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Article

Full Workflows for the Analysis of Gas Chromatography—Ion Mobility Spectrometry in Foodomics: Application to the Analysis of Iberian Ham Aroma

1
Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, 08028 Barcelona, Spain
2
Department of Electronics and Biomedical Engineering, University of Barcelona, 08028 Barcelona, Spain
3
Department of Analytical Chemistry, University of Córdoba, 14071 Córdoba, Spain
4
Integrative Pharmacology and Systems Neuroscience Research Group, IMIM-Institut Hospital del Mar d’Investigacions Mèdiques, 08003 Barcelona, Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Tomasz Marcin Dymerski
Sensors 2021, 21(18), 6156; https://doi.org/10.3390/s21186156
Received: 14 July 2021 / Revised: 8 September 2021 / Accepted: 10 September 2021 / Published: 14 September 2021
(This article belongs to the Special Issue Gas Sensors and Gas Chromatography for Analytical Applications)
Gas chromatography—ion mobility spectrometry (GC-IMS) allows the fast, reliable, and inexpensive chemical composition analysis of volatile mixtures. This sensing technology has been successfully employed in food science to determine food origin, freshness and preventing alimentary fraud. However, GC-IMS data is highly dimensional, complex, and suffers from strong non-linearities, baseline problems, misalignments, peak overlaps, long peak tails, etc., all of which must be corrected to properly extract the relevant features from samples. In this work, a pipeline for signal pre-processing, followed by four different approaches for feature extraction in GC-IMS data, is presented. More precisely, these approaches consist of extracting data features from: (1) the total area of the reactant ion peak chromatogram (RIC); (2) the full RIC response; (3) the unfolded sample matrix; and (4) the ion peak volumes. The resulting pipelines for data processing were applied to a dataset consisting of two different quality class Iberian ham samples, based on their feeding regime. The ability to infer chemical information from samples was tested by comparing the classification results obtained from partial least-squares discriminant analysis (PLS-DA) and the samples’ variable importance for projection (VIP) scores. The choice of a feature extraction strategy is a trade-off between the amount of chemical information that is preserved, and the computational effort required to generate the data models. View Full-Text
Keywords: feature extraction; food analysis; GC-IMS; PLD-DA; pre-processing feature extraction; food analysis; GC-IMS; PLD-DA; pre-processing
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MDPI and ACS Style

Freire, R.; Fernandez, L.; Mallafré-Muro, C.; Martín-Gómez, A.; Madrid-Gambin, F.; Oliveira, L.; Pardo, A.; Arce, L.; Marco, S. Full Workflows for the Analysis of Gas Chromatography—Ion Mobility Spectrometry in Foodomics: Application to the Analysis of Iberian Ham Aroma. Sensors 2021, 21, 6156. https://doi.org/10.3390/s21186156

AMA Style

Freire R, Fernandez L, Mallafré-Muro C, Martín-Gómez A, Madrid-Gambin F, Oliveira L, Pardo A, Arce L, Marco S. Full Workflows for the Analysis of Gas Chromatography—Ion Mobility Spectrometry in Foodomics: Application to the Analysis of Iberian Ham Aroma. Sensors. 2021; 21(18):6156. https://doi.org/10.3390/s21186156

Chicago/Turabian Style

Freire, Rafael, Luis Fernandez, Celia Mallafré-Muro, Andrés Martín-Gómez, Francisco Madrid-Gambin, Luciana Oliveira, Antonio Pardo, Lourdes Arce, and Santiago Marco. 2021. "Full Workflows for the Analysis of Gas Chromatography—Ion Mobility Spectrometry in Foodomics: Application to the Analysis of Iberian Ham Aroma" Sensors 21, no. 18: 6156. https://doi.org/10.3390/s21186156

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