利用增强型深度学习技术快速检测中药材中的霉菌。

IF 1.7 3区 农林科学 Q4 CHEMISTRY, MEDICINAL
Journal of medicinal food Pub Date : 2024-08-01 Epub Date: 2024-06-26 DOI:10.1089/jmf.2024.k.0004
Ting Zhu, Xincan Wu, Ling Ma, Yadian Zeng, Junbo Lian, Jiapeng Liu, Xinnan Chen, Lei Zhong, Jingnan Chang, Guohua Hui
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引用次数: 0

摘要

霉菌污染是中药材(CHM)加工和储存过程中的一个重大挑战,会导致质量下降和药效降低。为解决这一问题,我们提出了一种快速、准确的中药霉菌检测方法,特别是利用电子鼻(e-nose)技术检测白术中的霉菌。该方法引入了偏心时间卷积网络(ETCN)模型,可有效捕捉电子鼻数据中的时间和空间信息,从而实现对 CHM 中霉菌的高效、精确检测。在我们的方法中,我们采用了随机共振(SR)技术来消除电子鼻原始数据中的噪声。通过全面分析八个传感器的数据,SR 增强型 ETCN(SR-ETCN)方法的准确率达到了惊人的 94.3%,优于其他七个仅使用上升阶段前 7.0 秒响应时间的比较模型。实验结果展示了 ETCN 模型的准确性和高效性,为中药霉菌检测提供了可靠的解决方案。这项研究为加快中药质量评估做出了重大贡献,从而有助于确保传统医学的安全性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
Journal of medicinal food
Journal of medicinal food 医学-食品科技
CiteScore
4.50
自引率
0.00%
发文量
154
审稿时长
4.5 months
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.
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