Rapid discrimination of quality grade of black tea based on near-infrared spectroscopy (NIRS), electronic nose (E-nose) and data fusion
文献类型： 外文期刊
作者： Xia, Hongling 1 ; Chen, Wei 1 ; Hu, Die 1 ; Miao, Aiqing 1 ; Qiao, Xiaoyan 1 ; Qiu, Guangjun 3 ; Liang, Jianhua 1 ; Guo, Weiqing 2 ; Ma, Chengying 1 ;
作者机构： 1.Guangdong Acad Agr Sci, Tea Res Inst, Guangdong Prov Key Lab Tea Plant Resources Innovat, Guangzhou 510640, Peoples R China
2.GRINM Guangdong Inst Adv Mat & Technol, Foshan 528000, Guangdong, Peoples R China
3.Guangdong Acad Agr Sci, Inst Facil Agr, Guangzhou 510640, Peoples R China
关键词： Tea; Quality evaluation; Near -infrared spectroscopy; Electronic nose; Data fusion
期刊名称：FOOD CHEMISTRY （ 影响因子：8.8； 五年影响因子：8.6 ）
ISSN： 0308-8146
年卷期： 2024 年 440 卷
页码：
收录情况： SCI
摘要： For the manufacturing and sale of tea, rapid discrimination of overall quality grade is of great importance. However, present evaluation methods are time-consuming and labor-intensive. This study investigated the feasibility of combining advantages of near-infrared spectroscopy (NIRS) and electronic nose (E-nose) to assess the tea quality. We found that NIRS and E-nose models effectively identify taste and aroma quality grades, with the highest accuracies of 99.63% and 97.00%, respectively, by comparing different principal component numbers and classification algorithms. Additionally, the quantitative models based on NIRS predicted the con-tents of key substances. Based on this, NIRS and E-nose data were fused in the feature-level to build the overall quality evaluation model, achieving accuracies of 98.13%, 96.63% and 97.75% by support vector machine, K -nearest neighbors, and artificial neural network, respectively. This study reveals that the integration of NIRS and E-nose presents a novel and effective approach for rapidly identifying tea quality.
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