Amino acids (AAs) and tea polyphenols (TPs) are essential quality indicators in tea, impacting sensory attributes and economic value. Hyperspectral technology enables efficient, real-time detection of these compounds on field-grown tea leaves. “The original spectra were preprocessed using fractional-order derivatives (0.1–1.0 orders) to enhance subtle spectral features. Compared to fixed integer-order derivatives (e.g., first or second order), fractional-order derivatives allow continuous tuning between 0 and 1, thereby amplifying minor absorption peaks while effectively suppressing noise amplification”. The Competitive Adaptive Reweighted Sampling (CARS) method selects optimal spectral bands, and Partial Least Squares Regression (PLSR) models were built with raw spectral reflectance as independent variables and AA and TP content as dependent variables. Results show that FOD had better prediction accuracy compared to classical integer-order derivatives, e.g., the optimal FOD order of 0.7 for AA prediction increased the R² from 0.73 to 0.80 and reduced the RMSE from 0.30% to 0.25%, while for TP prediction, a FOD order of 0.1 raised the R² from 0.40 to 0.42 and lowered the RMSE from 4.03% to 3.96%. In addition, CARS shows a better performance over the correlation coefficient (CC) method in model accuracy, contributing to more accurate selection of sensitive bands for the content prediction of tea ingredients. Our FOD–CARS–PLSR models achieved an R² of 0.80 and RMSE of 0.25% for AAs, and an R² of 0.42 and RMSE of 3.96% for TPs in fresh tea leaves. Beyond tea quality monitoring, this flexible preprocessing and modeling framework can be readily adapted to estimate biochemical or biophysical properties in other crops, soils, or vegetated ecosystems, offering a generalizable tool for precision agriculture and environmental sensing.

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 ($R_{\mathrm{c}\mathrm{v}}^2=0.867$) and TP ($R_{\mathrm{c}\mathrm{v}}^2=0.755$) and good external prediction coefficient of determination accuracy ($R_{\mathrm{P}}^2=0.793$ 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 (R²ₚ = 0.687, RPDₚ = 1.788) and the TP index of 1673/1660 nm (R²ₚ = 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.