Please use this identifier to cite or link to this item:
Title: Partial correlation metric based classifier for food product characterization
Authors: Melissa, A.S.
Raghuraj, K.R. 
Lakshminarayanan, S. 
Keywords: Discriminant analysis
Food products
Partial correlations
Quality classification
Issue Date: Jan-2009
Citation: Melissa, 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.
Abstract: Data 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.
Source Title: Journal of Food Engineering
ISSN: 02608774
DOI: 10.1016/j.jfoodeng.2008.06.017
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Sep 22, 2020


checked on Sep 15, 2020

Page view(s)

checked on Sep 20, 2020

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.