Discrimination of maize inbreds for kernel quality traits and fatty acid composition by a multivariate technique

Abstract

Quality is considered to be mainly related to protein and oil concentration in maize kernel. Great effort has been made through the elevation of oil and protein concentrations; however, studies to enhance the composition quality of oil have been limited. Maize genotypes with special fatty acid composition may confer different end uses and added value. We investigated eight maize inbreds for kernel quality and fatty acid composition. Kernel samples from a two-year experiment were analyzed for oil, protein, moisture and ash, as well as several fatty acids, including oleic, linoleic, and linolenic acids. Results indicated differences among the genotypes for all traits, except for behenic acid. A680 appeared to be a promising parent to elevate oleic acid, while N194 and RSSSC were the genotypes with the highest oil and protein levels, respectively. A new statistical approach, Canonical Discriminant Analysis with Hypothesis-Error plot technique was utilized to evaluate the data. It showed a good level of agreement with linear tests, and show similar results with those of multi comparison test. With the easily understandable graphical outputs that can interpret the interaction of variables and cases, it offers a good alternative way in analyzing data.