Applicability of high-absorbance MIR spectroscopy in industrial quality control of reformed gasolines

Partial least squares (PLS), polynomial partial least squares (polynomial-PLS), locally weighted regression (LWR) and genetic inside neural network (GINN) algorithms were used to develop models for predicting motor octane number (MON) from non-leaded and catalytically reformed gasolines. Medium infrared (mid-infrared) spectra were obtained on liquid samples and chemometrically processed in order to get acceptable predictive models which allow their use for routine industrial quality monitoring. As MIR spectra currently present peaks with high absorbances, the presence and influence of nonlinearities was sought comparing the broadly-used PLS method with several other algorithms specially designed to cope with such influences (polynomial-PLS, local regression and neural networks). Their prediction abilities; i.e., stability and global prediction error when predicting new samples as well as their usefulness for routine industrial control were studied.