Abstract
An alternative methodology was developed to monitor the biokerosene content of palm kernel in blend with kerosene using medium infrared spectroscopy associated with partial least squares (PLS). The efficiency of this methodology was analyzed based on the parameters of accuracy and figures of merit. The values of root-mean-square error of cross-validation (RMSECV), root-mean-square error of calibration (RMSEC) and root-mean-square error of prediction (RMSEP) were in agreement because the RMSEP was higher than RMSECV and RMSEC. In addition, the RMSEP value is considered acceptable according to the Brazilian standard ABNT NBR 15568 because it is less than 1%. The figures of merit were performed in agreement with the requirements established in the standard ASTM E1655-05. The linearity of the model was assessed based on the analysis of the model fit through the correlation of the actual and predicted values of the calibration and prediction sets, where a high correlation between the values was evidenced, with a correlation coefficient (R) exceeding 0.99. The good results of the application of MIR spectroscopy combined with multivariate regression by PLS suggest that this analytical methodology is feasible, efficient and suitable for use by inspection agencies to control the biokerosene content of palm kernel in mixture with diesel.
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