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Prediction of multi-task physicochemical indices based on hyperspectral imaging and analysis of the relationship between physicochemical composition and sensory quality of tea

文献类型： 外文期刊

作者： Jiang, Xinna 1 ; Cao, Xingda 1 ; Liu, Quancheng 1 ; Wang, Fan 1 ; Fan, Shuxiang 1 ; Yan, Lei 1 ; Wei, Yuqing 1 ; Chen, Yun 1 ; Yang, Guijun 2 ; Xu, Bo 2 ; Wu, Quan 3 ; Xu, Ze 3 ; Yang, Haibin 3 ; Zhai, Xiuming 3 ;

作者机构： 1.Beijing Forestry Univ, Sch Technol, Beijing 100083, Peoples R China

2.Chongqing Acad Agr Sci, Chongqing 400000, Peoples R China

3.Chongqing Acad Agr Sci, Tea Res Inst, Yongchuan 402160, Peoples R China

关键词： Hyperspectral imaging; Green tea; Multi-task regression model; Quantitative analysis; Correlation analysis

期刊名称：FOOD RESEARCH INTERNATIONAL （ 影响因子：8.0； 五年影响因子：8.5 ）

ISSN： 0963-9969

年卷期： 2025 年 211 卷

页码：

收录情况： SCI

摘要： Tea is highly valued by consumers worldwide for its distinctive flavor and rich nutritional profile. Efficient and accurate assessment of tea quality is essential for both producers and consumers. This study focuses on Yongchuan Xiuya green tea and utilizes hyperspectral imaging (HSI) technology integrated with a multi-task regression (MTR) model to simultaneously predict 12 physicochemical indices (WE, SSC, FAA, TP, CAF, EGCG, EGC, EC, ECG, GA, C, GC). To develop this model, the relationship between sensory attributes and physicochemical components was first analyzed, identifying key quality indicators. The original spectral data were preprocessed using the SNV-SG method to enhance data quality. The predictive performance of various models, including partial least squares regression (PLSR), random forest (RF), and extreme gradient boosting (XGBoost), was evaluated, with XGBoost identified as the most effective. Subsequently, the Newton-RaphsonBased Optimization (NRBO) algorithm was employed to optimize the parameters of XGBoost, forming the foundation of the MTR model. By incorporating feature enhancement and correlation analysis, the MTR model effectively predicted multiple quality indices. The model exhibited high predictive accuracy, as indicated by an average RP2 of 0.9774 and an average RMSEP of 0.1097, demonstrating its robustness and reliability in assessing tea quality.

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