 PCA algorithms aim to replace all correlated variables by a much smaller number of uncorrelated variables (referred to as principal components (PCs)) and retain most of the information in the original dataset.This clustering can elucidate patterns and assist in the determination of disease biomarkers - metabolites that correlate most with class membership.Linear models are commonly used for metabolomics data, but are affected by multicollinearity.