The consumption of mushrooms has considerably increased in recent years because of their beneficial nutritional properties due to their essential amino acids, proteins, and dietary fiber content. Recent research has shown that they are also rich in polysaccharides and phenolic compounds. These compounds exhibit decisive free radical and ROS scavenging power with potential application to the treatment of neurodegenerative disorders. In addition, they present important properties like antioxidant, antiaging, and immune modulation. In the present research, the optimization for the extraction of total phenolic compounds and the antioxidant activity (DPPH and ABTS), based on ultrasound–assisted techniques has been carried out. Five variables (% MeOH in solvent, extraction temperature, amplitude, cycle, and sample:solvent ratio have been selected; both the total phenolic compounds content as well as the antioxidant activity (DPPH and ABTS)) have been considered as the response variables. The optimal conditions, determined by means of a multiresponse optimization method, were established at 0.2 g of sample extracted with 15.3 mL of solvent (93.6% MeOH) at 60 °C for 5 min and using 16.86% amplitude and 0.71 s−1 cycles. A precision study of the optimized method has been performed with deviations lower than 5%, which proves the repeatability and precision of the extraction method. Finally, the extraction method has been applied to wild and commercial mushrooms from Andalusia and Northern Morocco, which has confirmed its suitability for the extraction of the phenolic compounds from mushroom samples, while ensuring maximum antioxidant activity.
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.
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