Matrix effect modelling in multivariate determination of priority pollutant chlorophenols in urine samples Original Research Article

The possibility of modelling matrix effect in multivariate calibration has been studied for a complex sample such as urine. The matrix effect was minimized by including, in the calibration step, standard samples containing the analytes in the presence of the interfering matrix. The number of samples was selected taking into account the standard error of prediction (SEP). By this method, a flow-injection multivariate spectrophotometric determination of five priority pollutant chlorophenols in urine samples using a PLS method is demonstrated. The recoveries of the chlorophenols in natural urine samples ranged from 85±32% to 121±31% for concentrations between 0.2 and 1.0 mg l−1. Net analyte signal calculation was used to estimate the selectivity of the signal for each chlorophenol. Selectivity was found to range between 0% and 2.5% of the measured signal.