Colorimetric/fluorescent/photothermal multi-model sensor array based on the nanozyme for differentiating tea polyphenols and white tea vintages

Abstract

Sensor arrays relying solely on a single signal fail to accurately distinguish the vintage of white tea. In contrast, multi-signal sensor arrays have been shown to effectively enhance the accuracy of identifying diverse substances. In this work, the development of a multi-model sensor array based on the nanozyme was developed for the distinction of storage time of white tea for the first time. The nanozyme (BDC-Cu) was selected for its ability to generate three distinct sensor signals: colorimetric, fluorescent, and specially photothermal signals. Notably, photothermal signals are rarely utilized in sensor arrays. It was found that relying solely on colorimetric and fluorescent signals was insufficient to achieve accurate differentiation of tea polyphenols. However, the introduction of photothermal signals enabled 100 % differentiation of tea polyphenols. Consequently, a multi-modal sensor array was developed in conjunction with a random forest machine learning approach for the identification of tea polyphenols. The types and concentrations of tea polyphenols in white tea of different storage years are different. Consequently, the multi-signal sensor array has the potential to differentiate the storage year of white tea. In comparison with conventional manual inspection methods that rely on large instruments, the multi-signal sensor array ensures enhanced detection accuracy while significantly streamlining the testing process. This development provides a solid foundation for the commercial identification of white tea quality.

Keywords: Multi-modal sensor array; Nanozyme; Tea polyphenols; White tea identification.