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Monitoring black tea fermentation quality by intelligent sensors: Comparison of image, e-nose and data fusion

文献类型： 外文期刊

作者： Zhou, Qiaoyi 1 ; Dai, Zhenhua 2 ; Song, Feihu 2 ; Li, Zhenfeng 2 ; Song, Chunfang 2 ; Ling, Caijin 1 ;

作者机构： 1.Guangdong Acad Agr Sci, Tea Res Inst, Guangdong Prov Key Lab Tea Plant Resource Innovat, Guangzhou 510640, Peoples R China

2.Jicmgnan Univ, Sch Mech Engn, Jiangsu Key Lab Adv Food Mfg Equipment & Technol, Wuxi 214122, Peoples R China

关键词： Computer vision; Electronic nose (e-nose); Data fusion strategy; Black tea

期刊名称：FOOD BIOSCIENCE （ 影响因子：5.2； 五年影响因子：5.4 ）

ISSN： 2212-4292

年卷期： 2023 年 52 卷

页码：

收录情况： SCI

摘要： To scieudfically and objectively monitor the fermentatinn gudlity ot bkick tea, a computer vision system (CVS) and elecuonic nose (e-nose) mere employed to analyze the black tea image and odor ei:,envalues of Yinghong No. 9 black tea. First, me variation trends of tea polyphenols, volatile substances, image eigenvalues and odor eigen-alues with the extension of fermentation time were analyzed, and the fennentation process was categorized into three stages for classification. Second. principal component analysis (PCA) was employed on the image and odor eigenvalues obtained by CVS and e-nose. Partial least sguares disaiminant analysis (PLS-DA) mas peiforrned on '.17 volatile components, and 51 differential volatiles were screened out based on variable irnponance in projection (IP >= 1) and one-way analysis of variance (P < 0.05), including geraniol, linalool, nerolidol, and a-ionone. Then, image features and odor features are zsed by using a data fusion strategy. Finally, the image, smell and fusion information were cornbined with random forest (RF), K-nearest neighbor (KNN) and support vector machine (SVM) to establish the classification rnodels of different fermentation stages and to compaie them. The results show that the feature-level fusion strategy integrating the SVM was the most efficient approach, with classification accmacy rates of 100% for the training sets and 95.6% for the testing sets. The perfomiance of Support Vector Regression (SVR) prediction models for tea polyphenol content based on featurelevel fusion data outperfomied data-level models (Rc, RMSEC, Rp and RMSEP of O.96, 0.48 mg/g, 0.94, 0.6 mg/ g)center dot

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