Multivariate calibration modelling using electrochemical/inductively coupled plasma mass spectrometry data for trace elements in ultrahigh quality water and humic acid matrices

Abstract

Robust multivariate calibration is described for the determination of trace metals in water matrices. A multivariate calibration model containing electrochemical and ICP-MS concentration data for Zn, Cu, Cd and Pb mixtures in the 0.5–50 µg l^–1 range was constructed using the partial least squares (PLS) method. Fifty solutions were prepared in ultrahigh quality (UHQ) water to which humic acid was added to simulate interferents. A second data set consisting of similar elemental combinations was prepared in a UHQ water matrix only. The electrochemical data was collected using anodic stripping voltammetry with ICP-MS providing independent quantitative data. All experimental work was carried out in replicate to account for variations in the ambient experimental conditions and to aid the identification of outliers. The training data set used for the calibration model was transformed prior to modelling using two separate data pre-treatment techniques. The first technique scaled the raw data using the mean of one method whilst the second used the same technique with 10% random noise added to the raw data. These two pre-treatment techniques are compared and contrasted. The calibration model using the second pre-treatment technique gave the most accurate concentration predictions for eight unknown test solutions which consisted of four solutions from either matrix. These predictions were all within a 10% relative standard error of the actual concentrations.