Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jfoodeng.2008.06.017
DC FieldValue
dc.titlePartial correlation metric based classifier for food product characterization
dc.contributor.authorMelissa, A.S.
dc.contributor.authorRaghuraj, K.R.
dc.contributor.authorLakshminarayanan, S.
dc.date.accessioned2014-10-09T06:57:03Z
dc.date.available2014-10-09T06:57:03Z
dc.date.issued2009-01
dc.identifier.citationMelissa, A.S., Raghuraj, K.R., Lakshminarayanan, S. (2009-01). Partial correlation metric based classifier for food product characterization. Journal of Food Engineering 90 (2) : 146-152. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jfoodeng.2008.06.017
dc.identifier.issn02608774
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/89723
dc.description.abstractData classification algorithms applied to multidimensional and multiclass food characterization problems mainly assume feature independency to quantify intra-class similarity or inter-class dissimilarities. As an alternative, possible class specific inter-relations among the feature vectors can be exploited for distinguishing samples into specific classes. Based on this idea, a new partial correlation coefficient metric (PCCM) based classification method is proposed. Existence of such inter-variable correlations as signatures of unique classes is established with illustrative problems. Categorized variable dependency structures are hypothesized as the basis for class discrimination. Two food quality analysis datasets with chemometrics importance are utilized as benchmark problems to compare the performance of new method with classification algorithms like LDA (linear discriminant analysis), CART, Treenet and SVM (support vector machines). The PCCM method is observed to perform well for different tests over large sets of classification experiments. Discriminating PCCM classifier also provides a quick visualization tool to diagnose complex classification problems. © 2008 Elsevier Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.jfoodeng.2008.06.017
dc.sourceScopus
dc.subjectDiscriminant analysis
dc.subjectFood products
dc.subjectPartial correlations
dc.subjectQuality classification
dc.typeArticle
dc.contributor.departmentCIVIL ENGINEERING
dc.contributor.departmentCHEMICAL & BIOMOLECULAR ENGINEERING
dc.description.doi10.1016/j.jfoodeng.2008.06.017
dc.description.sourcetitleJournal of Food Engineering
dc.description.volume90
dc.description.issue2
dc.description.page146-152
dc.description.codenJFOED
dc.identifier.isiut000259836900002
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