Principal component analysis - an efficient tool for selection of wheat samples with wide variation in properties

Abstract

Twenty-three variables were studied that described the chemical composition and baking properties of 100 samples of spring and winter wheat. Sixteen of these samples were selected with the aid of principal component analysis (PCA) in such a manner that much of the variation in all the parameters was retained. The selection procedure preserved a larger part of the variation in the original material than selection by random sampling. This was deduced from comparison of ranges recovered with respect to each variable and selection procedure. The principles of PCA are general and the procedure developed can therefore be recommended as one means of solving sample selection problems.