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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1486

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contributor.authorKundu, M-
contributor.authorNaga, C K-
date.accessioned2011-07-12T06:17:58Z-
date.available2011-07-12T06:17:58Z-
date.issued2010-09-
identifier.citationInternational Electronic Engineering Mathematical Society, Volume (5), September 2010, pp. 1-8en
identifier.issn1687-787X-
identifier.urihttp://hdl.handle.net/2080/1486-
descriptionCopyright belongs to IEEMSen
description.abstractMonitoring of a product quality & controlling it for ensuring certain standards using multivariate statistics has become almost a norm in food and beverage industry. A pattern recognition tool, principal component analysis (PCA) was applied to discriminate reliably among 178 samples of wine possessing 13 number of feature variables. K-means clustering, a supervised clustering technique was used to designate the classes available among the wine samples with the help of first two principal components. Hierarchical clustering technique was also applied to classify them with a mention of their classification level in the produced dendrograms. A classifier was developed using probabilistic neural networks (PNN) which can help in online process motoring.en
format.extent225350 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherIEEMSen
subjectK-meansen
subjectStatistical Quality Controlen
titleApplication of probabilistic neural network for Wine classificationen
typeArticleen
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