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Different Chemometric Approaches to Detect Adulteration of Cold‐Pressed Flaxseed oil with Refined Rapeseed Oil Using Differential Scanning Calorimetry

cris.virtual.author-orcid0000-0001-7888-0026
cris.virtual.author-orcid0000-0002-6331-5726
cris.virtual.author-orcid0000-0003-3964-8093
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cris.virtualsource.author-orcida9ebcddc-25c5-49d4-b5ca-92e14baa67f8
cris.virtualsource.author-orcid6b8ff0a0-7556-4635-ae2c-064721f8c43a
cris.virtualsource.author-orcide2cdc4d2-9df9-458f-87ff-76bb71cfa6dc
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
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 use the differential scanning calorimetry (DSC) technique to detect adulterations of cold-pressed flaxseed oil with refined rapeseed oil (RP). 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 a RP concentration of 5, 10, 20, 30, 50% (w/w). Significant linear correlations (p ≤ 0.05) between the RP concentration and all DSC parameters were observed, except for h1. 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, Artificial Neural Networks, ANNs); 2) regression models (Multiple Linear Regression, MLR, MARS, SVM, ANNs, 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. These results demonstrate the usefulness of the DSC technique combined with chemometrics 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.access2025-08-27
dc.date.accessioned2025-09-15T07:38:34Z
dc.date.available2025-09-15T07:38:34Z
dc.date.copyright2023-07-20
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 use the differential scanning calorimetry (DSC) technique to detect adulterations of cold-pressed flaxseed oil with refined rapeseed oil (RP). 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 a RP concentration of 5, 10, 20, 30, 50% (w/w). Significant linear correlations (p ≤ 0.05) between the RP concentration and all DSC parameters were observed, except for h1. 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, Artificial Neural Networks, ANNs); 2) regression models (Multiple Linear Regression, MLR, MARS, SVM, ANNs, 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&amp;amp;gt; SVM &amp;amp;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&amp;amp;gt; SVM&amp;amp;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. These results demonstrate the usefulness of the DSC technique combined with chemometrics for predicting the adulteration of cold-pressed flaxseed oil with refined rapeseed oil.</jats:p>
dc.description.accesstimebefore_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.versionfinal_author
dc.identifier.doi10.20944/preprints202307.1441.v1
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/4776
dc.identifier.weblinkhttps://www.preprints.org/manuscript/202307.1441/v1
dc.languageen
dc.relation.ispartofPreprints.org
dc.relation.pagesart. 2023071441
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_REPOSITORY
dc.subject.enDSC melting profile
dc.subject.enchemometric analysis
dc.subject.enoil adulteration
dc.subject.enplant oils
dc.subject.enregression analysis
dc.subject.enclassification model
dc.subject.enartificial neural networks
dc.subtypePublicationInPreprintService
dc.titleDifferent Chemometric Approaches to Detect Adulteration of Cold‐Pressed Flaxseed oil with Refined Rapeseed Oil Using Differential Scanning Calorimetry
dc.typeJournalArticle
dspace.entity.typePublication