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Application of several statistical classification techniques to the differentiation of whisky brands
Author(s) -
MartinAlvarez Pedro J,
Cabezudo María Dolores,
Sanz Jesus,
Herranz Alberto,
De La Serna Pilar,
Barro Carmen
Publication year - 1988
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.2740450409
Subject(s) - principal component analysis , linear discriminant analysis , statistical analysis , pattern recognition (psychology) , artificial intelligence , computer science , nearest neighbour , mathematics , statistics
Several supervised (stepwise discriminant analysis (SDA), statistical isolinear multi category analysis (SIMCA) and nearest neighbour analysis (KNN)) and unsupervised (principal components analysis (PCA) and cluster analysis (CA)) classification techniques have been applied to analytical data from different whisky samples in order to distinguish between genuine whisky of a well known and expensive brand and other less expensive whiskies that could be used to replace the original contents of the bottle.