A review of multivariate calibration methods applied to biomedical analysis

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• 作者：Graciela M. Escandar ; Patricia C. Damiani ; Hé ; ctor C. Goicoechea and Alejandro C. Olivieri
• 关键词：Multivariate calibration methods ; Biomedical analysis ; First- and higher-order data
• 刊名：Microchemical Journal
• 出版年：2006
• 出版时间：January 2006
• 年：2006
• 卷：82
• 期：1
• 页码：29-42
• 全文大小：218 K

文摘

The 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.