Fruit juices are one of the most adulterated beverages, usually because of the addition of water, sugars, or less expensive fruit juices. This study presents a method based on Fourier transform infrared spectroscopy (FT-IR), in combination with machine learning methods, for the correct identification and quantification of adulterants in juices. Thus, three types of 100% squeezed juices (pineapple, orange, and apple) were evaluated and adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The results of the exploratory data analysis revealed a clear clustering trend of the samples according to the type of juice analyzed. The supervised learning analysis, based on the development of models for the detection of adulteration, obtained significant results for all tested methods (i.e., support-vector machines or SVM), random forest or RF, and linear discriminant analysis or LDA) with an accuracy above 97% on the test set. Regarding quantification, the best results are obtained with the support vector regression and with partial least square regression showing an R² greater than 0.99 and a root mean square error (RMSE) less than 1.4 for the test set.

Recent cultivar techniques on vineyards can have a marked influence on the final nitrogen content of grapes, specifically individual amino acid contents. Furthermore, individual amino acid contents in grapes are related to the final aromatic composition of wines. A new ultrasound-assisted method for the extraction of amino acids from grapes has been developed. Several extraction variables, including solvent (water/ethanol mixtures), solvent pH (2-7), temperature (10-70°C), ultrasonic power (20-70%) and ultrasonic frequency (0.2-1.0s(-)(1)), were optimized to guarantee full recovery of the amino acids from grapes. An experimental design was employed to optimize the extraction parameters. The surface response methodology was used to evaluate the effects of the extraction variables. The analytical properties of the new method were established, including limit of detection (average value 1.4mmolkg(-)(1)), limit of quantification (average value 2.6mmolkg(-)(1)), repeatability (average RSD=12.9%) and reproducibility (average RSD=15.7%). Finally, the new method was applied to three cultivars of white grape throughout the ripening period.

Honey is one of the most adulterated foods, usually through the addition of sweeteners or low-cost honeys. This study presents a method based on visible near infrared spectroscopy (Vis-NIRs), in combination with machine learning (ML) algorithms, for the correct identification and quantification of adulterants in honey. Honey samples from two botanical origins (orange blossom and sunflower) were evaluated and adulterated with low-cost honey in different percentages (5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, and 50%). The results of the exploratory analysis showed a tendency to group the samples according to botanical origin, as well as the presence of adulteration. A supervised analysis was performed to detect the presence of adulterations. The best performance with 100% accuracy was achieved by support vector machines (SVM) and random forests (RF). A regression study was also carried out to quantify the percentage of adulteration. The best result was obtained by support vector regression (SVR) with a coefficient of determination (R²) of 0.991 and a root mean squared error (RMSE) of 1.894. These results demonstrate the potential of combining ML with spectroscopic data as a method for the automated quality control of honey.

Fruit juice production is one of the most important sectors in the beverage industry, and its adulteration by adding cheaper juices is very common. This study presents a methodology based on the combination of machine learning models and near-infrared spectroscopy for the detection and quantification of juice-to-juice adulteration. We evaluated 100% squeezed apple, pineapple, and orange juices, which were adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The spectroscopic data have been combined with different machine learning tools to develop predictive models for the control of the juice quality. The use of non-supervised techniques, specifically model-based clustering, revealed a grouping trend of the samples depending on the type of juice. The use of supervised techniques such as random forest and linear discriminant analysis models has allowed for the detection of the adulterated samples with an accuracy of 98% in the test set. In addition, a Boruta algorithm was applied which selected 89 variables as significant for adulterant quantification, and support vector regression achieved a regression coefficient of 0.989 and a root mean squared error of 1.683 in the test set. These results show the suitability of the machine learning tools combined with spectroscopic data as a screening method for the quality control of fruit juices. In addition, a prototype application has been developed to share the models with other users and facilitate the detection and quantification of adulteration in juices.

A method for the extraction of phenolic compounds from grape seeds and skins using microwave-assisted extraction (MAE) was developed. Optimization of the effects of the extraction parameters in terms of the results of extraction was obtained using the response surface methodology. The parameters studied were extraction solvent (methanol, ethanol, acetone, and water), percentage of methanol in water, quantity of sample in relation to volume of extraction solvent (solid:liquid, 10–50 mg mL⁻¹), power (100–500 W), magnetic stirring speed (0–100%), and extraction time (5–20 min). Finally, the repeatability and the intermediate precision of the method were determined. The best conditions proved to be: 65% methanol in water as an optimum extraction solvent; 0.5 g of grape skin or seed in a volume of 25 mL; a power of 500 W with the maximum stirring speed (100%); and an extraction time of 5 min. The phenolic compounds proved to be stable in the optimized extraction conditions. The resulting repeatability and the intermediate precision of the optimized method showed a relative standard deviation below 7%. The new method applied on Napoleon grape allowed for the determination of catechin (453.2 (mg kg⁻¹)), epicatechin (306.3 mg kg⁻¹), caftaric acid (22.37 mg caffeic acid equivalents kg⁻¹), dihydrokaempferol-glycoside (11.13 mg kaempferol equivalents kg⁻¹), quercetin (18.28 mg kg⁻¹), quercetin-3-glucoside (20.09 mg quercetin equivalents kg⁻¹), and kaempferol-3-glucoside (11.10 mg kaempferol equivalents kg⁻¹).