{"title":"利用增强型深度学习技术快速检测中药材中的霉菌。","authors":"Ting Zhu, Xincan Wu, Ling Ma, Yadian Zeng, Junbo Lian, Jiapeng Liu, Xinnan Chen, Lei Zhong, Jingnan Chang, Guohua Hui","doi":"10.1089/jmf.2024.k.0004","DOIUrl":null,"url":null,"abstract":"<p><p>Mold contamination poses a significant challenge in the processing and storage of Chinese herbal medicines (CHM), leading to quality degradation and reduced efficacy. To address this issue, we propose a rapid and accurate detection method for molds in CHM, with a specific focus on <i>Atractylodes macrocephala</i>, using electronic nose (e-nose) technology. The proposed method introduces an eccentric temporal convolutional network (ETCN) model, which effectively captures temporal and spatial information from the e-nose data, enabling efficient and precise mold detection in CHM. In our approach, we employ the stochastic resonance (SR) technique to eliminate noise from the raw e-nose data. By comprehensively analyzing data from eight sensors, the SR-enhanced ETCN (SR-ETCN) method achieves an impressive accuracy of 94.3%, outperforming seven other comparative models that use only the response time of 7.0 seconds before the rise phase. The experimental results showcase the ETCN model's accuracy and efficiency, providing a reliable solution for mold detection in Chinese herbal medicine. This study contributes significantly to expediting the assessment of herbal medicine quality, thereby helping to ensure the safety and efficacy of traditional medicinal practices.</p>","PeriodicalId":16440,"journal":{"name":"Journal of medicinal food","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Rapid Mold Detection in Chinese Herbal Medicine Using Enhanced Deep Learning Technology.\",\"authors\":\"Ting Zhu, Xincan Wu, Ling Ma, Yadian Zeng, Junbo Lian, Jiapeng Liu, Xinnan Chen, Lei Zhong, Jingnan Chang, Guohua Hui\",\"doi\":\"10.1089/jmf.2024.k.0004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Mold contamination poses a significant challenge in the processing and storage of Chinese herbal medicines (CHM), leading to quality degradation and reduced efficacy. To address this issue, we propose a rapid and accurate detection method for molds in CHM, with a specific focus on <i>Atractylodes macrocephala</i>, using electronic nose (e-nose) technology. The proposed method introduces an eccentric temporal convolutional network (ETCN) model, which effectively captures temporal and spatial information from the e-nose data, enabling efficient and precise mold detection in CHM. In our approach, we employ the stochastic resonance (SR) technique to eliminate noise from the raw e-nose data. By comprehensively analyzing data from eight sensors, the SR-enhanced ETCN (SR-ETCN) method achieves an impressive accuracy of 94.3%, outperforming seven other comparative models that use only the response time of 7.0 seconds before the rise phase. The experimental results showcase the ETCN model's accuracy and efficiency, providing a reliable solution for mold detection in Chinese herbal medicine. This study contributes significantly to expediting the assessment of herbal medicine quality, thereby helping to ensure the safety and efficacy of traditional medicinal practices.</p>\",\"PeriodicalId\":16440,\"journal\":{\"name\":\"Journal of medicinal food\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of medicinal food\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1089/jmf.2024.k.0004\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/6/26 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"CHEMISTRY, MEDICINAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of medicinal food","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1089/jmf.2024.k.0004","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/26 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
Rapid Mold Detection in Chinese Herbal Medicine Using Enhanced Deep Learning Technology.
Mold contamination poses a significant challenge in the processing and storage of Chinese herbal medicines (CHM), leading to quality degradation and reduced efficacy. To address this issue, we propose a rapid and accurate detection method for molds in CHM, with a specific focus on Atractylodes macrocephala, using electronic nose (e-nose) technology. The proposed method introduces an eccentric temporal convolutional network (ETCN) model, which effectively captures temporal and spatial information from the e-nose data, enabling efficient and precise mold detection in CHM. In our approach, we employ the stochastic resonance (SR) technique to eliminate noise from the raw e-nose data. By comprehensively analyzing data from eight sensors, the SR-enhanced ETCN (SR-ETCN) method achieves an impressive accuracy of 94.3%, outperforming seven other comparative models that use only the response time of 7.0 seconds before the rise phase. The experimental results showcase the ETCN model's accuracy and efficiency, providing a reliable solution for mold detection in Chinese herbal medicine. This study contributes significantly to expediting the assessment of herbal medicine quality, thereby helping to ensure the safety and efficacy of traditional medicinal practices.
期刊介绍:
Journal of Medicinal Food is the only peer-reviewed journal focusing exclusively on the medicinal value and biomedical effects of food materials. International in scope, the Journal advances the knowledge of the development of new food products and dietary supplements targeted at promoting health and the prevention and treatment of disease.