by Keyword: Gc-ims
Mallafre-Muro, C, Cruz, M, Borrego, ABI, Romero, LF, Martinez, AP, Colas, SM, (2022). Study of quality controls for stability check of the ROIs of a ketones mixture in different GC-IMS measurement campaigns 2022 Ieee International Symposium On Olfaction And Electronic Nose (Isoen 2022)
GC-IMS is a very good complementary technique to traditional GC-MS, that presents some advantages, but also, some disadvantages such as misalignments produced by many parameters affecting the equipment stability. The reproducibility of the measures has been studied in two different measurement campaigns with a set of automatized quality control parameters. Figures of merit from one region of interest present in the samples show that the saturation and asymmetry do not change between measurement campaigns, but the volume and area of the total ion spectra change. A correction of these changes between batches should be developed.
JTD Keywords: Batches, Figures of merit, Gc-ims, Reproducibility, Rois
Fernandez, L, Blanco, A, Mallafre-Muro, C, Marco, S, (2022). Towards batch correction for GC-IMS data 2022 Ieee International Symposium On Olfaction And Electronic Nose (Isoen 2022)
Gas Chromatography Ion Mobility Spectrometry (GC-IMS) is a fast, non-expensive analytical technique that allows obtaining relevant chemical information from vapor mixtures. However, the technique presents some difficulties that should be solved to ensure reliable and reproducible results, namely: 1) data exhibits simultaneously high dimensionality and sparsity on their chemical information content, 2) data samples must usually be corrected even within a batch because of baseline and misalignment problems, 3) additional data corrections must be performed to prevent from chemical fingerprinting variations among batches. In this work, we have acquired data from two different batches (A and B) of ketone mixtures (2-Butanone, 2-Pentanone, 2-Hexanone, and 2-Heptanone). The analytical method for batch A and B was the same, except for the value of carrier gas flow parameter, which was approximately doubled for batch B. We have addressed problems 1) and 2) independently for each batch, obtaining as a result two peak tables. 3). Common peaks present in batches A and B were found after scaling the retention time axis of batch B and perform k-medoids clustering. Using this information, test data from batch B has been corrected through a linear transformation.
JTD Keywords: Batch correction, Batch effect, Gc-ims
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,
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, Feature extraction, Food analysis, Gc-ims, Hs-gc-ims, Pld-da, Pre-processing
Fernandez, L., Martin-Gomez, A., Mar Contreras, M., Padilla, M., Marco, S., Arce, L., (2017). Ham quality evaluation assisted by gas chromatography ion mobility spectrometry IEEE Conference Publications ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) , IEEE (Montreal, Canada) , 1-3
In recent years, Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) has been successfully employed in food science as a control technique for the prevention of fraud according to food and labeling regulations. In this work, we propose the use of GC-IMS technique to assess the quality of Iberian ham with regard to the Iberian Pig's diet (either nourished with feed or with acorns). For this purpose, we have acquired a dataset composed of 53 samples of Iberian ham from different food providers using a commercial GC-IMS (FlavourSpec, from G.A.S. Dortmund, Germany). Intensive signal pre-processing for GC-IMS was applied to the raw data. This dataset was employed to create four Partial Least Squares Discriminant Analysis (PLSDA) models corresponding to different train/test partitions of the dataset. Nearly perfect classification rates (above 91 %) were obtained for each partition of the dataset, denoting the high power of GC-IMS to characterize food samples.
JTD Keywords: Classification, Food Science, GC-IMS, Ham quality, PLSDA