3.3. Principal component analysis of cooked rice sulphur volatiles
Principal component analysis, PCA, is a multivariate pattern recognition procedure which was employed to determine if there were differences in the sulphur volatile patterns from the three rice types. Shown in Fig. 3 are the PCA score (upper) and load (bottom) plots from 37 integrated sulphur peaks from the three rice samples reported in triplicate. Since there were at least two orders of magnitude difference in the sulphur peak areas, the data for each sulphur peak was mean centred and scaled to one standard deviation. The grouping of the three triplicate rice samples indicates that the GC–S chromatographic peak areas were fairly consistent. The first principal component separated the Basmati samples from the other two fragrant rice samples and PC2 separated the Jasmine from the Basmati samples. The first two principal components combined to describe 60% of the variance. The fact that the three groups did not overlap indicates that each of the three rice types had a unique sulphur peak pattern.