Full workflows for the analysis of gas chromatography-ion mobility spectrometry in foodomics: application to the analysis of iberian ham aroma

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  • dc.contributor.author Freire, Rafael
  • dc.contributor.author Fernández, Luis
  • dc.contributor.author Mallafré-Muro, Celia
  • dc.contributor.author Martín-Gómez, Andrés
  • dc.contributor.author Madrid-Gambin, Francisco
  • dc.contributor.author Oliveira, Luciana
  • dc.contributor.author Pardo, Antonio
  • dc.contributor.author Arce, Lourdes
  • dc.contributor.author Marco, Santiago
  • dc.date.accessioned 2022-07-12T06:18:31Z
  • dc.date.available 2022-07-12T06:18:31Z
  • dc.date.issued 2021
  • 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.description.sponsorship This work is part of the BEST Postdoctoral Program, funded by the European Commission under Horizon 2020 Marie Skłodowska-Curie Actions COFUND scheme (Grant Agreement no. 712754) and by the Severo Ochoa program of the Spanish Ministry of Science and Competitiveness (Grant SEV-2014-0425 (2015–2019)). We would like to acknowledge, the Departament d’Universitats, Recerca i Societat de la Informació de la Generalitat de Catalunya (expedient 2017 SGR 1721); the Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya; and the European Social Fund (ESF). Additional financial support has been provided by the Institut de Bioenginyeria de Catalunya (IBEC). IBEC is a member of the CERCA Programme/Generalitat de Catalunya. This work has been additionally funded by Spanish MINECO Project TENSOMICS (RTI2018-098577-B-C22) and by the Consejería de Economía, Conocimiento, Empresas y Universidad de la Junta de Andalucía (Programa Operativo FEDER Andalucía 2014–2020. Convocatoria 2018. 1261925-R).
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Freire R, Fernandez L, Mallafré-Muro C, Martín-Gómez A, Madrid-Gambin F, Oliveira L, et al. Full workflows for the analysis of gas chromatography-ion mobility spectrometry in foodomics: application to the analysis of iberian ham aroma. Sensors (Basel). 2021 Sep 14; 21(18): 6156. DOI: 10.3390/s21186156
  • dc.identifier.doi http://dx.doi.org/10.3390/s21186156
  • dc.identifier.issn 1424-8220
  • dc.identifier.uri http://hdl.handle.net/10230/53718
  • dc.language.iso eng
  • dc.publisher MDPI
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/712754
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/RTI2018-098577-B-C22
  • dc.rights © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.rights.uri http://creativecommons.org/licenses/by/4.0/
  • dc.subject.keyword GC-IMS
  • dc.subject.keyword PLD-DA
  • dc.subject.keyword Feature extraction
  • dc.subject.keyword Food analysis
  • dc.subject.keyword Pre-processing
  • 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.version info:eu-repo/semantics/publishedVersion

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