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Can near-infrared spectroscopy replace a panel of tasters in sensory analysis of dry-cured bísaro loin?

2023Artigo científico Acesso aberto
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dc.contributor.authorVasconcelos, Lia
dc.contributor.authorDias, L.G.
dc.contributor.authorLeite, Ana
dc.contributor.authorFerreira, Iasmin da Silva
dc.contributor.authorPereira, Etelvina
dc.contributor.authorBona, Evandro
dc.contributor.authorMateo, Javier
dc.contributor.authorRodrigues, Sandra
dc.contributor.authorTeixeira, Alfredo
dc.date.accessioned2024-01-11T12:15:50Z
dc.date.available2024-01-11T12:15:50Z
dc.date.issued2023
dc.description.abstractThis study involved a comprehensive examination of sensory attributes in dry-cured Bísaro loins, including odor, androsterone, scatol, lean color, fat color, hardness, juiciness, chewiness, flavor intensity and flavor persistence. An analysis of 40 samples revealed a wide variation in these attributes, ensuring a robust margin for multivariate calibration purposes. The respective near-infrared (NIR) spectra unveiled distinct peaks associated with significant components, such as proteins, lipids and water. Support vector regression (SVR) models were methodically calibrated for all sensory attributes, with optimal results using multiplicative scattering correction pre-treatment, MinMax normalization and the radial base kernel (non-linear SVR model). This process involved partitioning the data into calibration (67%) and prediction (33%) subsets using the SPXY algorithm. The model parameters were optimized via a hybrid algorithm based on particle swarm optimization (PSO) to effectively minimize the root-mean-square error (RMSECV) derived from five-fold cross-validation and ensure the attainment of optimal model performance and predictive accuracy. The predictive models exhibited acceptable results, characterized by R-squared values close to 1 (0.9616–0.9955) and low RMSE values (0.0400–0.1031). The prediction set’s relative standard deviation (RSD) remained under 5%. Comparisons with prior research revealed significant improvements in prediction accuracy, particularly when considering attributes like pig meat aroma, hardness, fat color and flavor intensity. This research underscores the potential of advanced analytical techniques to improve the precision of sensory evaluations in food quality assessment. Such advancements have the potential to benefit both the research community and the meat industry by closely aligning their practices with consumer preferences and expectations.pt_PT
dc.description.sponsorshipThis research was funded by “BisOlive: Use of olive pomace in the feeding of Bísaro swine. Evaluation of the effect on meat quality” project. NORTE-01-0247-FEDER-072234. Financial support under the CIMO project (UIDB/00690/2020).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationVasconcelos, Lia Inês Machado; Dias, L.G.; Leite, Ana; Ferreira, Iasmin da Silva; Pereira, Etelvina; Bona, Evandro; Mateo, Javier; Rodrigues, Sandra; Teixeira, Alfredo (2023). Can near-infrared spectroscopy replace a panel of tasters in sensory analysis of dry-cured bísaro loin?. Foods. eISSN 2304-8158. 12:23, p. 1-15pt_PT
dc.identifier.doi10.3390/foods12234335pt_PT
dc.identifier.eissn2304-8158
dc.identifier.urihttp://hdl.handle.net/10198/29171
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationMountain Research Center
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectConsumerspt_PT
dc.subjectMeat productspt_PT
dc.subjectBísaro breedpt_PT
dc.subjectFood quality assessmentpt_PT
dc.subjectNIR analysispt_PT
dc.subjectNon-linear SVR modelspt_PT
dc.titleCan near-infrared spectroscopy replace a panel of tasters in sensory analysis of dry-cured bísaro loin?pt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleMountain Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00690%2F2020/PT
oaire.citation.endPage15pt_PT
oaire.citation.issue23pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleFoodspt_PT
oaire.citation.volume12pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameVasconcelos
person.familyNameDias
person.familyNameLeite
person.familyNameFerreira
person.familyNameRodrigues
person.familyNameTeixeira
person.givenNameLia
person.givenNameLuís G.
person.givenNameAna
person.givenNameIasmin da Silva
person.givenNameSandra
person.givenNameAlfredo
person.identifier2638674
person.identifier958487
person.identifier.ciencia-id2015-35EF-B2C9
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person.identifier.ciencia-id651F-D964-32E1
person.identifier.ciencia-id2A1A-FF0C-185B
person.identifier.orcid0009-0003-1035-5622
person.identifier.orcid0000-0002-1210-4259
person.identifier.orcid0000-0002-4480-724X
person.identifier.orcid0000-0003-0162-7182
person.identifier.orcid0000-0003-3301-1729
person.identifier.orcid0000-0003-4607-4796
person.identifier.ridC-6486-2008
person.identifier.ridG-4118-2011
person.identifier.scopus-author-id57224659068
person.identifier.scopus-author-id23569169900
person.identifier.scopus-author-id14048942500
person.identifier.scopus-author-id56195849200
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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