使用高光谱成像和深度学习进行Bombyx batryticatus及其假冒品的定性识别
Boxin Li1, Shiping Zhu1, Shengling Zhou1
1College of Engineering and Technology, Southwest University, Chongqing 400716, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|February 2, 2026
概括
这项研究引入了一种非破坏性方法,使用高光谱成像和深度学习来验证药物Bombyx batryticatus的真实性. 这种方法准确地识别出正品产品,确保中国传统医药的质量.
科学领域:
- 综合医学是一个整体的医学.
- 计算生物学 计算生物学
- 分析化学 分析化学
背景情况:
- 博姆比克斯 (Bombyx batryticatus) 是东亚一个有价值的传统医药.
- 假冒的氧处理的Bombyx batryticatus威胁着药品的质量和安全.
- 对于这种重要的药物材料,需要可靠的认证方法.
研究的目的:
- 开发一种高效,非破坏性的方法,使用高光谱成像和深度学习来验证Bombyx batryticatus的真实性.
- 调查Bombyx batryticatus的地理原产地分类的可能性.
- 为中国传统药品的质量控制和市场监督提供技术支持.
主要方法:
- 通过将真色图像与通过主要组件分析提取的超谱特征相结合,创建了一个多特征的聚变数据集.
- 改进的ResNeXt50-KS深度学习模型与空间和通道协同注意 (SCSA) 和Kolmogorov-Arnold网络 (KAN) 已开发用于基于图像的分析.
- 生成对抗网络 (GAN) 用于光谱数据增强,其次是1D-CNN用于光谱特征提取.
主要成果:
- ResNeXt50-KS模型使用融合图像功能在真实性分类中实现了98.58%的准确性.
- 1D-CNN模型在使用光谱数据的真实性歧视中显示出100%的准确性.
- 1D-CNN模型在地理原产地分类中实现了95.59%的准确性.
结论:
- 超光谱成像与深度学习相结合,为Bombyx batryticatus身份验证提供了一个快速而非破坏性的解决方案.
- 开发的方法为确保药物Bombyx batryticatus的质量和安全提供了有效的技术支持.
- 这种方法可以帮助市场监督和保持传统中药的完整性.
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