Programa de Pós-Graduação em Ciências Agrárias, Universidade Estadual da Paraíba, 58.429-500 Campina Grande - PB, Brazil.
Talanta. 2011 Dec 15;87:30-4. doi: 10.1016/j.talanta.2011.09.025. Epub 2011 Sep 19.
This work is concerned of evaluate the use of visible and near-infrared (NIR) range, separately and combined, to determine the biodiesel content in biodiesel/diesel blends using Multiple Linear Regression (MLR) and variable selection by Successive Projections Algorithm (SPA). Full spectrum models employing Partial Least Squares (PLS) and variables selection by Stepwise (SW) regression coupled with Multiple Linear Regression (MLR) and PLS models also with variable selection by Jack-Knife (Jk) were compared the proposed methodology. Several preprocessing were evaluated, being chosen derivative Savitzky-Golay with second-order polynomial and 17-point window for NIR and visible-NIR range, with offset correction. A total of 100 blends with biodiesel content between 5 and 50% (v/v) prepared starting from ten sample of biodiesel. In the NIR and visible region the best model was the SPA-MLR using only two and eight wavelengths with RMSEP of 0.6439% (v/v) and 0.5741 respectively, while in the visible-NIR region the best model was the SW-MLR using five wavelengths and RMSEP of 0.9533% (v/v). Results indicate that both spectral ranges evaluated showed potential for developing a rapid and nondestructive method to quantify biodiesel in blends with mineral diesel. Finally, one can still mention that the improvement in terms of prediction error obtained with the procedure for variables selection was significant.
这项工作涉及评估使用可见和近红外（NIR）范围，分别和组合，使用多元线性回归（MLR）和连续投影算法（SPA）的变量选择来确定生物柴油/柴油混合物中的生物柴油含量。全谱模型采用偏最小二乘（PLS）和逐步回归（SW）与多元线性回归（MLR）和 PLS 模型结合的变量选择，以及 Jack-Knife（Jk）的变量选择，比较了所提出的方法。评估了几种预处理方法，选择了二阶多项式和 17 点窗口的导数 Savitzky-Golay 进行 NIR 和可见-NIR 范围，带有偏移校正。总共制备了 100 种生物柴油含量在 5%至 50%（v/v）之间的混合物，从 10 种生物柴油样品开始。在 NIR 和可见区域，最佳模型是仅使用两个和八个波长的 SPA-MLR，RMSEP 分别为 0.6439%（v/v）和 0.5741%；而在可见-NIR 区域，最佳模型是使用五个波长的 SW-MLR，RMSEP 为 0.9533%（v/v）。结果表明，评估的两个光谱范围都显示出开发快速和非破坏性方法来定量混合物中生物柴油的潜力。最后，还可以提到，通过变量选择过程获得的预测误差的改善是显著的。
