Abstract

Multivariate calibration of UV-visible spectral data using partial least-squares (PLS) has been applied to the determination of the nitrate content in Chilean Caliche samples, in the concentration range from 1 to 20% NaNO3 The multivariate approach is required since the samples do also contain unknown interferences which are spectrally active in the useful wavelength region for nitrate quantitation (near 301 nm). A set of fifteen calibration samples was employed to build the multivariate model, selected using the Kennard-Stone methodology, starting from real Caliche samples whose nitrate content was previously determined using the reference Devarda method. The figures of merit of the multivariate model were satisfactory (the limit of detection and quantitation reached 0.04% and 0.12 % of NaNO3, respectively, with an average error of prediction of 0.3 % of NaNO3). Then, the PLS model was then applied to a set of independent Caliche samples. The results were compared with a univariate UV approach, and with the nitrate content determined by the reference method, using the linear regression of predicted vs. reference concentration values, together with the elliptical joint confidence region test for the slope and intercept of the latter regression. The results ind