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.

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.

A sensor array based on a nanozyme with polyphenol oxidase activity for the identification of tea polyphenols and Chinese green tea.

Green tea is popular among consumers because of its high nutritional value and unique flavor. There is often a strong correlation among the type of tea, its quality level and the price. Therefore, the rapid identification of tea types and the judgment of tea quality grades are particularly important. In this work, a novel sensor array based on nanozyme with polyphenol oxidase (PPO) activity is proposed for the identification of tea polyphenols (TPs) and Chinese green tea. The absorption spectra changes of the nanozyme and its substrate in the presence of different TPs were first investigated. The feature spectra were scientifically selected using genetic algorithm (GA), and then a sensor array with 15 sensing units (5 wavelengths × 3 time) was constructed. Combined with the support vector machine (SVM) discriminative model, the discriminative rate of this sensor array was 100% for different concentrations of typical TPs in Chinese green tea with a detection limit of 5 µM. In addition, the identification of different concentrations of the same tea polyphenols and mixed tea polyphenols have also been achieved. Based on the above study, we further developed a facile and efficient new method for the category differentiation and adulteration identification of green tea, and the accuracy of this array was 96.88% and 100% for eight types of green teas and different adulteration ratios of Biluochun, respectively. This work has significance for the rapid discrimination of green tea brands and adulteration.