Multivariate Statistical Process Control Applied to Sulfate Determination by Sequential Injection Analysis

Abstract

A methodology was developed for determining sulfates in water at levels up to 500 mg l ^-1 using a sequential injection analysis system. The multivariate calibration model avoids the need for the separation of interferents and sample pre-treatment. The trueness of the multivariate calibration model was assessed by comparing the predictions of the model with reference concentrations determined by a reference method using the joint interval test for the slope and intercept of the regression line with errors on both axes. The accuracy, evaluated by the root mean square error of prediction, reached 6.9%. Multivariate statistical process control techniques were used to check the system’s stability before developing the model and the validity of the model when it is used to predict the concentrations of unknown samples.