An efficient method for chili pepper variety classification and origin tracing based on an electronic nose and deep learning
文献类型： 外文期刊
作者： Chen, Yong 1 ; Wang, Xueya 2 ; Yang, Wenzheng 1 ; Peng, Guihua 2 ; Chen, Ju 2 ; Yin, Yong 2 ; Yan, Jia 1 ;
作者机构： 1.Southwest Univ, Coll Artificial Intelligence, Chongqing 400715, Peoples R China
2.Guizhou Acad Agr Sci, Chili Pepper Res Inst, Guiyang 550006, Peoples R China
3.Southwest Univ, Key Lab Luminescence Anal & Mol Sensing, Minist Educ, Chongqing 400715, Peoples R China
4.Chongqing Key Lab Brain inspired Comp & Intelligen, Chongqing 400715, Peoples R China
关键词： Chili pepper; Variety classification; Origin traceability; Deep learning; Electronic nose; Machine olfaction; Gas recognition
期刊名称：FOOD CHEMISTRY （ 影响因子：9.8； 五年影响因子：9.7 ）
ISSN： 0308-8146
年卷期： 2025 年 479 卷
页码：
收录情况： SCI
摘要： The quality of chili peppers is closely related to their variety and geographical origin. The market often substitutes high-quality chili peppers with inferior ones, and cross-contamination occurs during processing. The existing methods cannot quickly and conveniently distinguish between different chili varieties or origins, which require expensive experimental equipment and professional skills. Techniques such as energy-dispersive X-ray fluorescence and inductively coupled plasma spectroscopy have been used for chili pepper classification and origin tracing, but these methods are either costly or destructive. To address the challenges of accurately identifying chili pepper varieties and origin tracing of chili peppers, this paper presents a sensor-aware convolutional network (SACNet) integrated with an electronic nose (e-nose) for accurate variety classification and origin traceability of chili peppers. The e-nose system collects gas samples from various chili peppers. We introduce a sensor attention module that adaptively focuses on the importance of each sensor in gathering gas information. Additionally, we introduce a local sensing and wide-area sensing structure to specifically capture gas information features, enabling high-precision identification of chili pepper gases. In comparative experiments with other networks, SACNet demonstrated excellent performance in both variety classification and origin traceability, and it showed significant advantages in terms of parameter quantity. Specifically, SACNet achieved 98.56 % accuracy in variety classification with Dataset A, 97.43 % accuracy in origin traceability with Dataset B, and 99.31 % accuracy with Dataset C. In summary, the combination of SACNet and an e-nose provides an effective strategy for identifying the varieties and origins of chili peppers.
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