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Multivariate regression models obtained from near-infrared spectroscopy data for prediction of the physical properties of biodiesel and its blends

机译：从近红外光谱数据获得的多元回归模型可预测生物柴油及其混合物的物理性质

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Multivariate calibration based on Partial Least Squares (PLS), Random Forest (RF) and Support Vector Machine (SVM) methods combined with variable selections tools were used to model the relation between the near-infrared spectroscopy data of biodiesel fuel to its physical-chemical properties. The cold filter plugging point (CFPP) and a kinematic viscosity at 40 degrees C of the biodiesel samples and its blends were evaluated using spectroscopic data obtained with a near-infrared reflectance accessory (NIRA/NIR-FT-IR). Therefore, one hundred forty-nine blends were prepared using biodiesel from different sources, such as canola, corn, sunflower, and soybean. Furthermore, biodiesel samples purchased from the Brazil South Region were added to the study. One hundred samples were used for the calibration set, whereas the remaining samples were used as an external validation set. The results showed that the SVM model with baseline correction + mean centering preprocessing gave the best prediction for the CFPP, with a root-mean-square of error (RMSEP) equal to 0.9 degrees C. Among the models presented, the best result for predicting the kinematic viscosity at 40 degrees C was obtained by the PLS regression method using an interval selected by UVE with baseline correction+ derivative preprocessing, with the RMSEP equal to 0.0133 mm(2).s(-1). The results in this work showed that the proposed methodologies were ade-quated in predicting the biodiesel fuel properties. The figure of merit Sum of Wilcoxon Test Probability (SWTP) presented in this study was necessary for the conclusion of the best model.
机译：基于偏最小二乘（PLS），随机森林（RF）和支持向量机（SVM）方法并结合变量选择工具的多元标定用于模拟生物柴油燃料的近红外光谱数据与其物理化学之间的关系属性。使用通过近红外反射附件（NIRA / NIR-FT-IR）获得的光谱数据评估生物柴油样品及其混合物的冷滤塞点（CFPP）和40摄氏度时的运动粘度。因此，使用油菜籽，玉米，向日葵和大豆等不同来源的生物柴油制备了149种混合物。此外，从巴西南部地区购买的生物柴油样品也加入了研究。一百个样本用于校准集，而其余样本用作外部验证集。结果表明，带有基线校正+平均居中预处理的SVM模型对CFPP给出了最佳预测，误差的均方根（RMSEP）等于0.9摄氏度。在给出的模型中，预测的最佳结果通过PLS回归方法，使用UVE选择的间隔进行基线校正+导数预处理，获得40摄氏度时的运动粘度，RMSEP等于0.0133 mm（2）.s（-1）。这项工作的结果表明，所提出的方法学已足够预测生物柴油的燃料特性。这项研究中提出的Wilcoxon测试概率（SWTP）的优值总和对于得出最佳模型是必要的。

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