Investigation of Microbial Fermentation Degree of Pu-Erh Tea Using Deep Learning Coupled Colorimetric Sensor Array via Prediction of Total Polyphenols

Abstract

The degree of tea fermentation is crucial as it ultimately indicates the quality of the tea. Hence, this study developed a total polyphenol prediction system for Pu-erh tea liquid using eight porphyrin dyes and one pH dye in a printed colorimetric sensor array (CSA) coupled with a convolutional neural network (CNN) during microbial fermentation. Firstly, the Box–Behnken sampling method was applied to optimize the degree of microbial fermentation of Pu-erh tea liquid using the response surface methodology. Under optimized conditions, the polyphenol degradation rate reached up to 66.146%. CSA images were then collected from the volatile compounds of Pu-erh tea-reacted CSA sensors. Subsequently, six chemometric approaches were comparatively investigated, and CNN achieved the best results for predicting total polyphenol content. Therefore, the results suggest that the proposed approach can be used to predict the degree of fermentation by measuring total polyphenols in microbial-fermented Pu-erh tea liquid.