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机器学习辅助的HS-GC-IMS用于基于挥发性指纹的婴儿瓶的歧视和可追溯性

Junchao Ma¹, Yinghua Qi¹, Dan Zhang¹

¹Characteristic Laboratory of Forensic Science in Universities of Shandong Province, Shandong University of Political Science and Law, Jinan 250014, Shandong Province, China.

Journal of chromatography. A

|February 26, 2026

概括

这项研究开发了一种使用头空气色谱-离子移动性光谱 (HS-GC-IMS) 和机器学习的方法,用于识别婴儿瓶中的挥发性有机化合物 (VOC),以确保品牌的可追溯性和安全性. 该方法有效地区分不同的婴儿瓶材料和品牌.

科学领域:

分析化学分析化学
食品安全食品安全
化学测量化学测量化学测量

背景情况:

接触食品的材料,特别是婴儿奶瓶,需要对婴儿健康进行严格的安全评估.
挥发性有机化合物 (VOC) 可以表明材料成分和潜在的安全问题.

研究的目的:

开发一个全面的分析策略,以分析婴儿奶瓶中的VOC.
通过使用先进的分析技术,实现婴儿瓶材料和品牌的歧视和可追溯性.
确保与食品接触材料的安全性和真实性.

主要方法:

集成头部气体染色体-离子移动性光谱 (HS-GC-IMS) 用于挥发性有机化合物分析.
基于机器学习的化学测量的应用,包括无监督 (PCA,HCA) 和监督 (OPLS-DA,DT,RF,SVM) 的模型,以及深度学习 (CNN,变压器).
使用预测中变量重要性 (VIP) 评分和随机森林特征重要性来识别关键的歧视性标记.

主要成果:

87种挥发性有机化合物的鉴定,其中化物,,醇和是最多样和最丰富的.
在无监督模型 (PCA,HCA) 中观察到明显的集群模式.
随机森林 (RF) 模型证明了品牌分类的最佳性能,在识别的歧视性标记器上实现了高效率.

结论:

开发的分析框架为快速识别和跟踪婴儿奶瓶提供了有效和可靠的方法.
这种方法为防伪和食品接触材料的安全保证提供了宝贵的见解.
该研究强调了将HS-GC-IMS与机器学习相结合的潜力,用于材料认证和质量控制.

关键词:

婴儿奶瓶婴儿奶瓶的使用情况与食品接触的材料在HS-GC-IMS中使用.基于机器学习的化学测量.可追溯性可追溯性

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