Principal Component Analysis (PCA) is one of the most popular chemometrics methods . PCA is an unsupervised method of clustering , an exploratory approach that decreases the data matrix dimension and compresses the details into a few new variables called key components, linear combinations of the original variables . In a twodimensional plot the PCA are displayed using the first two main components. The first major variable, PC1, reflected the overall cumulative variance, the second, PC2 was orthogonal with the first, covering as much of the residual variation as possible. The interrelationships among different variables could be interpreted by plotting the PCs and analysing sample trends, groupings, similarities, and differences .