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DMC-LIBSAS:一种激光诱导的分解光谱分析系统,具有双重多重卷积神经网络,用于准确追溯中国药物材料

Tianhe Huang¹, Wenhao Bi¹, Yuxiao Song²

¹School of Medical Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, China.

Sensors (Basel, Switzerland)

|April 12, 2025

概括

一个结合激光诱导分解光谱 (LIBS) 和深度学习的新系统准确地追踪了Angelica dahurica的起源. 这种方法增强了传统中医药的质量控制,防止假冒.

科学领域:

分析化学分析化学
频谱学是一种光谱学.
计算科学计算科学

背景情况:

全球化增加了中国传统医药材料中伪造和错误识别的风险.
准确的来源追踪对于确保草药的质量,安全性和有效性至关重要.
传统中医药材料需要强大的原产地验证方法.

研究的目的:

开发一种新的系统,用于准确追踪中国传统医药材料的来源.
将激光诱导分解光谱 (LIBS) 与深度学习相结合,以提高分析能力.
为了验证拟议的系统在确定*Angelica dahurica*的地理来源方面的有效性.

主要方法:

开发一个双重多重卷积神经网络LIBS分析系统 (DMC-LIBSAS).
使用LIBS进行药物材料的快速,非破坏性元素分析.
使用深度学习 (特别是DMCNN) 来提取和分类LIBS光谱数据的特征.
使用1D梯度加权类激活映射 (1D-Grad-CAM) 演示DMCNN的特征提取能力.

主要成果:

对于 *Angelica dahurica* 原产地,DMC-LIBSAS 实现了 95.25% 的高追踪精度.
拟议的系统显著优于六种经典的机器学习和深度学习方法 (LeNet,AlexNet,Resnet18,KNN,RF,DT).
DMCNN有效地提取了特征的光谱峰值,使得准确的原产地歧视.

结论:

DMC-LIBSAS提供了一个有效和准确的解决方案,用于追踪Angelica dahurica的起源.
这种方法为中国传统医药材料的质量监督和认证提供了新的技术支持.
将LIBS与先进的深度学习模型相结合,代表了草药分析的一个有前途的方向.

关键词:

在美国,CNN是CNN.在LIBS中,LIBS是指LIBS.这是TCM分类的分类.原产地标识来源标识定性分析是一种定性分析.

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