The formation of volatiles in fruit wine process and its impact on wine quality.

Fruit wine is one of the oldest fermented beverages made from non-grape fruits. Owing to the differences in fruit varieties, growing regions, climates, and harvesting seasons, the nutritional compositions of fruits (sugars, organic acids, etc.) are different. Therefore, the fermentation process and microorganisms involved are varied for a particular fruit selected for wine production, resulting in differences in volatile compound formation, which ultimately determine the quality of fruit wine. This article reviews the effects of various factors involved in fruit wine making, especially the particular modifications differing from the grape winemaking process and the selected strains suitable for the specific fruit wine fermentation, on the formation of volatile compounds, flavor and aroma profiles, and quality characteristics of the wine thus produced. KEY POINTS: • The volatile profile and fruit wine quality are affected by enological parameters. • The composition and content of nutrients in fruit must impact volatile profiles. • Yeast and LAB are the key determining factors of the volatile profiles of fruit wines.

Terahertz Spectroscopic Identification of Roast Degree and Variety of Coffee Beans.

In this study, terahertz time-domain spectroscopy (THz-TDS) was proposed to identify coffee of three different varieties and three different roasting degrees of one variety. Principal component analysis (PCA) was applied to extract features from frequency-domain spectral data, and the extracted features were used for classification prediction through linear discrimination (LD), support vector machine (SVM), naive Bayes (NB), and k-nearest neighbors (KNN). The classification effect and misclassification of the model were analyzed via confusion matrix. The coffee varieties, namely Catimor, Typica 1, and Typica 2, under the condition of shallow drying were used for comparative tests. The LD classification model combined with PCA had the best effect of dimension reduction classification, while the speed and accuracy reached 20 ms and 100%, respectively. The LD model was found with the highest speed (25 ms) and accuracy (100%) by comparing the classification results of Typica 1 for three different roasting degrees. The coffee bean quality detection method based on THz-TDS combined with a modeling analysis method had a higher accuracy, faster speed, and simpler operation, and it is expected to become an effective detection method in coffee identification.

The substitution sites of hydroxyl and galloyl groups determine the inhibitory activity of human pancreatic α-amylase in twelve tea polyphenol monomers.

Tea polyphenols have been reported as potential α-amylase inhibitors. However, the quantitative structure-activity relationship (QSAR) between tea polyphenols and human pancreas α-amylase (HPA) is not well understood. Herein, the inhibitory effect of twelve tea polyphenol monomers on HPA was investigated in terms of inhibitory activity, as well as QSAR analysis and interaction mechanism. The results revealed that the HPA inhibitory activity of theaflavins (TFs), especially theaflavin-3'-gallate (TF-3'-G, IC50: 0.313 mg/mL), was much stronger than that of catechins (IC50: 18.387-458.932 mg/mL). The QSAR analysis demonstrated that the determinant for the inhibitory activity of HPA was not the number of hydroxyl and galloyl groups in tea polyphenol monomers, while the substitution sites of these groups potentially might play a more important role in modulating the inhibitory activity. The inhibition kinetics and molecular docking revealed that TF-3'-G as a mixed-type inhibitor had the lowest inhibition constant and bound to the active sites of HPA with the lowest binding energy (-7.74 kcal/mol). These findings could provide valuable insights into the structures-activity relationships between tea polyphenols and the HPA inhibitors.