根据GC-IMS与机器学习相结合,识别Panax quinquefolius L.不同的蒸度
Yuzhang Mi1,2, Hongjing Dong1,2, Xiao Wang1,2
1Shandong Engineering Research Center for Innovation and Application of General Technology for Separation of Natural Products, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China.
Rapid communications in mass spectrometry : RCM
|January 21, 2025
概括
蒸Panax quinquefolius L. (PQ) 改变了其挥发性有机化合物 (VOC). 机器学习有效地识别了蒸汽的学位,有助于对这种传统医学的质量控制.
科学领域:
- 植物化学 植物化学
- 分析化学 分析化学
- 计算生物学 计算生物学
背景情况:
- 松 (Panax quinquefolius L.) (PQ) 是一种传统的中国药物和食品,通过蒸加工成白色,红色和黑色品种.
- 中的挥发性有机化合物 (VOC) 具有显著的生物活性,包括抗菌,抗病毒和抗白血病的特性.
- 关于PQ的研究主要集中在人参化物上,对其VOC的调查有限.
研究的目的:
- 在蒸过程中分析PQ中的VOC的组成变化.
- 使用机器学习开发一种快速且具有成本效益的方法来识别PQ蒸汽度.
主要方法:
- 气色谱-离子运动谱法 (GC-IMS) 用于在经过不同蒸汽处理的PQ样本中分析VOC.
- 评估了六种机器学习算法,以根据已识别的VOC配置文件来预测PQ的蒸汽度.
主要成果:
- 总共有58个VOC被确定,其中20个关键化合物在蒸时表现出显著的含量变化,包括2-甲基甲,n-propanol和n-octanol.
- 纯粹贝叶斯 (NB) 和线性差异分析 (LDA) 在按蒸度分类PQ样本方面表现出强大的预测准确性.
- 该研究成功地确定了与特定蒸汽水平相关的特征性VOC.
结论:
- 蒸汽蒸发显著改变了Panax quinquefolius L.的挥发性有机化合物 (VOC) 概况.
- 机器学习模型,特别是NB和LDA,为快速准确地确定PQ蒸汽度提供了可行的方法.
- 这项研究为PQ加工提供了宝贵的见解,并为PQ产品的质量控制奠定了基础.
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