A New Robust Bilinear Least-Squares Method for the Analysis of Spectral-pH Matrix Data

H. GOICOECHEA AND A.C. OLIVIERI

APPLIED SPECTROSCOPY

Society for Applied Spectroscopy

Lugar: New York; Año: 2005 vol. 59 p. 926 - 933

0003-7028

A new second-order multivariate method has been developed for the analysis of spectral-pH matrix data, based on a bilinear least-squares (BLLS) model achieving the second-order advantage and handling multiple calibration standards. A simulated Monte Carlo study of synthetic absorbance-pH data allowed to compare the newly proposed BLLS methodology with constrained parallel factor analysis (PARAFAC) and with the combination multivariate curve resolution-alternating least-squares (MCR-ALS) under different conditions of sample-to-sample pH mismatch and analyte-background ratio. The results indicate an improved prediction ability of the new method. Experimental data generated by measuring absorption spectra of several calibration standards of ascorbic acid and samples of orange juice were subjected to second-order calibration analysis with PARAFAC, MCR-ALS and the new BLLS method. The results indicate that the latter one provides the best analytical results as regards analyte recovery in samples of complex composition requiring strict adherence to the second-order advantage. Linear dependencies appear when multivariate data are produced by using the pH or a reaction time as one of the data dimensions, posing a challenge on classical multivariate calibration models. The presently discussed algorithm is useful for these latter systems.