Eliminate indeterminacies of independent component analysis for chemometrics

An improved method has been proposed to eliminate the indeterminacies of independent component analysis (ICA) for chemometrics. Following the arrangement of principal components analysis (PCA), the ICA mixing matrix is selected as signal content indexes, and ICA output are sorted and directed. After many times reputations, independent components (ICs) are paired according to the maximum correlation coefficient, and then the mean values of each IC substitutes the original ICs. This indicates that the ICA indeterminacies are eliminated. A simulation example is tested to validate this improvement. Finally, a set of experimental LC-MS data is processed without any priori knowledge or specific limitation and the results show that the improved ICA can directly separate the mixed signals in chemometrics, and it is simpler and more reasonable than the simple to use interactive self-modeling mixture analysis (SIMPLISMA).