COMPARISON OF MULTIVARIATE CALIBRATION TECHNIQUES FOR THE QUANTIFICATION OF MODEL PROCESS STREAMS USING DIODE-ARRAY SPECTROPHOTOMETRY

The relative precisions of five multivariate calibration methods [dire ct multicomponent analysis (DMA), stepwise multiple linear regression (SMLR), principal components regression (PCR), and PLS1 and PLS2 (PLS = partial least-squares)] are evaluated for the determination of trans itionmetal ions in model multicomponent systems. These systems represe nt simulated industrial process streams containing mixtures of two, th ree and five metal ions. Physical and chemical interferences have been incorporated to provide a rigorous test of the calibration techniques . Diode-array spectrophotometry has been used to obtain spectra of the inherent absorbances of the metal ions in the visible region. Multiva riate calibration models have been constructed from these data and use d to predict concentrations of metal ions in test solutions. Results a re presented for 'well behaved' and more complex multicomponent system s, and the predictive performance of each calibration technique is dis cussed. It is demonstrated that SMLR, PCR, PLS1 and PLS2 provided cali bration models significantly more robust than those of DMA when physic al and chemical interferences were present. SMLR often provided the be st precisions in both well behaved and less well behaved systems. Cali brations based on first-derivative data afforded greater precision tha n those based on absorbance data.