INVESTIGADORES
CULZONI Maria Julia
congresos y reuniones científicas
Título:
Residual bilinearization. A useful tool providing PLS and ANN with the second order advantage.
Autor/es:
MARÍA J. CULZONI, MIGUEL A. CABEZÓN, GRACIELA M. ESCÁNDAR, ALEJANDRO GARCÍA-REIRIZ, HÉCTOR C. GOICOECHEA, NILDA MARSILI, ALEJANDRO C. OLIVIERI, ARIANA P. PAGANI, GISELA PICCIRILLI.
Lugar:
Águas de Lindóia, SP, Brasil.
Reunión:
Congreso; 10th International Conference on Chemometrics in Analytical Chemistry.; 2006
Resumen:
Partial least-squares
(PLS) regression based on unfolded second- or higher-order data can be combined
with residual bilinearization (RBL) to yield a flexible higher-order
multivariate method showing the second-order advantage. Due to its latent
variable structure, it can deal with data sets deviating from trilinearity, or
showing linear dependency in one or more modes. The technique is known from ca.
15 years ago, but only recently captured the attention of
analytical chemists.
In this report, the results are
presented for several experimental data sets, prepared in order to show the
great potentiality of the PLS/RBL technique, in all cases in the presence of an
interfering background requiring strict adherence to the second-order
advantage. They were designed to present the following challenges to
second-order algorithms: 1) variable analyte-background interactions, 2) linear
dependency due to pH changes in one mode, 3) linear dependency due to a reaction
kinetics in one mode, and 4) fluorescence inner filter effect. In all of these
cases, deviations from the ideal trilinearity are likely to occur due to
changes in component profiles from sample to sample.
Data
set 1 involves two pharmaceuticals which are determined in complex biomedical
samples (i.e., salicylate and tetracycline in human serum), where interactions
between the analytes and the background matrix modifies the analyte fluorescent
spectra, causing sample-to-sample spectral variations. PLS/RBL outperformed
parallel factor analysis (PARAFAC) in the analysis of the less sensitive
analyte tetracycline.
Data
set 2 includes absorbance-pH
measurements made by creating a double pH gradient within a flow injection
system. These data were applied to resolve mixtures of four dyes employed in
the manufacturing of juice powders. Linear dependency occurs in the pH mode,
because of the relationship between analyte species at different pH values, and
also in the spectral mode, due to severe collinearity between acid and basic
absorption spectra. Only PLS/RBL (and its classical variant of bilinear
least-squares, BLLS/RBL) could be used to resolve the analytical problem.
In Data
set 3, absorbance-time data were studied
for the reaction between the antibiotic amoxicillin and copper(II) ions, in
order to determine the former in human urine samples. Linear dependency in both
the time and spectral modes precluded the use of multivariate curve resolution
(MCR), while PLS/RBL provided better results than PARAFAC.
Data
set 4 consists of fluorescent excitation-emission matrices measured for a
mixture of fungicides, to be determined in water in the presence of other
potentially interfering agrochemicals. Inner filter effect from one of the
analytes over the other one precluded the use of PARAFAC, MCR and BLLS/RBL,
leaving PLS/RBL as the only second-order tool to be successfully employed if
the second-order advantage is required.
Finally,
developments concerning the achievement of the second-order advantage from non-linear
second-order signals will be discussed. A recent algorithm based on the
combination of artificial neural networks (ANN) with RBL may be able to provide
the second-order advantage in this instance.