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PCA using Varimax with Kaiser normalization has resulted in the extraction of three main principal components which identifies the factors influencing each principal components for the physico-chemical parameters.

PCA using Varimax with Kaiser normalization rotation has resulted in the extraction of three main principal components which identifies the factors influencing each principal components for the main physico-chemical parameters.

Fig. 3 Variance corresponding to each principal component.

where is the eigenvalue of each principal component.

The corresponding alpha level for each principal component reflects how consistently the variables measure the principal component.

The amplitude of the variance of principal components can be regarded as the amount of information in each principal component.

(4) Decompose the covariance matrix using principal component analysis (11) where is the eigenvalue of each principal component.

The amount of variance explained by each principal component is shown in brackets.

Each principal component is a linear combination of the original variables.

This coefficient indicates how much each principal component contributes to the representation of the given attribute.

PC1 and PC2 represented 35.4 and 34.1 % of the variance explained by each principal component, respectively.