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

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.

Automated summary

The GC-IMS technology allows for fast, reliable, and inexpensive chemical composition analysis of volatile mixtures, but the data processing can be complex and requires pre-processing. This study presents four different approaches for feature extraction and applies them to a dataset of Iberian ham samples to test the ability to infer chemical information from samples. The choice of feature extraction strategy is a balance between preserving chemical information and the computational effort required to generate data models.