both the thermal (Ahn et al. 2009 ) and non-thermal (Tyagi and Lo 2013 ) effects of microwave processing that disrupt the complex floc structure of the sludge. This disruption unfolds and denatures complex organic molecules, including intracellular and extracellular components, making them smaller and more biodegradable (Li et al. 2022a ). Such a process results in a notable increase in SCOD, a critical factor in boosting biogas production during subsequent anaerobic digestion. Research (Yu et al. 2010 ) has shown that microwave irradiation (total irradiation energy of 630 kJ) can raise the SCOD/TCOD ratio in sludge from 2 to 22%. Similarly, in WAS, this ratio increased from 8 to 18% after microwave heating at 72.5 °C and from 6 to 18% following treatment at 96 °C. Gil et al. ( 2019 ) reported an increase in solubility (COD/TVS ratio) of floating sewage sludge ranging from 43 to 66%, depending on the total energy applied and the power rating.

In addition to the increase in SCOD content, microwave irradiation significantly enhances biogas production from sludge (Yu et al. 2010 ). Subjecting WAS to microwave treatment at various temperatures results in a notable increase in both the rate and volume of biogas generated. The kinetic equation for biogas production from microwave-pretreated sludge is given in Eqs. 1 – 3 (Ebenezer et al. 2015 ; Ude and Oluka 2022 ), where S represents SCOD, B is the biogas yield, R is the reaction rate, k is the rate constant Q i is the input flow rate, Q 0 is the output flow rate, while S i and S 0 are the influent SCOD and effluent SCOD, respectively, and V d is the digester volume. The improvement in biogas production can be attributed to the increased SCOD, as indicated by these equations.

However, despite the benefits, energy consumption is crucial for scaling up microwave pretreatment technology for industrial application (Cano et al. 2015 ). Table 4 highlights the challenge of achieving a favourable energy balance, which is essential for the practical application of this technology on an industrial scale. Therefore, while microwave pretreatment offers significant potential in enhancing biogas production, optimizing the process to reduce energy consumption while maintaining high biogas yields is vital for its large-scale viability.

To enhance the energy viability of microwave pretreatment technology, significant research efforts have been made recently. Balasundaram et al. ( 2022 ) observed that low-temperature pretreatment (< 100 °C) reduces electrical energy consumption and achieves a positive energy balance, but the increased electrical demand from microwaves challenges the system’s energy self-sufficiency. Tang et al. ( 2010 ) highlighted that moisture content significantly affects microwave irradiation energy efficiency, and reducing sludge moisture content can decrease microwave energy input. Kavitha et al. ( 2018 ) implemented ultrasonic-assisted microwave pretreatment to increase methane yield, resulting in a net profit of US $2.67 per ton, although this study did not consider plant investment costs. Other studies, such as those by Kang et al. ( 2020 ), have utilized carbon nanotube-coated microwave vessels to enhance energy efficiency and sludge dissolution, showing promising results at the laboratory scale but lacking data from pilot and industrial-scale experiments. In conclusion, energy consumption remains a major barrier to the widespread adoption of microwave pretreatment, with the ongoing challenge being to reduce energy costs while increasing biogas production.

Limitations and prospects

The effectiveness of microwave pretreatment in anaerobic sludge digestion, sludge dewatering, and increased biogas production has been established in multiple studies. Whether applied alone or in combination with chemical, physical, and other auxiliary methods, microwave pretreatment has shown great promise as a method for sludge pretreatment. Numerous laboratory-scale experiments provide ample evidence to support the application of this technology in larger-scale experimental studies.

Despite the maturity of microwave pretreatment for sludge effluent technology, the mechanistic study of microwave radiation remains controversial. The existence of microwave non-thermal effects and the modeling of microwave mechanisms for industrial-scale applications continue to be important directions for future development. While there have been some studies on the modeling of microwave heating, these predominantly focus on food (Campañone et al. 2012 ; Yang and Chen 2021 ), materials (Lovás et al. 2010 ; Goyal and Vlachos 2020 ), and chemistry (Zhang et al. 2000 ; Zhu et al. 2007 ). However, there remains a gap in the modeling of microwave treatment of sewage sludge. The integration of microwave heating with sludge structural analysis could significantly bolster the expansion of microwave pretreatment in industrial applications.

