A methodology was developed to monitor the adulteration of the B10 blend of diesel and crambe biodiesel using proton nuclear magnetic resonance (¹H NMR) spectroscopy combined with data driven soft independent modeling of class analogy (DD-SIMCA) model. The training was performed only with samples of the target class (B10) while the validation was performed with a test set consisting of new samples of the target class (B10) and samples of B10 adulterated with crambe oil, used frying oil, and residual automotive lubricating oil. The efficiency of this methodology was characterized based on the sensitivity parameters for the training set and specificity for the test set, in which a value of 1 was obtained for both parameters. This sensitivity value for the training set indicates that no target class samples were classified as extreme or outliers. The specificity for the test set shows that all samples in the test set were correctly classified into their respective classes, demonstrating the high efficiency of the DD-SIMCA model in monitoring adulterants in B10 mixture of diesel and crambe biodiesel. The DD-SIMCA model is simpler to construct than the multivariate control chart and the partial least squares discriminant analysis (PLS-DA) because its development does not require prior information about the adulterants. The excellent obtained results in the application of this model suggest that this analytical methodology is efficient, feasible and suitable for use by inspection agencies to characterize the quality of this fuel.

开发了一种使用质子核磁共振来监测柴油和克拉姆贝生物柴油B10混合物掺假的方法（¹1 H NMR）光谱与数据驱动的类比模拟独立独立建模（DD-SIMCA）模型结合使用。仅使用目标类别（B10）的样本进行培训，而验证使用测试集进行，该测试集包括目标类别（B10）的新样本以及掺有蛤be油，用过的煎炸油和残留汽车用品的B10样本润滑油。该方法的效率基于训练集的敏感性参数和测试集的特异性进行了表征，其中两个参数的值均为1。训练集的此敏感性值表明没有目标类别样本被分类为极端或离群值。测试集的特异性表明，测试集中的所有样本均已正确分类为各自的类别，展示了DD-SIMCA模型在监测柴油和克拉姆贝生物柴油B10混合物中的掺假物中的高效率。DD-SIMCA模型比多元控制图和偏最小二乘判别分析（PLS-DA）更易于构建，因为其开发不需要有关掺假的先验信息。在该模型的应用中获得的优异结果表明，这种分析方法是高效，可行的，并且适合检查机构用来表征这种燃料的质量。DD-SIMCA模型比多元控制图和偏最小二乘判别分析（PLS-DA）更易于构建，因为其开发不需要有关掺假的先验信息。在该模型的应用中获得的优异结果表明，这种分析方法是高效，可行的，并且适合检查机构用来表征这种燃料的质量。DD-SIMCA模型比多元控制图和偏最小二乘判别分析（PLS-DA）更易于构建，因为其开发不需要有关掺假的先验信息。在该模型的应用中获得的优异结果表明，这种分析方法是高效，可行的，并且适合检查机构用来表征这种燃料的质量。