Chemometrics: the issues of measurement and modelling

In this paper, two spectral data sets have been used to illustrate the importance of maintaining chemical information whilst generating predictive multivariate calibration models. The first data set is based on 26 duplicate UV/VIS spectra for four meal ions (Fe, Ni, Co, Cu) present at varying concentrations in aqueous solution. Spectra were collected across the range 180–800 nm at a resolution of 3.5 nm generating 211 data points for each sample. Calibration was carried out using multiple linear regression (MLR) and a K-matrix approach to demonstrate the advantages the latter method has in describing real spectral features. In addition, the limitation of MLR in accommodating noise and spectral overlap in the data is also illustrated. The second data set based on NIR spectroscopy, was generated using a four-level 2 factor Factorial design strategy and consisted of two additives present at a range of concentrations in an aqueous caustic system, with the spectra being collected over the range 10,000–3000 cm⁻¹. Whilst a conventional partial least squares (PLS) model was applied to the data, it was through the use of variable selection (VS) prior to PLS and the application of weighted ridge regression (WRR) techniques that the need to develop chemometric methodology which intuitively reflected chemical information has been demonstrated. The results will also illustrate how a poorly designed experimental design protocol and missing data can limit the performance of the calibration models generated. The aims of this paper are not to prescribe ideal calibration methodology but rather to demonstrate the relevance of selecting multivariate calibration methodology that relates more to the chem rather than just the metrics in chemometrics.