Towards unsupervised analysis of second-order chromatographic data: automated selection of number of components in multivariate curve-resolution methods.

A method to apply multivariate curve-resolution unattendedly is presented. The algorithm is suitable to perform deconvolution of two-way data (e.g. retrieving the individual elution profiles and spectra of co-eluting compounds from signals obtained from a chromatograph equipped with multiple-channel detection: LC-DAD or GC-MS). The method is especially adequate to achieve the advantages of deconvolution approaches when huge amounts of data are present and manual application of multivariate techniques is too time-consuming. The philosophy of the algorithm is to mimic the reactions of an expert user when applying the orthogonal projection approach--multivariate curve-resolution techniques. Basically, the method establishes a way to check the number of significant components in the data matrix. The performance of the method was superior to the Malinowski F-test. The algorithm was tested with HPLC-DAD signals.