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.