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.

Usage considerations for headspace-gas chromatography-ion mobility spectrometry as a suitable technique for qualitative analysis in a routine lab.

The potential of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) to perform non-targeted qualitative analysis of complex samples has led to an unprecedented increase in its popularity in recent years. The operating principle of IMS makes quality control essential to ensure adequate results. Besides this, the suitability of GC-IMS is determined by multiple phenomena that take place before and during IMS detection. The present work discusses a novel GC-IMS quality control protocol for both beginners and experienced users. Likewise, it describes factors that must be taken into account in order to develop a robust GC-IMS qualitative analysis method and, if needed, to achieve the identification of VOCs present in real samples. The developed quality control protocol was successfully employed in our laboratory for the routine analysis of >500 real samples (olive oil and Iberian ham) for 6 months, thus it is recommended for the analysis of a great number of complex samples. Furthermore, the behaviour of the ions produced in the ionisation chamber and the possible reactions between them in GC-IMS qualitative analysis were assessed.

Target vs spectral fingerprint data analysis of Iberian ham samples for avoiding labelling fraud using headspace - gas chromatography-ion mobility spectrometry.

The data obtained with a polar or non-polar gas chromatography (GC) column coupled to ion mobility spectrometry (IMS) has been explored to classify Iberian ham, to detect possible frauds in their labelling. GC-IMS was used to detect the volatile compound profile of dry-cured Iberian ham from pigs fattened on acorn and pasture or on feed. Due to the two-dimensional nature of GC-IMS measurements, great quantities of data are obtained and an exhaustive chemometric processing is required. A first approach was based on the processing of the complete spectral fingerprint, while the second consisted of the selection of individual markers that appeared throughout the spectra. A classification rate of 90% was obtained with the first strategy, and the second approach correctly classified all Iberian ham samples according to the pigs' diet (classification rate of 100%). No significant differences were found between the GC columns tested in terms of classification rate.