An efficient method for tracing the geographic origin of Enshi Yulu fresh tea leaves based on near infrared spectroscopy combined with synergy interval PLS and genetic algorithm.

This paper applies near-infrared spectroscopy (NIRS) and multiple chemometrics to efficiently distinguish the origins of fresh tea leaves. The key components were obtained using the partial least squares discriminant analysis (PLS-DA) method. PLS, synergy interval PLS (siPLS), principal component analysis (PCA), genetic algorithm (GA), and their combination methods were used to establish NIRS non-destructive discrimination models. Then, the practical application was examined using external samples. The study identified nine key components (variable importance for the projection (VIP) > 1): epigallocatechin, epicatechin, total sugar, water extracts, total catechins, gallocatechin gallate, tea polyphenols, gallocatechin, and epigallocatechin gallate. Of the six NIRS models, the siPLS-GA model that used 37 spectral data points produced the best results (Rp2 = 0.9706, RMSEP = 0.0772, RPD = 6.59). This model had a prediction accuracy of 96.67% for the prediction set samples and 93.33% for the external samples. It offers a rapid, precise, and non-invasive approach to monitor and regulate the illicit trade of fresh tea leaves, thereby guaranteeing the authenticity of Enshi Yulu products from the processing source and fostering the long-term prosperity and stability of the Enshi Yulu tea industry. • There is a limited amount of research on using NIRS to trace the origin of fresh tea leaves. • PLS-DA was used to identify the origins of fresh tea leaves based on nine key components. • The NIRS-siPLS-GA digital combination model exhibits high robustness and wide adaptability. • It is a low-cost modeling solution effectively addresses the authenticity problem of Enshi Yulu. • The research findings suggest broad application prospects. [ABSTRACT FROM AUTHOR]