Soybean (Glycine max) hull valorization through the extraction of polyphenols by green alternative methods.

Soybean is one of the greatest crops in the world, with about 348.7 million tons being produced in 2018. Soybean hull is a by-product produced during the processing of soybean to obtain flour and oil. Though not being actually exploited, it is a source of polyphenols with antioxidant activity. Here, the extraction of polyphenols from soybean hull was performed by means of an alkaline hydrolysis treatment, which was optimized by the response surface methodology. At the optimal region, a total phenolic content of 0.72 g gallic acid equivalents per 100 g of soybean hull was obtained with an antioxidant activity of 2.17 mmoles of Trolox equivalents. Polyphenols responsible for the antioxidant activities were identified by LC-MS, including phenolic acids, anthocyanins, stilbenes, and the two main isoflavones of soybean, daidzein and genistein, in their non-glycosylated form. Other alternative extraction methods based on Aspergillus oryzae fermentation and α-amylase hydrolysis are also proposed.

Recovery of phenolic antioxidants from Syrah grape pomace through the optimization of an enzymatic extraction process.

Phenolic compounds are highly valuable products that remain trapped in grape pomace, an abundant winery by-product. Therefore, efficient extraction procedures of these compounds represent a route for grape pomace valorisation. Here we performed a screening of the factors affecting the aqueous enzymatic extraction of phenolic compounds from Syrah grape pomace, including the following independent variables: temperature, pH, pectinase, cellulase and tannase; and a subsequent optimization through response surface methodology. At the optimal region, the enzymatic treatment enhanced the extraction yield of phenolics by up to 66% and its antioxidant capacity by up to 80%, reducing the incubation time and enzyme doses in respect to previous studies. We found that tannase raises the antioxidant capacity of the extract by the liberation of gallic acid, while cellulose favours the liberation of p-coumaric acid and malvidin-3-O-glucoside. We also tested the procedure in different grape pomace varieties, verifying its wide applicability.

Continuous method to determine the trypsin inhibitor activity in soybean flour.

The determination of trypsin inhibitor (TI) activity is of importance to evaluate the nutritional value of soybean flours. An analytical method, which involves a continuous spectrophotometric rate determination for trypsin activity against the substrate N-benzoyl-DL-arginine p-nitroanilide, is proposed as an alternative to the standard discontinuous assay. Stopping the reaction with acetic acid and a centrifugation/filtration step to decrease turbidity are not required, thus reducing costs and sample preparation time. The TI activity of different flour samples, determined by both assays, demonstrated to be statistically comparable, irrespective of the TI concentration level. The coefficients of variation of the novel method did not exceed 8% at any concentration level. The curves of progress reaction showed a non-linear behavior in samples without TI. A reduction of incubation time from 10min to 2min increased the method sensitivity and extended its linear range. A more economical, faster and simpler assay was developed.

Screening of oil samples on the basis of excitation-emission room-temperature phosphorescence data and multiway chemometric techniques. Introducing the second-order advantage in a classification study.

Room-temperature phosphorescence excitation-emission matrices and multiway methods have been analyzed as potential tools for screening oil samples, based on full matrix information for polyaromatic hydrocarbons. Crude oils obtained from different sources of similar geographic origin, as well as light and heavy lubricating oils, were analyzed. The room-temperature phosphorescence matrix signals were processed by applying multilayer perceptron artificial neural networks, parallel factor analysis coupled to linear discriminant analysis, discriminant unfolded partial least-squares, and discriminant multidimensional partial least-squares (DN-PLS). The ability of the latter algorithm to classify the investigated oils into four categories is demonstrated. In addition, the combination of DN-PLS with residual bilinearization allows for a proper classification of oils containing unsuspected compounds not present in the training sample set. This second-order advantage concept is applied to a classification study for the first time. The employed approach is fast, avoids the use of laborious chromatographic analysis, and is relevant for oil characterization, identification, and determination of accidental spill sources.