Monitoring biodiesel and its intermediates in transesterification reactions with multivariate curve resolution alternating least squares calibration models

The monitoring of biodiesel production processes is currently performed by methods requiring high reagent consumption and long response times. Aiming to overcome these drawbacks, this article developed chemometric models combining principal component analysis (PCA), multivariate curve resolution alternating least squares (MCR-ALS) and mid infrared spectroscopy to determine simultaneously the concentrations of the main components, methyl esters and triglycerides, along transesterification reactions. This methodology also tried to model the intermediates (fatty acids plus monoglycerides, and diglycerides), thus providing semi-quantitative predictions for these components. These reactions were carried out in different experimental conditions according to a factorial design with two factors, the batch of soybean oil (raw material) and the alcohol:oil molar ratio. The concentration values estimated by high performance liquid chromatography (HPLC), as the reference method, were the inputs for the correlation constraint used as a key step in the multivariate calibration MCR-ALS models. The spectral profiles recovered for the methyl esters and for the triglycerides have shown high correlations (greater than 0.985) in comparison to the reference spectra. In addition, the spectra calculated for fatty acids, monoglycerides and diglycerides showed absorption bands characteristic of functional groups present in these molecules, mainly the hydroxyl and carbonyl stretching bands. The root mean square errors of calibration and prediction estimated for the triglycerides and for the methyl esters were within the range from 1.9% to 6.3%.