A review of multivariate calibration methods applied to biomedical analysis
2005; Elsevier BV; Volume: 82; Issue: 1 Linguagem: Inglês
10.1016/j.microc.2005.07.001
ISSN1095-9149
AutoresGraciela M. Escandar, Patricia C. Damiani, Héctor C. Goicoechea, Alejandro C. Olivieri,
Tópico(s)Fault Detection and Control Systems
ResumoThe determination of the contents of therapeutic drugs, metabolites and other important biomedical analytes in biological samples is usually performed by using high-performance liquid chromatography (HPLC). Modern multivariate calibration methods constitute an attractive alternative, even when they are applied to intrinsically unselective spectroscopic or electrochemical signals. First-order (i.e., vectorized) data are conveniently analyzed with classical chemometric tools such as partial least-squares (PLS). Certain analytical problems require more sophisticated models, such as artificial neural networks (ANNs), which are especially able to cope with non-linearities in the data structure. Finally, models based on the acquisition and processing of second- or higher-order data (i.e., matrices or higher dimensional data arrays) present the phenomenon known as "second-order advantage", which permits quantitation of calibrated analytes in the presence of interferents. The latter models show immense potentialities in the field of biomedical analysis. Pertinent literature examples are reviewed.
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