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Research on hyperspectral timely monitoring model of green tea processing quality based on PSO-LSSVR

文献类型： 外文期刊

作者： Zhang, Kai 'xing 1 ; Zuo, Zongyuan 1 ; Zhou, Chang'an 1 ; Chen, He 1 ; Ding, Zhaotang 2 ;

作者机构： 1.Shandong Agr Univ, Coll Mech & Elect Engn, Tai An 271018, Peoples R China

2.Shandong Acad Agr Sci, Tea Res Inst, Jinan, Peoples R China

关键词： Green tea; Tea inclusions; Hyperspectral; PSO-LSSVR

期刊名称：JOURNAL OF FOOD COMPOSITION AND ANALYSIS （ 影响因子：4.0； 五年影响因子：4.1 ）

ISSN： 0889-1575

年卷期： 2024 年 134 卷

页码：

收录情况： SCI

摘要： To monitor the processing status and content of the main components of tea leaves during green tea processing timely, a monitoring model for the contents of green tea processing based on hyperspectral imaging is established in this paper. The aim of this model is to improve the quality of finished green tea products by addressing the shortcomings of existing methods that cannot achieve timely, non-destructive, and accurate detection. Initially, hyperspectral and biochemical data were collected across four processing procedures of green tea. Following the exclusion of abnormal samples, preprocessing techniques including Savitzky-Golay, multiple scatter correction, and 2nd derivative were compared and selected. Subsequently, feature wavelengths extracted through correlation analysis, principal component analysis, and successive projections algorithm were utilized to construct least squares support vector regression (LSSVR) prediction models, determining the optimal feature extraction method. Finally, particle swarm optimization (PSO) was employed to refine LSSVR model parameters, resulting in the PSO-LSSVR model. Post-optimization, the determination coefficient (R2) exceeded 0.9, the root means square error remained around 0.005, and the residual prediction deviation surpassed 3. The PSO-LSSVR model exhibited better predictive capabilities compared to the LSSVR model, offering a method reference for timely monitoring of green tea processing inclusions.

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