Scientia Agricultura Sinica ›› 2026, Vol. 59 ›› Issue (18): 4149-4162.doi: 10.3864/j.issn.0578-1752.2026.18.015

• HORTICULTURE • Previous Articles     Next Articles

Rapid Prediction of Tea Leaf Catechins via Hyperspectral Multi-Task Synergistic Sensing Fusing Plant Physiological Priors

CHAI ZhiChao(), QIAN XiaoNa, YU YongChen, LI XiWang, SUN XiaoLing()   

  1. Tea Research Institute, Chinese Academy of Agricultural Sciences/National Center for Tea Plant Improvement/State Key Laboratory of Tea Plant Germplasm Innovation and Resource Utilization, Key Laboratory of Biology/Genetics and Breeding of Special Economic Animals and Plants, Ministry of Agriculture and Rural Affairs, Hangzhou 310008
  • Received:2026-01-28 Accepted:2026-04-28 Online:2026-09-16 Published:2026-09-20
  • Contact: SUN XiaoLing

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Abstract:

【Objective】To meet the demand for in situ, real-time, and non-destructive monitoring of tea leaf quality in smart tea gardens, this study proposed a multi-component quantitative prediction method based on visible-near infrared hyperspectral imaging (400-1 000 nm) integrated with plant physiological priors, so as to realize rapid assessment of secondary metabolites, such as catechins.【Method】Twelve tea cultivars were used as experiment materials. Leaves were classed as young (2nd-3rd), mature (4th-5th), and old (6th-7th) to represent physiological gradients, giving 429 independent samples in total. For each leaf, hyperspectral reflectance was recorded, and the contents of catechin (C), epicatechin (EC), epigallocatechin (EGC), epicatechin gallate (ECG), epigallocatechin gallate (EGCG), gallocatechin (GC), gallocatechin gallate (GCG), caffeine (CAF), and gallic acid (GA) were quantified by high-performance liquid chromatography. Spectra were preprocessed using Savitzky-Golay (SG) smoothing, standard normal variate (SNV), and first derivative. The generalization performance of partial least squares regression (PLSR), support vector regression (SVR), XGBoost, multilayer perceptron (MLP), and 1D-CNN-SE was compared. Based on the best single-task model, a multi-task XGBoost framework was then developed to simultaneously predict the nine components. Additionally, the successive projections algorithm (SPA) was used to select key wavelengths from the full spectrum (557 bands) to assess the feasibility of simplified modeling.【Result】When leaf maturity was ignored, EGCG and GCG showed a spurious negative correlation due to confounding by maturity, which was a clear instance of Simpson's paradox. This meant that maturity needed to be accounted for, either by stratifying the data or by explicit adjustment, so that models would not learn false correlations. Among the tested models, XGBoost combined with SG+SNV+first-derivative preprocessing achieved the best performance on the small-sample with high-dimensional spectral dataset. The R2 values for EGCG and GCG were approximately 0.60 and 0.59, respectively. In contrast, 1D-CNN-SE achieved calibration set R2 value above 0.90 but dropped to about 0.40-0.50 on the prediction set, showing clear overfitting. The multi-task XGBoost model predicted all nine components in a single run; the R2 value for EGCG remained about 0.57, and the RPD values for EGCG, GCG, and CAF were about 1.48-1.52, with only a slight loss in single-component accuracy but a substantial gain in throughput. After SPA reduced the full spectrum from 557 bands to 18 key wavelengths, the R2 for EGCG remained about 0.54. These key wavelengths fell mainly in the visible region and the near-infrared overtone absorption bands, supporting the design of portable quality sensors based on discrete wavelengths.【Conclusion】The spectral modeling that integrated physiological priors, such as leaf maturity, could enhance the model’s explanatory power and improve its generalization across cultivars and leaf ages. The single- and multi-task XGBoost frameworks, combined with wavelength selection, provided a theoretical basis and practical support for rapid multi-index tea quality assessment in tea plantations.

