Volatile-Compound Fingerprinting by Headspace-Gas-Chromatography Ion-Mobility Spectrometry (HS-GC-IMS) as a Benchtop Alternative to 1H NMR Profiling for Assessment of the Authenticity of Honey
Volatile-Compound Fingerprinting by Headspace-Gas-Chromatography Ion-Mobility Spectrometry (HS-GC-IMS) as a Benchtop Alternative to 1H NMR Profiling for Assessment of the Authenticity of Honey
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DOI：
10.1021/acs.analchem.7b03748
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发表时间：
2018-02-06
影响因子：
7.4
通讯作者：
Weller, Philipp
中科院分区：
文献类型：
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作者：
Gerhardt, Natalie;Birkenmeier, Markus;Weller, Philipp
这项工作描述了一种简单的方法，用于基于分辨率优化的HS-GC-IMS结合优化的化学计量学技术，即PCA，LDA和kNN，对挥发性化合物进行非靶向分析，以验证蜂蜜的植物来源。HS-GC-IMS和H-1 NMR数据之间的PCA LDA模型的直接比较表明，HS-GC-WS分析可以用作蜂蜜样品的NMR分析的补充工具。尽管NMR分析仍然需要相对精确的样品制备，特别是pH值调节，但HS-GC-IMS指纹图谱可以被认为是真正完全自动化、具有成本效益且特别是高度灵敏的方法的替代方法。结果表明，所有测试的蜂蜜样品都可以根据其植物来源进行区分。加载图揭示了负责单花蜂蜜之间的差异的挥发性化合物。基于HS-GC-IMS的PCA LDA模型由两个线性判别函数和10个选择的PC组成，其判别油菜、金合欢和蜜露蜂蜜，预测准确率为98.6%。LDA模型的应用程序的外部测试集的10个真实的蜂蜜清楚地证明了高预测能力的模型，正确地将它们分为三个品种组与100%正确的分类。所构建的模型提出了一种简单有效的分析方法，并可作为其他食品类型认证的基础。
This work describes a simple approach for the untargeted profiling of volatile compounds for the authentication of the botanical origins of honey based on resolution-optimized HS-GC-IMS combined with optimized chemometric techniques, namely PCA, LDA, and kNN. A direct comparison of the PCA LDA models between the HS-GC-IMS and H-1 NMR data demonstrated that HS-GC-WS profiling could be used as a complementary tool to NMR-based profiling of honey samples. Whereas NMR profiling still requires comparatively precise sample preparation, pH adjustment in particular, HS-GC-IMS fingerprinting may be considered an alternative approach for a truly fully automatable, cost-efficient, and in particular highly sensitive method. It was demonstrated that all tested honey samples could be distinguished on the basis of their botanical origins. Loading plots revealed the volatile compounds responsible for the differences among the monofloral honeys. The HS-GC-IMS-based PCA LDA model was composed of two linear functions of discrimination and 10 selected PCs that discriminated canola, acacia, and honeydew honeys with a predictive accuracy of 98.6%. Application of the LDA model to an external test set of 10 authentic honeys clearly proved the high predictive ability of the model by correctly classifying them into three variety groups with 100% correct classifications. The constructed model presents a simple and efficient method of analysis and may serve as a basis for the authentication of other food types.