Comparison of Volatile and Nonvolatile Metabolites in Black Tea under Four Second-Drying Methods Using Widely Targeted Metabolomics.

Second-drying has an impact on the development of flavor and aroma in black tea. However, the effect of the shape changes of the tea leaves during second-drying on the quality of black tea has yet to be evaluated. In this study, GC-TOFMS and UPLC-HRMS identified 411 volatile metabolites and 253 nonvolatile metabolites. Additionally, 107 nonvolatile compounds and 21 different volatiles were screened. Significant alterations (p < 0.01) were found in 18 amino acid derivatives, 17 carbohydrates, 20 catechins, 19 flavonoids, 13 phenolic acids, and 4 organic acids. The content of certain amino acids and carbohydrates correlated with the shape of black tea. Furthermore, sweet aroma compound formation was facilitated by hot-air second-drying while the remaining second-drying approaches encouraged the formation of the fruity aroma compound. The results of the study provide a theoretical basis and technical instructions for the accurate and precise processing of premium black tea.

A Rapid Prediction Method of Moisture Content for Green Tea Fixation Based on WOA-Elman.

Fixation is the most critical step in the green tea process. Hence, this study developed a rapid and accurate moisture content detection for the green tea fixation process based on near-infrared spectroscopy and computer vision. Specifically, we created a quantitative moisture content prediction model appropriate for the processing of green tea fixation. First, we collected spectrum and image information of green tea fixation leaves, utilizing near-infrared spectroscopy and computer vision. Then, we applied the partial least squares regression (PLSR), support vector regression (SVR), Elman neural network (ENN), and Elman neural network based on whale optimization algorithm (WOA-ENN) methods to build the prediction models for single data (data from a single sensor) and mid-level data fusion, respectively. The results revealed that the mid-level data fusion strategy combined with the WOA-ENN model attained the best effect. Namely, the prediction set correlation coefficient (Rp) was 0.9984, the root mean square error of prediction (RMSEP) was 0.0090, and the relative percent deviation (RPD) was 17.9294, highlighting the model's excellent predictive performance. Thus, this study identified the feasibility of predicting the moisture content in the process of green tea fixation by miniaturized near-infrared spectroscopy. Moreover, in establishing the model, the whale optimization algorithm was used to overcome the defect whereby the Elman neural network falls into the local optimum. In general, this study provides technical support for rapid and accurate moisture content detection in green tea fixation.