INVESTIGADORES
GOICOECHEA Hector Casimiro
artículos
Título:
MULTIVAR: a program for multivariate calibration incorporating net analyte signal calculations. H. Goicoechea y A. Olivieri
Autor/es:
GOICOECHEA, HÉCTOR C; AC OLIVIERI,
Revista:
TRAC-TRENDS IN ANALYTICAL CHEMISTRY
Editorial:
Elsevier
Referencias:
Año: 2000 vol. 19 p. 599 - 605
ISSN:
0165-9936
Resumen:
A useful chemometric tool is the use of multivariate
calibration applied to spectroscopic data [ 1],
which enables the ef¢cient extraction of information
concerning certain analytes of interest from
spectra of multicomponent mixtures. Two popular
methods are principal component regression (PCR)
and partial least-squares (PLS) [ 2 ], both of which
use inverse calibration steps combined with a prior
optimisation of the calibration information. They
have the following advantages: (1) use of full spectra,
(2) knowledge of only the concentrations of the
analytes of interest in the calibration samples is
required, and (3) spectral decomposition into factors
avoids the problems associated with spectral
collinearities [ 2 ]. They are ideally suited for the
study of complex biological samples, as in drug or
metabolite monitoring in blood [ 3,4 ], or in pharmaceutical
analysis of certain multicomponent preparations
where not all the excipients may be known
[ 5 ]. Acommon requirement of all thesemultivariate
methods is that the background should be modelled
during calibration, i.e. compounds of no interest
( but present in unknown samples) should be
contained in the calibration samples.