Key words: tea plant, catechins, hyperspectral imaging, multi-task learning, successive projections algorithm (SPA), leaf maturity, plant physiological priors

Fig. 1

Comparison of the evolution of spectral signals from raw hyperspectral reflectance (A) to those transformed by SG smoothing (B), SNV normalization (C), and first derivative (D)"

Fig. 2

Hyperspectral multi-task collaborative sensing workflow for tea leaf catechins"

Fig. 3

Gradient distribution of catechins and chemical components content in tea leaves with different maturity levels"

Fig. 4

Hierarchical clustering heatmap of tea leaf samples based on chemical component profiles"

Fig. 5

Pseudo-negative correlation between EGCG and GCG caused by tea leaf maturity confounding (Simpson’s paradox)"

Fig. 6

Pearson correlation coefficient matrix of tea leaf chemical components A: Young leaf; B: Mature leaf; C: Old leaf; D: Whole sample. *: Correlation is significant at the 0.05 level; **: Correlation is significant at the 0.01 level; ***: Correlation is significant at the 0.001 level"

Fig. 7

Radar chart of prediction performance of various algorithms on catechin components under different preprocessing strategies"

Table 1

Performance statistics of the multi-task XGBoost model for simultaneous prediction of nine components in tea leaves based on full- spectrum data"

成分
Component		 校正集决定系数
$R_{\mathrm{c}}^{2}$		 预测集决定系数
$R_{\mathrm{p}}^{2}$		 校正均方根误差
RMSEC		 预测均方根误差
RMSEP		 相对分析误差
RPD
儿茶素 C 0.937 0.568 0.041 0.122 1.522
表没食子儿茶素没食子酸酯 EGCG 0.903 0.567 0.557 1.128 1.519
没食子儿茶素没食子酸酯 GCG 0.950 0.548 0.014 0.042 1.487
咖啡因CAF 0.918 0.545 0.229 0.498 1.482
没食子儿茶素 GC 0.947 0.540 0.056 0.142 1.474
没食子酸GA 0.893 0.419 0.000 0.001 1.312
表儿茶素没食子酸酯 ECG 0.908 0.413 0.170 0.404 1.305
表儿茶素EC 0.929 0.287 0.151 0.385 1.185
表没食子儿茶素 EGC 0.904 0.099 0.393 1.129 1.053

Table 2

Distribution of characteristic bands for tea leaves each component selected by the successive projection algorithm"

成分
Component		 波段数量
Number of bands		 特征波段
Characteristic wavelengths (nm)
表没食子儿茶素没食子酸酯 EGCG 18 [763.2, 872.3, 715.0, 414.5, 669.2, 843.6, 906.6, 908.8, 926.5, 867.9, 549.9, 887.8, 943.0, 445.2, 911.0, 852.5, 928.7, 940.8]
咖啡因CAF 18 [561.6, 416.5, 934.2, 487.7, 867.9, 906.6, 898.9, 713.9, 893.3, 549.9, 926.5, 843.6, 675.7, 400.2, 940.8, 852.5, 920.9, 877.9]
没食子儿茶素没食子酸酯 GCG 12 [443.1, 496.1, 839.2, 961.8, 414.5, 844.7, 906.6, 866.8, 675.7, 926.5, 549.9, 713.9]
没食子儿茶素 GC 18 [717.2, 822.7, 911.0, 415.5, 485.6, 871.2, 895.5, 926.5, 549.9, 866.8, 675.7, 933.1, 843.6, 404.3, 897.7, 940.8, 918.7, 943.0]
儿茶素 C 7 [635.6, 417.5, 954.0, 895.5, 834.8, 892.2, 485.6]
表儿茶素没食子酸酯 ECG 7 [717.2, 822.7, 911.0, 415.5, 485.6, 871.2, 895.5]
没食子酸GA 1 [592.6]
表儿茶素EC 1 [549.9]
表没食子儿茶素 EGC 1 [549.9]

Table 3

Prediction performance of the simplified XGBoost model based on SPA-selected characteristic bands"

预处理方法
Preprocessing method		 成分
Component		 校正集决定系数
$R_{\mathrm{c}}^{2}$		 预测集决定系数
$R_{\mathrm{p}}^{2}$		 校正均方根误差
RMSEC		 预测均方根误差
RMSEP		 相对分析误差
RPD
SG+SNV+D1ST 表没食子儿茶素没食子酸酯 EGCG 0.963 0.544 0.341 1.157 1.482
咖啡因CAF 0.919 0.500 0.228 0.522 1.414
没食子儿茶素没食子酸酯 GCG 0.919 0.458 0.017 0.046 1.359
没食子儿茶素 GC 0.900 0.353 0.076 0.168 1.243
儿茶素 C 0.752 0.347 0.082 0.150 1.238
表儿茶素没食子酸酯 ECG 0.625 0.266 0.344 0.452 1.167
没食子酸GA 0.419 0.255 0.001 0.001 1.159
表儿茶素EC 0.590 0.026 0.362 0.450 1.013
表没食子儿茶素 EGC 0.436 -0.079 0.953 1.235 0.963
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