Dipeptidyl peptidase IV (DPP-IV) inhibitors occupy a growing place in the drugs used for the management of type 2 diabetes. Recently, food components, including food-derived bioactive peptides, have been suggested as sources of DPP-IV inhibitors without side effects. Chinese black tea is a traditional health beverage, and it was used for finding DPP-IV inhibitory peptides in this study. The ultra-filtrated fractions isolated from the aqueous extracts of black tea revealed DPP-IV inhibitory activity in vitro. Four peptides under 1 kDa were identified by SDS-PAGE and LC-MS/MS (Liquid Chromatography-Mass Spectrometry-Mass Spectrometry) from the ultra-filtrate. The peptide II (sequence: AGFAGDDAPR), with a molecular mass of 976 Da, showed the greatest DPP-IV inhibitory activity (in vitro) among the four peptides. After administration of peptide II (400 mg/day) for 57 days to streptozotocin (STZ)-induced hyperglycemic mice, the concentration of glucagon-like peptide-1 (GLP-1) in the blood increased from 9.85 ± 1.96 pmol/L to 19.22 ± 6.79 pmol/L, and the insulin level was increased 4.3-fold compared to that in STZ control mice. Immunohistochemistry revealed the improved function of pancreatic beta-cells and suppressed proliferation of pancreatic alpha-cells. This study provides new insight into the use of black tea as a potential resource of food-derived DPP-IV inhibitory peptides for the management of type 2 diabetes.

Tea is an important beverage in humans’ daily lives. For a long time, tea grade identification relied on sensory evaluation, which requires professional knowledge, so is difficult and troublesome for laypersons. Tea chemical component detection usually involves a series of procedures and multiple steps to obtain the final results. As such, a simple, rapid, and reliable method to judge the quality of tea is needed. Here, we propose a quick method that combines ultraviolet (UV) spectra and color difference to classify tea. The operations are simple and do not involve complex pretreatment. Each method requires only a few seconds for sample detection. In this study, famous Chinese green tea, Huangshan Maofeng, was selected. The traditional detection results of tea chemical components could not be used to directly determine tea grade. Then, digital instrument methods, UV spectrometry and colorimetry, were applied. The principal component analysis (PCA) plots of the single and combined signals of these two instruments showed that samples could be arranged according to grade. The combined signal PCA plot performed better with the sample grade descending in clockwise order. For grade prediction, the random forest (RF) model produced a better effect than the support vector machine (SVM) and the SVM + RF model. In the RF model, the training and testing accuracies of the combined signal were all 1. The grades of all samples were correctly predicted. From the above, the UV spectrum combined with color difference can be used to quickly and accurately classify the grade of Huangshan Maofeng tea. This method considerably increases the convenience of tea grade identification.