Using portable visible and near-infrared spectroscopy to authenticate beef from grass, barley, and corn-fed cattle

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dc.contributor.authorLeón Ecay, Sara
dc.contributor.authorLópez-Campos, Óscar
dc.contributor.authorLópez Maestresalas, Ainara
dc.contributor.authorInsausti Barrenetxea, Kizkitza
dc.contributor.authorSchmidt, Bryden
dc.contributor.authorPrieto, Nuria
dc.contributor.departmentAgronomía, Biotecnología y Alimentaciónes_ES
dc.contributor.departmentAgronomia, Bioteknologia eta Elikaduraeu
dc.contributor.departmentIngenieríaes_ES
dc.contributor.departmentIngeniaritzaeu
dc.contributor.departmentInstitute on Innovation and Sustainable Development in Food Chain - ISFOODen
dc.contributor.funderUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
dc.contributor.funderGobierno de Navarra / Nafarroako Gobernua
dc.date.accessioned2024-11-21T13:04:43Z
dc.date.issued2024-12-01
dc.date.updated2024-11-21T12:56:49Z
dc.description.abstractMeat product labels including information on livestock production systems are increasingly demanded, as consumers request total traceability of the products. The aim of this study was to explore the potential of visible and near-infrared spectroscopy (Vis-NIRS) to authenticate meat and fat from steers raised under different feeding systems (barley, corn, grass-fed). In total, spectra from 45 steers were collected (380-2,500 nm) on the subcutaneous fat and intact longissimus thoracis (LT) at 72 h postmortem and, after fabrication, on the frozen-thawed ground longissimus lumborum (LL). In subcutaneous fat samples, excellent results were obtained using partial least squares-discriminant analysis (PLS-DA) with the 100 % of the samples in external Test correctly classified (Vis, NIR or Vis-NIR regions); whereas linear-support vector machine (L-SVM) discriminated 75-100 % in Test (Vis-NIR range). In intact meat samples, PLS-DA segregated 100 % of the samples in Test (Vis-NIR region). A slightly lower percentage of meat samples were correctly classified by L-SVM using the NIR region (75-100 % in Train and Test). For ground meat, 100 % of correctly classified samples in Test was achieved using Vis, NIR or Vis-NIR spectral regions with PLS-DA and the Vis with L-SVM. Variable importance in projection (VIP) reported the influence of fat and meat pigments as well as fat, fatty acids, protein, and moisture absorption for the discriminant analyses. From the results obtained with the animals and diets used in this study, NIRS technology stands out as a reliable and green analytical tool to authenticate fat and meat from different livestock production systems.en
dc.description.sponsorshipThis research was supported by funding from the Agriculture and Agri-Food Canada (A-base J-002504). Sara León-Ecay would like to thank Universidad Pública de Navarra (Predoctoral scholarship Res.2178/2022 and mobility grant Res.1027/2024) and Government of Navarra (Mobility grant nº 0011-3564-2024-000012) for the financial support.
dc.embargo.lift2025-12-01
dc.embargo.terms2025-12-01
dc.format.mimetypeapplication/pdfen
dc.identifier.citationLeón-Ecay, S., López-Campos, Ó., López-Maestresalas, A., Insausti, K., Schmidt, B., Prieto, N. (2024). Using portable visible and near-infrared spectroscopy to authenticate beef from grass, barley, and corn-fed cattle. Food Research International, 198, 1-11. https://doi.org/10.1016/j.foodres.2024.115327.
dc.identifier.doi10.1016/j.foodres.2024.115327
dc.identifier.issn0963-9969
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/52557
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofFood Research International (2024), vol. 198, 115327
dc.relation.projectIDinfo:eu-repo/grantAgreement/Gobierno de Navarra//0011-3564-2024-000012/
dc.relation.publisherversionhttps://doi.org/10.1016/j.foodres.2024.115327
dc.rights© 2024 Published by Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectGrass-feden
dc.subjectGrain-feden
dc.subjectMeat authenticationen
dc.subjectVis-NIRSen
dc.subjectMachine-learningen
dc.titleUsing portable visible and near-infrared spectroscopy to authenticate beef from grass, barley, and corn-fed cattleen
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePublication
relation.isAuthorOfPublication8f62db26-219d-4520-b17f-d0d47ca60c29
relation.isAuthorOfPublicationbc607da1-a1ab-4216-be92-08409b033643
relation.isAuthorOfPublicationd73299bb-63e6-4023-a134-8742f3913a0a
relation.isAuthorOfPublication.latestForDiscovery8f62db26-219d-4520-b17f-d0d47ca60c29

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