The increasing use of petrodiesel-biodiesel fuel blends throughout the world requires fast, economic and efficient analytical techniques that can be used for the quality control of these fuels. In this work, we develo...The increasing use of petrodiesel-biodiesel fuel blends throughout the world requires fast, economic and efficient analytical techniques that can be used for the quality control of these fuels. In this work, we developed an analytical method for determining the concentration of African palm biodiesel in blends with petrodiesel;the method is based on infrared spectroscopy (FTIR-ATR). To build a prediction model, nineteen petrodiesel-biodiesel blends were prepared in triplicate with biodiesel concentrations for 0%-100% by weight. The blends were analyzed using Fourier transform infrared spectroscopy, the spectral fingerprint data were used to build a prediction model through PLS regression. The optimal number of principal components (PCs), the standard error of calibration (SEC), the standard validation error (SEV), the correlation coefficient of calibration (r Cal) and the validation correlation coefficient (r Val) were used to validate the predictive ability of the model. The results show that the model obtained in this work has a good ability for determining the concentration of African palm biodiesel in petrodiesel-biodiesel blends.展开更多
文摘The increasing use of petrodiesel-biodiesel fuel blends throughout the world requires fast, economic and efficient analytical techniques that can be used for the quality control of these fuels. In this work, we developed an analytical method for determining the concentration of African palm biodiesel in blends with petrodiesel;the method is based on infrared spectroscopy (FTIR-ATR). To build a prediction model, nineteen petrodiesel-biodiesel blends were prepared in triplicate with biodiesel concentrations for 0%-100% by weight. The blends were analyzed using Fourier transform infrared spectroscopy, the spectral fingerprint data were used to build a prediction model through PLS regression. The optimal number of principal components (PCs), the standard error of calibration (SEC), the standard validation error (SEV), the correlation coefficient of calibration (r Cal) and the validation correlation coefficient (r Val) were used to validate the predictive ability of the model. The results show that the model obtained in this work has a good ability for determining the concentration of African palm biodiesel in petrodiesel-biodiesel blends.
