使用E-nose,HS-GC-IMS和智能数据融合方法对Amomi fructus进行质量识别
Pan-Pan Zhang1, Xin-Jing Gui2,3,4, Xue-Hua Fan1
1School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China.
Frontiers in chemistry
|February 21, 2025
概括
这项研究使用电子鼻子和HS-GC-IMS来分析Amomi fructus中的挥发性化合物,成功地以高精度识别了真实性和原产地. 数据融合改善了识别,提供了快速质量评估方法.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 食品科学 食品科学 食品科学
背景情况:
- 阿莫米果实 (AF) 被广泛使用,但由于改和来源混合,面临质量问题.
- 确保AF的真实性和来源对于其药物和食品应用至关重要.
研究的目的:
- 开发和验证一种快速的方法来识别Amomi fructus的真实性,来源和来源.
- 用E-nose和HS-GC-IMS分析AF中的挥发性有机化合物 (VOC) 和其假冒物.
主要方法:
- 利用电子鼻子 (EN) 和头空气气色谱-离子移动性光谱法 (HS-GC-IMS) 来检测和分析VOC.
- 使用化学计量方法,包括PCA,PCA-DA,PLS-DA和OPLS-DA用于数据分析.
- 实现了EN和HS-GC-IMS数据的数据层融合,以提高识别准确度.
主要成果:
- 确定了111种挥发性有机物,其中47种作为真实AF和假冒的差异标记.
- 在使用PCA的真实性识别中实现了100%的准确性.
- 原产地和来源识别模型的准确率分别为95.65%和98.18%,数据融合将原产地识别提高到97.96%.
结论:
- 电子鼻子和HS-GC-IMS的智能数据融合为评估Amomi fructus质量提供了快速而准确的方法.
- 开发的方法有效地区分了AF的真实性,起源和来源.
- 这种方法为阿莫米果实市场的质量控制提供了可靠的解决方案.
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