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

Rafael Freire,Celia Mallafré-Muro,Luciana Oliveira,Andrés Martín-Gómez,Francisco Madrid-Gambin,Luis Fernandez,Antonio Pardo,Lourdes Arce,Santiago Marco

doi:10.3390/s21186156

Abstract

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.

Highlights

One of the leading indicators of food quality and freshness is aroma [1]
In method 4, we consider the sparsity of the chemical information in the raw Gas chromatography—ion mobility spectrometry (GC-IMS) data, and we focus on ion peak intensities
It is possible to has corrected thethe imperfections of to observe observethat thatthe thedata datapre-processing pre-processingpipeline pipeline has corrected imperfections thethe raw data, removing the baseline of raw data, removing the baselineand andpeak peaktailing tailingeffects effectsthat thatusually usuallyappear appear in in IMS

Summary

Introduction

One of the leading indicators of food quality and freshness is aroma [1]. The analysis of volatile constituents in food products may be carried out by two different main approaches, sensory and instrumental analyses. Sensory analysis is the evaluation of flavor and aroma by trained experts to identify and classify a pre-set of characteristics [10]. Instrumental analysis, refers to determining the molecules behind an aroma, using one or more chemical techniques and detectors [11,12]. The use of instrumental analyses leads to less subjective aroma evaluation with more accuracy, and richly informative techniques that elucidate the chemical features of the aroma. The field is not without its difficulties and, on many occasions, the conclusions are problem-dependent [13,14,15,16,17,18]

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