Analytical Sciences
Online ISSN : 1348-2246
Print ISSN : 0910-6340
ISSN-L : 0910-6340
Original Papers
Comparison of Calibration Methods with and without Feature Selection for the Analysis of HPLC Data
Piedad Parrilla VÁZQUEZMaría Martínez GALERAAntonia Garrido FRENICHJosé L. Martínez VIDAL
Author information
JOURNALS FREE ACCESS

2000 Volume 16 Issue 1 Pages 49-55

Details
Abstract

A comparison of two multivariate calibration methods, partial least squares (PLS) and principal component regression (PCR), applied to high-performance liquid chromatography (HPLC) data, is presented for the resolution of a pesticide mixture. The data set showed both coeluted peaks and overlapped absorption spectra. Besides, there is an additional overlapping between the signal of the mobile phase and that of some pesticide. Multivariate calibration models were evaluated using different criteria to choose the optimum number of latent variables. It is shown that PLS yields the best predictive models. Furthermore, two methods for selecting regions were applied with the goal to achieve an improved prediction ability in the present multicomponent determination by HPLC-DAD (diode array detector) with PLS. The selection of regions associated with a large correlation to the concentration and with large values in loading-weighs (from PLS) were considered. It is concluded that feature selection can also improve the multivariate calibration results using chromatographic data.

Information related to the author
© 2000 by The Japan Society for Analytical Chemistry
Previous article Next article
feedback
Top