Evaluation of black tea appearance quality using a segmentation-based feature extraction method
文献类型： 外文期刊
作者： Song, Feihu 1 ; Lu, Xiaolong 1 ; Lin, Yiqing 1 ; Zhou, Qiaoyi 2 ; Li, Zhenfeng 1 ; Ling, Caijin 2 ; Song, Chunfang 1 ;
作者机构： 1.Jiangnan Univ, Sch Mech Engn, Jiangsu Key Lab Adv Food Mfg Equipment & Technol, Wuxi 214122, Peoples R China
2.Guangdong Acad Agr Sci, Tea Res Inst, Guangdong Prov Key Lab Tea Plant Resources Innovat, Guangzhou 510640, Peoples R China
关键词： Black tea; K-means clustering algorithm; Image segmentation; Gray-level run-length matrix; Sensory evaluation
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： 2024 年 58 卷
页码：
收录情况： SCI
摘要： Compared with highly subjective manual sensory quality evaluation, the application of computer vision techniques in black tea appearance quality evaluation helps to establish an objective and efficient black tea quality evaluation system. In this study, Yinghong No. 9 black tea was taken as the research object, and the gold pekoe, color and strips were adopted as the appearance evaluation characteristics for black tea. An image segmentation method based on the improved K-means clustering algorithm was proposed to realize the segmentation of the dark background area, tea area and golden pekoe area. The CIELAB color model was used to extract color features of the tea area. The texture features extracted by GLRLM were applied to evaluate the strips. The RF, SVR and BPNN were selected to construct prediction models for evaluating tea appearance quality. The prediction accuracy and generalization ability of the RF model are superior to those of the SVR model and BP model, with R2p, RMSEP and RPD values of 0.898, 1.548 and 3.207, respectively. The proposed feature extraction method based on regional segmentation intuitively described the key evaluation characteristics of black tea appearance, and the predicted results were highly consistent with the manual sensory evaluation.
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