ATR-FTIR光谱学与深度学习相结合,用于识别和定量检测Panax notoginseng造物
Jiawen Liang1, Li Wang2, Dandan Kong1
1Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|November 6, 2025
概括
这项研究引入了一种使用ATR-FTIR光谱学和深度学习来检测Panax notoginseng中 adulterants的快速,负担得起的方法. 先进的模型准确地识别和量化常见的 adulterants,确保产品的安全.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 帕纳克斯诺托金森 (Panax notoginseng) 的价值很高,但经常被改,造成健康风险.
- 目前的检测方法对于大规模查来说是低效和昂贵的.
- 需要快速,经济高效地检测药品材料中的伪造.
研究的目的:
- 开发一种快速,具有成本效益的方法来检测Panax notoginseng的改.
- 整合减弱总反射 - 里埃变换红外光谱 (ATR-FTIR) 与深度学习.
- 识别和量化伪造物,如纤维根粉,Curcumae根茎和大米粉.
主要方法:
- 使用ATR-FTIR光谱仪获取光谱数据.
- 应用深度学习模型,包括卷积神经网络 (CNN) 和变压器.
- 评估了模型性能,用于识别和预测伪造剂度的四类回归.
主要成果:
- 美国有线电视新闻 (CNN) 和变压器 (Transformer) 实现了很高的识别准确率 (97.77%和98.66%).
- 回归模型显示出出色的性能 (R2值0.93870.9848) 伪造剂量化.
- 模型准确地预测了伪造样本中主要活性成分的含量 (R2 0.94450.9726).
结论:
- 与深度学习相结合的ATR-FTIR光谱学是检测伪造的强大工具.
- 开发的方法为质量控制提供了快速和经济有效的解决方案.
- 这种方法提高了药用材料市场的消费者健康和食品安全.
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