Industrial applications are another key factor limiting the wider use of microwave pretreatment. Most research in this area has focused on laboratory-scale (Zaker et al. 2019 ; Bozkurt and Apul 2020 ; Vialkova et al. 2021 ), although there have been pilot-scale (Kocbek et al. 2020 ; Guo et al. 2021 ) experiments confirming the potential successful application of microwave pretreatment technology. However, progress from laboratory to pilot scale has been hindered by apparent inconsistencies in performance in the original experiments. These inconsistencies can be attributed to factors such as variability in sludge characteristics and differences in microwave treatment parameters. For instance, Mawioo et al. ( 2017 ) used four diverse types of sludge (partially dewatered/centrifuged WAS, fresh faecal sludge, septic tank sludge, and WAS) to examine the performance of a microwave reactor at pilot scale. The results showed that microwave-based technology is a promising option for the treatment of faecal sludge, septic sludge, and WAS; however, due to differing organic contents, sanitization and volume reduction performances showed significant differences. Passos et al. ( 2015 ) compared thermal (95 °C, 10 h) and microwave irradiation (900 W, 3 min, 34.3 MJ/kg TS) for improving microalgae anaerobic digestion at pilot scale, finding that the best results were obtained with thermal pretreatment (95 °C, 10 h) rather than microwave irradiation, likely due to the choice of microwave treatment parameters.

Additionally, complexities in scaling up, such as managing uniform heating and precise parameter control in larger setups, equipment limitations for processing large volumes, and the economic challenges associated with larger-scale operations, contribute to these inconsistencies. Aguilar-Reynosa et al. ( 2017 ) point out that for the development of microwave reactors, it is necessary to further explore the design of applicators (traveling wave, multimode, and monomodal cavities) that enable high-power densities and faster heating rates, thereby assessing microwave heating processing as an alternative pretreatment in second-generation biorefineries.

Building upon these challenges, microwave pretreatment faces its own set of hurdles that need addressing to enable industrialization. Key among these is a more thorough understanding of the interactions and relationships between microwave radiation, biomass, and the heating medium, particularly concerning the non-thermal effects of microwave irradiation on biomass; the development of more efficient microwave absorbents that can effectively transfer energy to the biomass and be easily separated afterwards; and the upscaling of microwave pretreatment reactors to accommodate large-scale treatment of various types of sewage sludge.

Conclusions

In conclusion, this scientometric analysis of 1763 bibliographic records provides a comprehensive overview of the development and current state of microwave pretreatment technology in sewage sludge treatment. The study traces the technology’s evolution, highlights significant advancements, identifies challenges, and suggests potential future directions. The global contributions and thematic focuses reflect the technology’s dynamic role in environmental management practices. The primary conclusions of this research are summarized as follows:

Originating in the 1980s for trace substance measurement in sludge, microwave pretreatment technology has evolved significantly, becoming a key strategy for optimizing anaerobic digestion processes.

Since 2010, there has been a notable shift in focus towards the mechanics of microwave pretreatment, aimed at enhancing energy efficiency and refining treatment techniques, indicating a progression to more advanced methodologies.

Contributions from key figures like Cigdem Eskicioglu and research teams in China, India, and the USA highlight global interest. Research themes have centred around “microwave pyrolysis,” “anaerobic digestion,” “biogas,” and “degradation.”

The study underscores a consistent emphasis on enhancing energy efficiency and exploring combinations with other treatment methods, signaling a mature phase in the development of microwave pretreatment methods.

The transition from laboratory to industrial scale has faced challenges due to inconsistencies in performance and the need for comprehensive mathematical models for accurately representing microwave radiation mechanisms.

A significant gap exists in the development of comprehensive models that capture the interactions between microwave radiation and sludge components, which is crucial for scalability and assessing environmental impact.

Industrial-scale application of this technology confronts challenges in developing scalable operational models, evaluating economic viability, and understanding the comprehensive environmental impact.

Fostering collaboration between academia and industry is essential for translating laboratory findings into practical, scalable solutions.

Addressing the identified research gaps and focusing on practical applications are crucial for the transition of this technology from laboratory research to impactful industrial solutions, ensuring its sustainability and wider adoption.

Data availability

This literature review is based on publicly available sources, including peer-reviewed journal articles, conference papers, books, and online resources. Where possible, references have been directly linked to their source URLs or DOIs to facilitate access.

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Yuxuan Li, Luiza C. Campos & Yukun Hu

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The development of this manuscript represents a collective effort, drawing upon the unique strengths and expertise of each author. The distribution of contributions is delineated as follows: Yuxuan Li: initiated and conceptualized the original paper, conducting primary research and drafting the initial manuscript; Luiza C. Campos: engaged in meticulous review and editing, focusing on precision and clarity, thereby enriching the manuscript’s quality and readability; and Yukun Hu: provided critical insights, participated in extensive review and editing, refining the intellectual content and enhancing the overall coherence. The harmonious collaboration among authors facilitated the synthesis of diverse perspectives, resulting in a refined and comprehensive research document.

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Correspondence to Yukun Hu .

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Li, Y., Campos, L.C. & Hu, Y. Microwave pretreatment of wastewater sludge technology—a scientometric-based review. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-32931-9

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Received : 24 August 2023

Accepted : 11 March 2024

Published : 27 March 2024

DOI : https://doi.org/10.1007/s11356-024-32931-9

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