Automated wavelength selection for spectroscopic fuel models by symmetrically contracting repeated unmoving window partial least squares

The need for automated quality surveillance of liquid hydrocarbon fuels has driven the development of rapid fuel property modeling from spectroscopic sensordata. The correlation of near-infrared (NIR) and Raman spectroscopic data with jet and diesel fuel properties can be improved by the deliberate selection of continuous wavelength sub-ranges. An automatic wavelength selection strategy would allow for the unsupervised construction of partial least squares (PLS) regression models of increased predictive utility when supervised model construction and maintenance is not feasible. Changeable size moving window partial least squares (CSMWPLS) is one of the most thorough operations suited for this task. Unfortunately, the necessarily large number of PLS model constructions required by an automated version of this procedure limits the evaluation of the predictive ability of the resulting models through full cross-validation results. Presented here is a novel restricted version of the CSMWPLS algorithm in which the initial spectral range selection is accomplished through multiple interval PLS (iPLS) analyses, where analysis windows for the refinement step no longer move, and size changes are limited to a series of symmetric attenuations. It is shown that the proposed algorithm can provide significant PLS model improvements during the course of a fully automated analysis of jet and diesel fuel spectra in less time than an automated CSMWPLS algorithm. Published by Elsevier B.V.