by Keyword: Odorants
Freire, R, Fernandez, L, Mallafré-Muro, C, Martín-Gómez, A, Madrid-Gambin, F, Oliveira, L, Pardo, A, Arce, L, Marco, S, (2021). Full workflows for the analysis of gas chromatography—ion mobility spectrometry in foodomics: Application to the analysis of iberian ham aroma Sensors 21, 6156 
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.
JTD Keywords: authenticity, classification, electronic-nose, feature extraction, food analysis, gc-ims, headspace, least-squares, models, pld-da, pre-processing, quality, sensory analysis, wine, Discriminant analysis, Feature extraction, Food analysis, Gas chromatography-mass spectrometry, Gc-ims, Hs-gc-ims, Ion mobility spectrometry, Odorants, Pld-da, Pre-processing, Workflow
Auffarth, B., Gutierrez-Galvez, A., Marco, S., (2010). Relevance and LOCI of odorant features in the rat olfactory bulb: Statistical methods for understanding olfactory codes in glomerular images BIOSIGNALS 2010 - Proceedings of the 3rd International Conference on Bio-inpsired Systems and Signal Processing, Proceedings 
3rd International Conference on Bio-inspired Systems and Signal Processing, BIOSIGNALS 2010 (ed. Fred, A., Filipe, J., Gamboa, H.), Springer-Verlag (Valencia, Spain) , 37-44
The relationship between physicochemical properties of odor molecules and perceived odor quality is arguably one of the most important issues in olfaction and the rules governing this relationship remain unknown. Any given odor molecule will stimulate more than one type of receptor in the nose, perhaps hundreds, and this stimulation reflects itself in the neural code of the olfactory nervous system. We present a method to investigate neural coding at the glomerular level of the olfactory bulb, the first relay for olfactory processing in the brain. Our results give insights into localization of coding sites, relevance of odorant properties for information processing, and the size of coding zones.
JTD Keywords: Classification, Glomeruli, Non-parametric statistics, Odorants, Olfactory bulb, Olfactory coding, Property-activity relationship
