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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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† Institute for Instrumental Analytics and Bioanalysis, Mannheim University of Applied Sciences, 68163 Mannheim, Germany
‡ Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, 20146 Hamburg, Germany
*Tel.: +49-(0)621 292 6484, Fax: +49-(0)621 292 6420, E-mail: [email protected]
Cite this: Anal. Chem. 2018, 90, 3, 1777–1785
Publication Date (Web):January 3, 2018
https://doi.org/10.1021/acs.analchem.7b03748
Copyright © 2018 American Chemical Society
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    Abstract

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    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 1H NMR data demonstrated that HS-GC-IMS 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.

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