DETECTION AND CORRECTION OF BIASED RESULTS OF INDIVIDUAL ANALYTES IN MULTICOMPONENT SPECTROSCOPIC ANALYSIS

Simultaneous spectroscopic multicomponent analysis based on Beer's law requires the test sample to follow the hard model, independently of w hether this model is built with the full spectrum or only with a few s ensors selected according to an optimality criterion. We have develope d a graphical method based on the net analytical signal concept to det ect bias in the predicted results of individual analytes in test sampl es. When an interference, or other causes which produce bias, are dete cted, 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 mi xtures of four pesticides in water analyzed with a FIA system with dio de array detection.