Abstract: The bidimensional multivariate regression procedures: multiple linear regression (MLR), principal component regression (PCR), partial least squares regression (PLS) and continuum regression (CR), and several N-way methods such as N-way PLS (nPLS) and parallel factor analysis (PARAFAC) are tested as calibration methods for the kinetic-spectrophotometric determination of ternary mixtures in a pseudo first-order kinetic system. The different calibration procedures were first applied to computer simulated kinetic-spectrophotometric data where the effect of spectral overlap and the differences in the kinetic constants were evaluated at a low level of experimental noise. Later they were applied to the stopped-flow kinetic-spectrophotometric simultaneous resolution of Co(II), Ni(II) and Ga(III) using 4-(2-pyridylazo)resorcinol (PAR) as a chromogenic reagent. Accurate estimations of concentrations with relative standard errors of prediction of about 8% were obtained even though a high degree of spectral overlap and similar rate constants were present. The study of the influence of experimental noise on the 3-component system justifies the difference between the simulations and the experimental results for the different calibration procedures. PARAFAC and MLR did not allow the resolution of the proposed 3-component system. CR provided slightly better results than those obtained by PLS, PCR and nPLS.

Abstract: Binary mixtures of ethanol-methanol were resolved by use of an enzymatic spectrophotometric method using artificial neural network (ANN) methodology for multivariate calibration. The chemical system involves two coupled reactions, viz the oxidation of the primary alcohols to the corresponding aldehydes in the presence of alcohol oxidase and the oxidation of p-phenylenediamine to Bandrowskis base by hydrogen peroxide, catalyzed by the previously formed aldehydes. The high complexity of the system studied entails the use of this non-linear calibration methodology, which provides significantly improved results relative to a multi-variate bilinear calibration, principal component regression (PCR), which was used for comparison. The optimized ANN allows the quantitation of both mixture components in ethanol to methanol mole ratios from 20 : 1 to 400 : 1, with relative standard errors of prediction in the region of 5% for both analytes.