INVESTIGADORES
GOICOECHEA Hector Casimiro
congresos y reuniones científicas
Título:
DIFFERENT STRATEGIES FOR SECOND ORDER DATA GENERATION AND MODELING. APPLICATIONS TO SOLVE DIFFERENT ANALYTICAL PROBLEMS
Autor/es:
GOICOECHEA, H C
Reunión:
Congreso; CAC 2012; 2012
Resumen:
Second-order
data enclose the so-called second-order advantage, which allows predicting
the concentration of the analyte of interest even in the presence of unknown
interferents, as well as enabling several analytes to be determined
simultaneously [1].
In this report, results for several
experimental data sets are presented in order to show the great potentiality of
the second order data modeled with convenient algorithms to solve different
analytical problems. They present the following challenges to second-order
algorithms: 1) linear dependency due to a kinetic reaction in one mode, 2) peak
shifts (CE data), and 3) non-linearity. 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 five
fluoroquinolones which are determined in environmental samples (i.e., river water)
by using capillary electrophoresis with diode array detection. Multivariate
curve resolution with alternating least squares (MCR-ALS) without pre-treatment
outperformed parallel factor analysis (PARAFAC) and partial least squares
followed by residual bilinearization (PLS/RBL) in profiles extraction and
quantitation of the five analytes.
Data set 2 includes fluorescence-time
measurements made by creating a gradient within a flow injection system. These false
second order data were applied to resolve mixtures of two antihistaminic drugs
(loratadine and desloratadine) in serum samples. The use of a surfactant was
necessary to obtain the selectivity to differentiate both spectra. This kind of
data present the problem of complete overlapping of profiles in one data
dimension, which can be regarded as a special and serious case of linear
dependency [2]. The strategy involves the building of a MCR-ALS model composed
of matrices augmented in the temporal mode, i.e. spectra remain invariant while
time profiles may change from sample to sample.
In Data set 3,
fluorescence-time data were studied for the oxidation reaction of loratadine
and desloratadine with potassium bromate, in order to determine both drugs in
human serum samples. Linear dependency in both temporal and spectral modes
precluded the use of MCR-ALS and PLS/RBL, providing better results than
PARAFAC.
Data set 4 consists of fluorescence-time
data obtained for the oxidation reaction of three dyes with potassium bromate. The
possibility of exploiting the second-order advantage from these non-linear
second-order data could be reached by the application of two successive
methods: the first one modeled the calibration and validation data removing the
contribution of unexpected components, and the second one models the non-linear
relationship. MCR-ALS was the only strategy that retrieved reasonably accurate
predictions.