A novel hybrid sensor array based on the polyphenol oxidase and its nanozymes combined with the machine learning based dual output model to identify tea polyphenols and Chinese teas

Abstract

A novel sensor array was developed based on the enzyme/nanozyme hybridization for the identification of tea polyphenols (TPs) and Chinese teas. The enzyme/nanozyme with polyphenol oxidase activity can catalyze the reaction between TPs and 4-aminoantipyrine (4-AAP) to produce differences in color, and the sensor array was thus constructed to accurately identify TPs mixed in different species, concentrations, or ratios. In addition, a machine learning based dual output model was further used to effectively predict the classes and concentrations of unknown samples. Therefore, the qualitative and quantitative detection of TPs can be realized continuously and quickly. Furthermore, the sensor array combining the machine learning based dual output model was also utilized for the identification of Chinese teas. The method can distinguish the six teas series in China, and then precisely differentiate the more specific tea varieties. This study provides an efficient and facile strategy for the identification of teas and tea products.