Multi-window classical least-squares multivariate calibration methods for quantitative ICP-AES analyses

The advent of inductively coupled plasma atomic emission spectrometers (ICP -AES) equipped with charge-coupled device (CCD) detector arrays allows the application of multivariate calibration methods to the quantitative analysi s of spectral data. We have applied classical least-squares (CLS) methods t o the analysis of a variety of samples containing up to 12 elements plus an internal standard. The elements included in the calibration models were Ag , Al, As, Au, Cd, Cr, Cu, Fe, Ni, Pb, Pd, and Se. By performing the CLS ana lysis separately in each of 46 spectral windows and by pooling the CLS conc entration results for each element in all windows in a statistically effici ent manner, we have been able to significantly improve the accuracy and pre cision of the ICP-AES analyses relative to the univariate and single-window multivariate methods supplied with the spectrometer. This new multi-window CLS (MWCLS) approach simplifies the analyses by providing a single concent ration determination for each element from all spectral windows. Thus, the analyst does not have to perform the tedious task of reviewing the results from each window in an attempt to decide the correct value among discrepant analyses in one or more windows for each element. Furthermore, it is not n ecessary to construct a spectral correction model for each window prior to calibration and analysis. When one or more interfering elements were presen t, the new MWCLS method was able to reduce prediction errors compared to th e single-window multivariate and univariate predictions. The MWCLS detectio n limits in the presence of multiple interferences are 15 ng/g (i.e., 15 pp b) or better for each element. In addition, errors with the new method are only slightly inflated when only a single target element is included in the calibration (i.e., knowledge of all other elements is excluded during cali bration). The MWCLS method is found to be vastly superior to partial least- squares (PLS) in this case of limited numbers of calibration samples.