文献类型： 外文期刊
作者： Zou, Zhiyong 1 ; Chen, Jie 1 ; Zhou, Man 2 ; Wang, Zhitang 3 ; Liu, Ke 4 ; Zhao, Yongpeng 1 ; Wang, Yuchao 1 ; Wu, Weijia 1 ; Xu, Lijia 1 ;
作者机构： 1.Sichuan Agr Univ, Sch Mech & Elect Engn, Yaan, Peoples R China
2.Sichuan Agr Univ, Sch Food, Yaan, Peoples R China
3.Hunan Univ Sci & Engn, Coll Continuing Educ, Yaan, Peoples R China
4.Sichuan Acad Agr Sci, Yaan, Peoples R China
关键词： hyperspectral; freshness; non-destructive testing techniques; feature selection; regression model
期刊名称：FOOD SCIENCE AND TECHNOLOGY （ 影响因子：2.602； 五年影响因子：2.968 ）
ISSN： 0101-2061
年卷期： 2022 年 42 卷
页码：
收录情况： SCI
摘要： Peanut storage time affected the quality of peanut seed sowing and germination and also affected the taste of edible peanuts. With the increase of peanut storage time, the total amount of water and amino acids decreased, and peanuts appeared moldy. The artificial judgment of peanut storage time mostly relied on visual classification to evaluate the color, which leads to large differences in color classifications between observers. This research was conducted to determine the fresh state of peanuts during storage based on the hyperspectral imaging (HSI) technology, and to identify the storage time of peanuts through hyperspectral images (387 similar to 1035 nm). Three models, two preprocessing methods, and two feature band extraction methods were combined. The experimental results shows that the DT-MF-Catboost model was the best method to detect the storage time of peanuts, and its accuracy of identifying the storage time of peanuts was 97.53%. Studies have shown that HSI has great potential in classifying the freshness and identification of peanuts, and provides a basis for non-destructive testing classification as well as grading of peanuts during storage.
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