Chen Yong, Wang Xueya, Yang Wenzheng, Peng Guihua, Chen Ju, Yin Yong, Yan Jia
College of Artificial Intelligence, Southwest University, Chongqing 400715, China.
Chili Pepper Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China.
Food Chem. 2025 Jul 1;479:143850. doi: 10.1016/j.foodchem.2025.143850. Epub 2025 Mar 12.
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.
辣椒的品质与其品种和地理来源密切相关。市场上常以次充好，且加工过程中会发生交叉污染。现有的方法无法快速便捷地区分不同辣椒品种或产地，这些方法需要昂贵的实验设备和专业技能。诸如能量色散X射线荧光光谱法和电感耦合等离子体光谱法等技术已被用于辣椒分类和产地溯源，但这些方法要么成本高昂，要么具有破坏性。为应对准确识别辣椒品种和辣椒产地溯源的挑战，本文提出了一种集成电子鼻（e-nose）的传感器感知卷积网络（SACNet），用于辣椒品种的准确分类和产地溯源。电子鼻系统从各种辣椒中采集气体样本。我们引入了一个传感器注意力模块，自适应地聚焦于每个传感器在收集气体信息方面的重要性。此外，我们引入了局部传感和广域传感结构，专门捕捉气体信息特征，从而实现对辣椒气体的高精度识别。在与其他网络的对比实验中，SACNet在品种分类和产地溯源方面均表现出优异的性能，并且在参数量方面具有显著优势。具体而言，SACNet在数据集A上的品种分类准确率达到98.56%，在数据集B上的产地溯源准确率达到97.43%，在数据集C上的准确率达到99.31%。综上所述，SACNet与电子鼻的结合为识别辣椒品种和产地提供了一种有效的策略。
