Non-targeted detection of chemical contamination in carbonated soft drinks using NMR spectroscopy, variable selection and chemometrics
Non-targeted detection of chemical contamination in carbonated soft drinks using NMR spectroscopy, variable selection and chemometrics
复制标题
DOI：
10.1016/j.aca.2008.04.050
复制
发表时间：
2008-06-23
影响因子：
6.2
通讯作者：
Godward, John
中科院分区：
文献类型：
--
作者：
Charlton, Adrian J.;Robb, Paul;Godward, John
利用 H-1 核磁共振 (NMR) 和化学计量学相结合的方法，开发出了一种检测恶意和意外食品污染的有效方法。该方法已通过市售碳酸软饮料得到证实，能够识别非典型产品并识别污染物共振。使用软独立类比建模 (SIMCA) 将正品（从制造商获得）的 1H NMR 谱与实验室添加杂质的零售产品进行比较。还评估了使用特征选择提取污染物 NMR 频率的好处。使用示例杂质（百草枯、对甲酚和草甘膦）NMR 光谱，使用多变量方法进行分析，得出对甲酚、百草枯和草甘膦的检测限分别约为 0.075、0.2 和 0.06 mM。这些检测限比百草枯的最低致死剂量低约 100 倍。这里介绍的方法用于评估复杂基质的组成是否存在污染分子，而无需事先了解潜在污染物的性质。在不求助于多个目标分析的情况下检测样本是否不符合预期特征的能力是事件检测和取证应用的宝贵工具。皇冠版权所有 (c) 2008 由 Elsevier B.V 出版。保留所有权利。
An efficient method for detecting malicious and accidental contamination of foods has been developed using a combined H-1 nuclear magnetic resonance (NMR) and chemometrics approach. The method has been demonstrated using a commercially available carbonated soft drink, as being capable of identifying atypical products and to identify contaminant resonances. Soft-independent modelling of class analogy (SIMCA) was used to compare 1H NMR profiles of genuine products (obtained from the manufacturer) against retail products spiked in the laboratory with impurities. The benefits of using feature selection for extracting contaminant NMR frequencies were also assessed. Using example impurities (paraquat, p-cresol and glyphosate) NMR spectra were analysed using multivariate methods resulting in detection limits of approximately 0.075, 0.2, and 0.06 mM for p-cresol, paraquat and glyphosate, respectively. These detection limits are shown to be approximately 100-fold lower than the minimum lethal dose for paraquat. The methodology presented here is used to assess the composition of complex matrices for the presence of contaminating molecules without a priori knowledge of the nature of potential contaminants. The ability to detect if a sample does not fit into the expected profile without recourse to multiple targeted analyses is a valuable tool for incident detection and forensic applications. Crown Copyright (c) 2008 Published by Elsevier B.V. All rights reserved.