Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/180312
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Freire, Rafael | - |
dc.contributor.author | Fernández Romero, Luis | - |
dc.contributor.author | Mallafre Muro, Celia | - |
dc.contributor.author | Martín Gómez, Andrés | - |
dc.contributor.author | Madrid Gambín, Francisco Javier | - |
dc.contributor.author | Oliveira, Luciana | - |
dc.contributor.author | Pardo Martínez, Antonio | - |
dc.contributor.author | Arce, Lourdes | - |
dc.contributor.author | Marco Colás, Santiago | - |
dc.date.accessioned | 2021-09-28T16:49:30Z | - |
dc.date.available | 2021-09-28T16:49:30Z | - |
dc.date.issued | 2021-09-14 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | http://hdl.handle.net/2445/180312 | - |
dc.description.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. | - |
dc.format.extent | 19 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | - |
dc.publisher | MDPI | - |
dc.relation.isformatof | Reproducció del document publicat a: https://doi.org/10.3390/s21186156 | - |
dc.relation.ispartof | Sensors, 2021, vol. 21, num. 18, p. 6156-6174 | - |
dc.relation.uri | https://doi.org/10.3390/s21186156 | - |
dc.rights | cc-by (c) Freire, Rafael et al., 2021 | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.source | Articles publicats en revistes (Enginyeria Electrònica i Biomèdica) | - |
dc.subject.classification | Química dels aliments | - |
dc.subject.classification | Cromatografia de gasos | - |
dc.subject.classification | Pernil | - |
dc.subject.other | Food composition | - |
dc.subject.other | Gas chromatography | - |
dc.subject.other | Ham | - |
dc.title | Full Workflows for the Analysis of Gas Chromatography-Ion Mobility Spectrometry in Foodomics: Application to the Analysis of Iberian Ham Aroma | - |
dc.type | info:eu-repo/semantics/article | - |
dc.type | info:eu-repo/semantics/publishedVersion | - |
dc.identifier.idgrec | 714081 | - |
dc.date.updated | 2021-09-28T16:49:30Z | - |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/712754/EU//BEST | - |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | - |
dc.identifier.pmid | 34577363 | - |
Appears in Collections: | Articles publicats en revistes (Enginyeria Electrònica i Biomèdica) Articles publicats en revistes (Institut de Bioenginyeria de Catalunya (IBEC)) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
714081.pdf | 12.19 MB | Adobe PDF | View/Open |
This item is licensed under a
Creative Commons License