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通过HS-GC-IMS对来自不同来源的沙夫兰进行表征,以及与深度学习相结合的真实性识别

Yingjie Lu¹, Chi Zhang^1,2, Kunmiao Feng¹

¹School of Pharmacy, Naval Medical University, Shanghai 200433, China.

Food chemistry: X

|January 24, 2025

概括

结合头部气体染色学-离子流动性光谱学 (HS-GC-IMS) 和卷积神经网络 (CNN) 的新方法可以准确识别香的来源并检测假冒产品. 这种快速的技术确保了沙夫兰在市场上的质量和真实性.

科学领域:

分析化学分析化学
食品科学食品科学食品科学
化学信息学化学信息学

背景情况:

沙夫兰的质量控制需要可靠的方法来确认原产地和检测改.
目前用于快速验证藏红花真实性的策略不足以满足市场需求.

研究的目的:

开发一个快速可靠的分析策略,以确定沙夫兰的原产地和检测伪造.
建立一个卷积神经网络 (CNN) 模型,用于分析头空间-气体染色学-离子运动谱学 (HS-GC-IMS) 数据.

主要方法:

使用头部气体染色学-离子移动性光谱学 (HS-GC-IMS) 来检测沙夫兰中的挥发性化合物 (VOC).
开发了一个卷积神经网络 (CNN) 模型,用于直接分析GC-IMS数据,从而实现自动特征提取.
将GC-IMS图像直接输入到CNN模型中,数据预处理最小.

主要成果:

检测到69个挥发性化合物,包括7个同位素组,快速和直接.
通过使用CNN模型来预测沙夫兰起源的平均准确率约为90%.
在识别假冒沙夫兰方面表现出高准确度 (98%),超过了PCA (61%) 和SVM (71%) 等传统方法.

结论:

结合HS-GC-IMS和CNN方法,提供了一种快速,可靠和准确的法子来验证沙夫兰身份.
这种技术有效地验证了沙夫兰的原产地,并检测了假冒产品,确保了市场质量.
开发的CNN模型与传统的分析方法相比,提供了优越的性能.

关键词:

卷积神经网络是一种卷积神经网络.在HS-GC-IMS中使用.源头的起源源头的起源撒法伦是什么意思撒法伦是什么意思挥发性元件是挥发性的元件.

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