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Non-destructive determination of taste-related substances in fresh tea using NIR spectra

文献类型： 外文期刊

作者： Wang, Fan 1 ; Cao, Qiong 1 ; Zhao, Chunjiang 1 ; Duan, Dandan 1 ; Chen, Longyue 2 ; Meng, Xiangyu 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 10097, Peoples R China

2.Guangdong Prov Lab Modern Agr Sci & Technol Lingna, Heyuan 517000, Peoples R China

3.Hunan Agr Univ Coll, Changsha 410000, Peoples R China

4.Nongxin Technol Guangzhou Co Ltd, Guangzhou 511466, Peoples R China

5.Qingyuan Smart Agr & Rural Res Inst, Qingyuan 511500, Peoples R China

关键词： Non-destructive detection; Taste constituents; Near-infrared spectra; Continuous wavelet transform; Bootstrapping soft shrinkage

期刊名称：JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION （ 影响因子：3.4； 五年影响因子：3.2 ）

ISSN： 2193-4126

年卷期： 2023 年

页码：

收录情况： SCI

摘要： Non-destructive determination of taste-related substances using near-infrared spectroscopy (NIRS) is of great significance for effectively evaluating tea quality. In the present research work, NIRS (400-2400 nm) were correlated with tea polyphenol (TP), free amino acid (FAA), caffeine (CAFF), and total sugar (TS) content in 187 tea samples by processing of spectra using continuous wavelet transform (CWT). Some effective variable selection algorithms (variable combination population analysis and iterative retained information variable algorithm (VCPA-IRIV), competitive adaptive reweighted sampling (CARS), variable iterative space shrinkage approach (VISSA), bootstrapping soft shrinkage (BOSS), and genetic algorithm (GA) were used for the quantification of taste-related substances of tea samples. The developed models were established and evaluated for the content of taste substances in several varieties of tea, including PLS and the potential of some machine learning models such as Gaussian process regression, support vector regression, and random forest regression. The efficiency of the developed model was significantly enhanced with the use of CWT-BOSS-PLS for monitoring the state of each component. More than 99.34% of variables was reduced. The predicted R values for TP, FAA, CAFF, and TS were 0.6891, 0.8385, 0.6810, and 0.8638 with accuracy improved by 6%, 3%, 45%, and 8%. Overall, this study provides an important support method for the practical application of content analysis for tea taste components.

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