Non-destructive Detection of the Moisture Content of Withered Leaves for Black Tea Based on Micro-Near Infrared Spectroscopy

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 (r_c) of 0.994, correlation coefficient of prediction (r_p) 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.