Linear and Nonlinear Calibration Methods for Predicting Mechanical Properties of Polypropylene Pellets Using Raman Spectroscopy.

A nondestructive and faster methodology to quantify mechanical properties of polypropylene (PP) pellets, obtained from an industrial plant, was developed with Raman spectroscopy. Raman spectra data were obtained from several types of samples such as homopolymer PP, random ethylene-propylene copolymer, and impact ethylene-propylene copolymer. Multivariate calibration models were developed by relating the changes in the Raman spectra to mechanical properties determined by ASTM tests (Young's traction modulus, tensile strength at yield, elongation at yield on traction, and flexural modulus at 1% secant). Several strategies were evaluated to build robust models including the use of preprocessing methods (baseline correction, vector normalization, de-trending, and standard normal variate), selecting the best subset of wavelengths to model property response and discarding irrelevant variables by applying genetic algorithm (GA). Linear multivariable models were investigated such as partial least square regression (PLS) and PLS with genetic algorithm (GA-PLS) while nonlinear models were implemented with artificial neural network (ANN) preceded by GA (GA-ANN). The best multivariate calibration models were obtained when a combination of genetic algorithms and artificial neural network were used on Raman spectral data with relative standard errors (%RSE) from 0.17 to 0.41 for training and 0.42 to 0.88% validation data sets.

In-line near-infrared (NIR) and Raman spectroscopy coupled with principal component analysis (PCA) for in situ evaluation of the transesterification reaction.

Biodiesel was synthesized from different commercially available oils while in-line Raman and near-infrared (NIR) spectra were obtained simultaneously, and the spectral changes that occurred during the reaction were evaluated with principal component analysis (PCA). Raman and NIR spectra were acquired every 30 s with fiber optic probes inserted into the reaction vessel. The reaction was performed at 60-70 °C using magnetic stirring. The time of reaction was 90 min, and during this time, 180 Raman and NIR spectra were collected. NIR spectra were collected using a transflectance probe and an optical path length of 1 mm at 8 cm(-1) spectral resolution and averaging 32 scans; for Raman spectra a 3 s exposure time and three accumulations were adequate for the analysis. Raman spectroscopy showed the ester conversion as evidenced by the displacement of the C=O band from 1747 to 1744 cm(-1) and the decrease in the intensity of the 1000-1050 cm(-1) band and the 1405 cm(-1) band as methanol was consumed in the reaction. NIR spectra also showed the decrease in methanol concentration with the band in the 4750-5000 cm(-1) region; this signal is present in the spectra of the transesterification reaction but not in the neat oils. The variations in the intensity of the methanol band were a main factor in the in-line monitoring of the transesterification reaction using Raman and NIR spectroscopy. The score plot of the first principal component showed the progress of the reaction. The final product was analyzed using (1)H nuclear magnetic resonance ((1)H NMR) spectroscopy and using mid-infrared spectroscopy, confirming the conversion of the oils to biodiesel.