Near Infrared Spectroscopic Determination of Alcohols—Solving Non-Linearity with Linear and Non-Linear Methods

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Abstract
The concentrations of methanol and ethanol in carbon tetrachloride solutions have been determined using near infrared (NIR) spectra in the region between 1100 and 2500 nm. Spectral non-linearities due to the effects of concentration and temperature on hydrogen bonding were observed. The feasibility of obtaining accurate predictions of MeOH concentrations was assessed by applying multilinear regression (MLR) techniques, full spectra statistical methods and artificial neural networks (ANN) to their NIR spectra. Principal Component Regression (PCR), Partial Least Squares (PLS) and ANN produced calibration models with decreasing relative standard errors of prediction ( RSEP) of 0.02%, 0.012% and 0.008%, respectively, compared with MLR methods based on selected wavelengths which yielded a RSEP of 0.04%. Modelling of ethanol spectra gave similar results. We conclude that using full spectra chemometric techniques and ANN methods can accurately predict alcohol concentrations in spite of non-linearities.