Pre-processed spectra have been used to determine biochemical correlations between pollen samples using PCA and HCA. Classification is unattended by PCA. Fig. shows the scatter plot for the first two principal components estimated with PCA, through which visualization of the data structure is obtained. A natural separation of pollen samples into three sets clear occurred in a 2-dimensional plot. The first principal component accounts for 70.2% of all the variances, while the second accounts for 20.9%, leading to a cumulative proportion of 91.1% of all of the other variances that were considered significant. Thus, PC1 is more important than PC2 and further PC3 (3.3%) in this study. In general, the PCA was able to discriminate pollen in three distinct groups based on similarity of chemical components and geographical origin. In general, the PCA was able to discriminate pollen in three distinct groups.