Fourier Transform Near-Infrared Spectroscopy as a Reliable Method for Quick Real Time Analysis of Complex Samples in Industry

Near-infrared spectroscopy (NIRS) in conjunction with multivariate calibration have proved to be adequate techniques to predict water content as well as methanol concentration in a industrial multicomponent mixture. The calibration models were developed using real industrial complex samples of water, methanol, and several other components in variable but lower concentrations such as sulfonic acids, acetone, and methyl cyanide. These samples come from industrial distillation of hydrated methanol. Prior to the establishment of the two calibration models, wavelength selection, original spectral data compression by means of the principal components analysis technique, and preprocessing optimization were performed. Calibration was modeled according to partial least squares (PLS) regression which was developed using the mean centering and the leave-one-out cross validation (LOOCV) method for the optimization of the principal components (PC) number. Both models were developed in the 5369-5153 cm(-1) spectral region. Karl Fischer titration and gas chromatography were used as reference methods for water and methanol quantification, respectively. The suitability of the models was evaluated through the calculation of the standard error of calibration and standard error of prediction. For the water content determination the variance scaling pretreatment was applied with 12 PLS factors, the calibration and prediction errors being 2.56% and 1.7696 respectively. As for the methanol concentration variance scaling was applied in conjunction with Savitzky-Golay's second order derivative with six PLS factors. In this case the calibration and prediction errors were 0.42% and 0.90%, respectively.