Real-world systems are usually multivariate and hence usually cannot be adequately described by one selected variable without the risk of serious misrepresentation. Analyzing the effect of one variable at a time by analysis of variance techniques can give useful descriptive information, but this will not give specific information about relationships among variables and other important relationships in the entire matrix. Multivariate data analysis was developed in the late 1960s, and used by a number of research groups in analytical and physical organic chemistry due to the introduction of instrumentation giving multivariate responses for each sample analyzed. Development of such methods was also made possible by the availability of computers. Multivariate data analysis involves the use of mathematical and statistical techniques to extract information from complex data sets. The objective of this paper is to briefly describe and illustrate some multivariate data analysis methods used for grape and wine analysis. © 2009 Cozzolino et al, publisher and licensee Dove Medical Press Ltd.

Item Type:	Refereed Article
Research Division:	Agricultural, Veterinary and Food Sciences
Research Group:	Horticultural production
Research Field:	Oenology and viticulture
Objective Division:	Plant Production and Plant Primary Products
Objective Group:	Industrial crops
Objective Field:	Wine grapes
UTAS Author:	Dambergs, RG (Dr Robert Dambergs)
ID Code:	56241
Year Published:	2009
Deposited By:	Agricultural Science
Deposited On:	2009-04-07
Last Modified:	2010-04-29
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