Improving the robustness of a partial least squares (PLS) model based on pure component selectivity analysis and range optimization: Case study for the analysis of an etching solution containing hydrogen peroxide Original Research Article

Pure component selectivity analysis (PCSA) was successfully utilized to enhance the robustness of a partial least squares (PLS) model by examining the selectivity of a given component to other components. The samples used in this study were composed of NH4OH, H2O2 and H2O, a popular etchant solution in the electronic industry. Corresponding near-infrared (NIR) spectra (9000–7500 cm−1) were used to build PLS models. The selective determination of H2O2 without influences from NH4OH and H2O was a key issue since its molecular structure is similar to that of H2O and NH4OH also has a hydroxyl functional group. The best spectral ranges for the determination of NH4OH and H2O2 were found with the use of moving window PLS (MW-PLS) and corresponding selectivity was examined by pure component selectivity analysis. The PLS calibration for NH4OH was free from interferences from the other components due to the presence of its unique NH absorption bands. Since the spectral variation from H2O2 was broadly overlapping and much less distinct than that from NH4OH, the selectivity and prediction performance for the H2O2 calibration were sensitively varied depending on the spectral ranges and number of factors used. PCSA, based on the comparison between regression vectors from PLS and the net analyte signal (NAS), was an effective method to prevent over-fitting of the H2O2 calibration. A robust H2O2 calibration model with minimal interferences from other components was developed. PCSA should be included as a standard method in PLS calibrations where prediction error only is the usual measure of performance.