Detection and Correction of Biased Results of Individual Analytes in Multicomponent Spectroscopic Analysis

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Abstract
Simultaneous spectroscopic multicomponent analysis based on Beer's law requires the test sample to follow the hard model, independently of whether this model is built with the full spectrum or only with a few sensors selected according to an optimality criterion. We have developed a graphical method based on the net analytical signal concept to detect bias in the predicted results of individual analytes in test samples. When an interference, or other causes which produce bias, are detected, a moving window approach is used to select the subset of sensors that minimize the bias of the predicted results. The method has been validated with UV−vis spectra of binary clorophenol mixtures and of mixtures of four pesticides in water analyzed with a FIA system with diode array detection.