Abstract
This chapter discusses the relevance of cluster analysis for food science. Food science and any associated technologies are continually evolving due to the large amounts of data either generated by instrumental methods or available in databases. Cluster analysis seeks to discover the number and the composition of the groups in the dataset. Principal component analysis (PCA) is used as a tool capable of providing an overview of the complexity that exists in multivariate datasets. PCA employs a mathematical procedure that transforms a set of possibly correlated response variables into a new set of non-correlated variables, called principal components. PCA can be performed on either a data matrix or a correlation matrix depending on the type of variables being measured. However, in a case where the original variables are nearly non-correlated, nothing can be gained by using a PCA analysis instead of classical statistics.
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