Extraction of Sensitive Spectral Bands and Construction of Spectral Indices for Quality Assessment of Fresh Tea Leaves

Abstract

Accurate, nondestructive assessment of fresh tea leaf quality is important for breeding and field management, yet most spectral work still targets processed or low-moisture products. Here, a mechanistically guided hyperspectral method was developed to estimate free amino acids (AA) and total polyphenols (TP) in fresh leaves. Spectral experiments on purified AA and TP powders and their water mixtures identified a key spectral window at 1660 nm. Fractional-order derivatives were applied to leaf reflectance spectra from 102 spring samples (53 varieties), and full-spectrum Partial Least Squares Regression (PLSR) models were used as comparison and validated on an independent set of 40 summer samples. PLSR achieved decent cross-validation coefficient of determination accuracy for AA (R²_cv = 0.867) and TP (R²_cv = 0.755) and good external prediction coefficient of determination accuracy (R²_P = 0.793 and 0.776, respectively). Guided by the powder and leaf-level analysis, two-band NDSI indices were derived: the AA index of 1735/1626 nm (R²_p = 0.687, RPDp = 1.788) and the TP index of 1673/1660 nm (R²_p = 0.785, RPDp = 2.157) approached the PLSR, indicating that much of the useful information for AA and TP is concentrated in this narrow window and can be captured by simple, interpretable indices potentially suitable for in-field sensing, pending validation across multiple sites, seasons, and management conditions.