A PLS regression model using NIR spectroscopy for on-line monitoring of the biodiesel production reaction

In this work, a Partial Least Squares (PLS) regression model using Near-Infrared (NIR) spectroscopy was developed to monitor the progress of the catalyzed transesterification reactions of soybean oil that produce biodiesel. The NIR spectra were collected during the transesterification reaction with a lab made spectrophotometric flow cell. Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy was employed for determining the conversion percentage of glycerides to methyl esters during the transesterification reaction and used as reference to build the PLS calibration model employing NIR spectroscopy data. The model, constructed with selected spectral range has not been tried before and allows the monitoring of the transesterification reaction in terms of conversion ratio for different temperatures. The model was validated and the values of Root Mean Square Error of Prediction (RMSEP) found for two different temperatures were 0.74% and 1.27% (of conversion) for reactions carried out at 20 ± 0.2 °C and 55 ± 0.2 °C, respectively.