与机器学习相结合的ATR-FTIR提供了一种快速的方法来识别和区分55种水果制药材料
Wen-Jie Zhao1,2, Ya-Ling An2, Chun-Qian Song2
1School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China.
Phytochemical analysis : PCA
|May 29, 2025
概括
准确识别水果制药材料至关重要. 减弱总反射-里埃变换红外光谱学 (ATR-FTIR) 结合机器学习 (ML),特别是K-最近邻居 (KNN) 模型,有效地区分了55种这些材料.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
- 药理学是指药理学,即药理学是指药理学.
背景情况:
- 水果制药材料 (FDMM) 广泛使用,但存在品种混,影响消费者安全和权利.
- 准确识别FDMM对于质量控制,疗效和安全保证至关重要.
研究的目的:
- 开发一种方法来区分和识别55种FDMM.
- 为此目的,将减弱总反射 - 里埃变换红外光谱学 (ATR-FTIR) 与机器学习 (ML) 技术相结合.
主要方法:
- 收集了861个FDMM样品批次用于模型开发和验证.
- 构建了10个分类模型,包括K-最近邻居 (KNN),支持向量机 (SVM) 和神经网络.
- 根据准确性,精度,回忆和F1分数评估模型,选择最佳模型进行验证.
主要成果:
- K-最近邻居 (KNN) 模型表现出卓越的性能,所有评估指标都超过0.98.
- 在140个样本的独立集合上,KNN模型实现了85.7%的预测准确度.
- 综合ATR-FTIR和ML方法在区分大多数FDMM方面被证明是有效的.
结论:
- 减弱总反射-里埃变换红外光谱学 (ATR-FTIR) 与机器学习 (ML) 结合,可以快速准确地识别FDMM.
- 这种方法有助于确保水果制药材料的质量和安全.
- 开发的模型为解决FDMM市场的品种混提供了强大的解决方案.
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