Quantitative prediction and visual detection of the moisture content of withering leaves in black tea (Camellia sinensis) with hyperspectral image
文献类型： 外文期刊
作者： Dong, Chunwang 1 ; An, Ting 2 ; Yang, Ming 4 ; Yang, Chongshan 1 ; Liu, Zhongyuan 1 ; Li, Yang 1 ; Duan, Dandan 3 ; Fan, Shuxiang 2 ;
作者机构： 1.Chinese Acad Agr Sci, Tea Res Inst, Hangzhou 310008, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China
3.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China
4.Chinese Acad Sci, Bur Dev Planning, Beijing 100864, Peoples R China
关键词： Accumulated withering leaves; Moisture content; Principal component analysis; Chemometric method; Nondestructive detection
期刊名称：INFRARED PHYSICS & TECHNOLOGY （ 影响因子：2.997； 五年影响因子：2.962 ）
ISSN： 1350-4495
年卷期： 2022 年 123 卷
页码：
收录情况： SCI
摘要： In actual production, the rapid and non-destructive detection of moisture content in withering leaves is still an unsolved problem due to the randomness of withering leaves on the conveyor belt and the limitation of detection range using near infrared detection equipment. To solve this problem, the hyperspectral images of accumulated withering leaves were obtained and the moisture prediction model was established in the range of 400-1000 nm. Different pretreatment and effective bands selection methods were used to optimize the model. The results showed that the performance of nonlinear model was better than that of linear model and the SNV-Si-CARS-ELM model had the best performance. The R-c(2), RMSEC, R-p(2), RMSEP and RPD were 0.9940, 0.0074, 0.9942, 0.0078 and 13.0907, respectively. Furthermore, the moisture distribution maps of accumulated withering leaves in different withering degrees were developed. This study provides a theoretical basis for the on-line application of hyper spectral image technology in black tea processing.
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