Abstract: In this study, a method for detecting the moisture content of withered leaves for black tea was proposed based on micro-near infrared spectroscopy (NIR). An NIR spectrometer developed in our lab was used to collect diffuse reflectance spectra of the withered leaf samples, whose moisture content was measured using a moisture meter at various time points. Pretreatment methods, variable screening methods and principal component analysis (PCA) were adopted to perform optimization and dimensionality reduction of spectral data to establish a discriminant model for the withering degree and a prediction model for the moisture content of withered leaves for Yimeng black tea. The results showed that the random forest (RF) discriminant model achieved an accuracy rate of 99.4% on the test set, exhibiting extraordinary classification performance. The model developed using bootstrapping soft shrinkage combined with support vector regression (BOSS-SVR) exhibited good prediction performance with correlation coefficient of calibration (rc) of 0.994, correlation coefficient of prediction (rp) of 0.984, root mean square error of calibration (RMSEC) of 0.730, root mean square error of prediction (RMSEP) of 1.198, and relative percent deviation (RPD) of 4.485. This research provides a theoretical basis and data support for the standardized and digital production of Yimeng black tea.

Key words: near-infrared spectroscopy; Yimeng black tea; moisture detection; model optimization