Variable reduction algorithm for atomic emission spectra: application to multivariate calibration and quantitative analysis of industrial samples

A variable selection procedure has been developed and used to reduce the number of wavelength data points necessary to formulate a predictive multivariate model for Pt, Pd and Rh using full atomic emission spectra (5684 wavelength data points per spectrum) obtained using a Segmented-Array Charge-Coupled Device Detector (SCCD) for inductively coupled plasma atomic emission spectrometry (ICP-AES). The first stage was the application of an Uninformative Variable Elimination Partial Least Squares (UVE-PLS) algorithm which identified the PLS regression coefficients that are equal to zero at a specified significance level, and removed the associated variables. The second stage was the application of an Informative Variable Degradation PLS (IVD) algorithm, which ranked variables using the ratio of their PLS mean regression coefficients and regression coefficient estimated standard errors (estimated using the jackknife). Variable selection was then achieved by examination of the cumulative sum of these ratios. The algorithms were applied to emission spectra for the determination of Pt, Pd and Rh in a synthetic matrix (Al, Mg, Ce, Zr, Ba, In, Sc and Y at various concentrations). The variable selection routines reduced 5684 wavelength data points to 47, 110 and 334 and gave relative root mean square error (RRMSE) values of 4.60, 3.20 and 1.65% for Pt, Pd and Rh, respectively, compared with 12.6, 8.32 and 27.2% obtained using all 5684 data points; and 5.19, 7.06 and 3.18% obtained when using 166 pre-selected, integrated atomic emission lines. There was no requirement for wavelength or background correction point selection or for wavelength alignment because the entire available segmented spectrum was used in the calibration model. This approach was extended by using real industrial fusion samples to build a calibration model in order to predict the concentration of Au, Ag and Pd in similar matrices. RRMSEs of 23, 29 and 9.1% were obtained for the respective elements in test samples.