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Comparing different chemometric approaches to detect adulteration of cold-pressed flaxseed oil with refined rapeseed oil using differential scanning calorimetry

cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid0000-0001-7888-0026
cris.virtual.author-orcid0000-0002-6331-5726
cris.virtual.author-orcid0000-0003-3964-8093
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcida9ebcddc-25c5-49d4-b5ca-92e14baa67f8
cris.virtualsource.author-orcid6b8ff0a0-7556-4635-ae2c-064721f8c43a
cris.virtualsource.author-orcide2cdc4d2-9df9-458f-87ff-76bb71cfa6dc
dc.abstract.enFlaxseed oil is one of the best sources of n-3 fatty acids, thus its adulteration with refined oils can lead to a reduction in its nutritional value and overall quality. The purpose of this study was to compare different chemometric models to detect adulteration of flaxseed oil with refined rapeseed oil (RP) using differential scanning calorimetry (DSC). Based on the melting phase transition curve, parameters such as peak temperature (T), peak height (h), and percentage of area (P) were determined for pure and adulterated flaxseed oils with an RP concentration of 5, 10, 20, 30, and 50% (w/w). Significant linear correlations (p ≤ 0.05) between the RP concentration and all DSC parameters were observed, except for parameter h1 for the first peak. In order to assess the usefulness of the DSC technique for detecting adulterations, three chemometric approaches were compared: (1) classification models (linear discriminant analysis—LDA, adaptive regression splines—MARS, support vector machine—SVM, and artificial neural networks—ANNs); (2) regression models (multiple linear regression—MLR, MARS, SVM, ANNs, and PLS); and (3) a combined model of orthogonal partial least squares discriminant analysis (OPLS-DA). With the LDA model, the highest accuracy of 99.5% in classifying the samples, followed by ANN > SVM > MARS, was achieved. Among the regression models, the ANN model showed the highest correlation between observed and predicted values (R = 0.996), while other models showed goodness of fit as following MARS > SVM > MLR. Comparing OPLS-DA and PLS methods, higher values of R2X(cum) = 0.986 and Q2 = 0.973 were observed with the PLS model than OPLS-DA. This study demonstrates the usefulness of the DSC technique and importance of an appropriate chemometric model for predicting the adulteration of cold-pressed flaxseed oil with refined rapeseed oil.
dc.affiliationWydział Nauk o Żywności i Żywieniu
dc.affiliation.instituteKatedra Zarządzania Jakością i Bezpieczeństwem Żywności
dc.affiliation.instituteKatedra Technologii Mięsa
dc.contributor.authorIslam, Mahbuba
dc.contributor.authorKaczmarek, Anna Maria
dc.contributor.authorMontowska, Magdalena
dc.contributor.authorTomaszewska-Gras, Jolanta
dc.date.access2025-06-23
dc.date.accessioned2025-09-30T12:46:06Z
dc.date.available2025-09-30T12:46:06Z
dc.date.copyright2023-09-07
dc.date.issued2023
dc.description.abstract<jats:p>Flaxseed oil is one of the best sources of n-3 fatty acids, thus its adulteration with refined oils can lead to a reduction in its nutritional value and overall quality. The purpose of this study was to compare different chemometric models to detect adulteration of flaxseed oil with refined rapeseed oil (RP) using differential scanning calorimetry (DSC). Based on the melting phase transition curve, parameters such as peak temperature (T), peak height (h), and percentage of area (P) were determined for pure and adulterated flaxseed oils with an RP concentration of 5, 10, 20, 30, and 50% (w/w). Significant linear correlations (p ≤ 0.05) between the RP concentration and all DSC parameters were observed, except for parameter h1 for the first peak. In order to assess the usefulness of the DSC technique for detecting adulterations, three chemometric approaches were compared: (1) classification models (linear discriminant analysis—LDA, adaptive regression splines—MARS, support vector machine—SVM, and artificial neural networks—ANNs); (2) regression models (multiple linear regression—MLR, MARS, SVM, ANNs, and PLS); and (3) a combined model of orthogonal partial least squares discriminant analysis (OPLS-DA). With the LDA model, the highest accuracy of 99.5% in classifying the samples, followed by ANN &gt; SVM &gt; MARS, was achieved. Among the regression models, the ANN model showed the highest correlation between observed and predicted values (R = 0.996), while other models showed goodness of fit as following MARS &gt; SVM &gt; MLR. Comparing OPLS-DA and PLS methods, higher values of R2X(cum) = 0.986 and Q2 = 0.973 were observed with the PLS model than OPLS-DA. This study demonstrates the usefulness of the DSC technique and importance of an appropriate chemometric model for predicting the adulteration of cold-pressed flaxseed oil with refined rapeseed oil.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if4,7
dc.description.number18
dc.description.points140
dc.description.versionfinal_published
dc.description.volume12
dc.identifier.doi10.3390/foods12183352
dc.identifier.issn2304-8158
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/5096
dc.identifier.weblinkhttps://www.mdpi.com/2304-8158/12/18/3352
dc.languageen
dc.relation.ispartofFoods
dc.relation.pagesart. 3352
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.enDSC melting profile
dc.subject.enmultivariate analysis
dc.subject.enoils authenticity
dc.subject.enplant oils
dc.subject.enmultiple linear regression
dc.subject.enclassification model
dc.subject.enartificial neural networks (ANN)
dc.subject.enorthogonal partial least squares discriminant analysis (OPLS-DA)
dc.subject.enMARS
dc.subject.enSVM
dc.titleComparing different chemometric approaches to detect adulteration of cold-pressed flaxseed oil with refined rapeseed oil using differential scanning calorimetry
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue18
oaire.citation.volume12