数字农科院2.0

Estimation of Amino Acid and Tea Polyphenol Content of Tea Fresh Leaves Based on Fractional-Order Differential Spectroscopy

文献类型： 外文期刊

作者： Li, Shirui;Sun, Rui;Li, Xin;Li, Yang;Zhao, Liang;Huang, Xinyu;Xu, Yufei

作者机构：

关键词： tea quality assessment;fresh leaves;spectra;fractional-order derivatives;competitive adaptive reweighted sampling

期刊名称： APPLIED SCIENCES-BASEL

ISSN：

年卷期： 2025 年 15 卷 11 期

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收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： 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.

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