Application of regularized alternating least squares and independent component analysis to HPLC-DAD data of Haematococcus pluvialis metabolites

The analysis of data from analytical equipment will be an important factor in the execution of metabolomics. Self-modeling curve resolution (SMCR) is one of the theoretical techniques of chemometrics and has recently been applied to the data of hyphenated chromatography techniques. Alternating least squares (ALS) is a classical SMCR method. In ALS, however, different solutions are produced depending on randomly chosen initial values. Simulation in the present study showed that the use of a normalized constraint in calculating ALS was effective in avoiding this problem. We also improved the ALS algorithm by adding a regularized term (regularized ALS: RALS). Independent component analysis (ICA) is a comparatively new method and has been discussed very actively by information science researchers, but has still been applied only in very few cases to curve resolution problems in chemometrics studies. We applied RALS with a normalized constraint and ICA to the HPLC-DAD data of Haematococcus pluvialis metabolites and obtained a high accuracy of peak detection, suggesting that these curve resolution methods are useful for identification of metabolites in metabolomics.