title: Towards unsupervised analysis of second-order chromatographic data: automated selection of number of components in multivariate curve-resolution methods.
abstract: 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.
authors.affiliation: Polymer-Analysis Group, van't Hoff Institute for Molecular Sciences, University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands. vivo@science.uva.nl
authors.forename: G
authors.initials: G
authors.lastname: Vivó-Truyols
authors.forename: J R
authors.initials: JR
authors.lastname: Torres-Lapasió
authors.forename: M C
authors.initials: MC
authors.lastname: García-Alvarez-Coque
authors.forename: P J
authors.initials: PJ
authors.lastname: Schoenmakers
mesh_terms: D001331:Automation; D002851:Chromatography, High Pressure Liquid / Q000379:methods*; D015999:Multivariate Analysis