Tracing the geographical origin of Chinese green tea based on fluorescent sensor array combined with multi-way...

Fluorescent sensor arrays are becoming a hot topic in many fields because they can simultaneously detect multiple targets in complex research systems. However, most researches have only collected two-dimensional fluorescence spectral data from fluorescent sensor array interacting with target analytes. In contrast, three-dimensional fluorescence spectra can provide richer information than two-dimensional fluorescence spectra. Based on the hypothesis that collecting three-dimensional fluorescence spectra can obtain more abundant information of green tea samples from different regions, which can improve the accuracy and reliability of origin identification. This study aimed to explore the feasibility of using three-dimensional fluorescent sensor array combined with multi-way pattern recognition methods for the origin discrimination of green tea based on the differences in the contents and types of metal ions in green tea. To investigate this, we first designed a fluorescent sensor array based on amino acid-derived carbon dots and examined its ability to recognize common metal ions in green tea. Excitation-emission matrix spectra of green tea extracts from different geographical origins after interaction with the fluorescent sensor array were collected. Several multi-way pattern recognition methods were used to analyze the three-dimensional fluorescent array data of 100 green tea samples from five origins. The overall classification results of green tea from the five geographical origins were satisfactory, with the best prediction accuracy reaching 96.88%. In comparison, multilinear partial least squares discriminant analysis could make full use of the information of three-dimensional fluorescence data. And its correct identification results for green tea were superior to those of unfold partial least squares discriminant analysis. These results sufficiently demonstrated that the fluorescent sensor array integrated with multi-way pattern recognition, has promising potential for tracing the origin of Chinese green tea.