Post-Graduation Program in Chemistry, Federal University of Rio Grande do Norte, Natal, 59078-900, Brazil.
School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston, PR1 2HE, UK.
Anal Bioanal Chem. 2019 Apr;411(11):2301-2315. doi: 10.1007/s00216-019-01671-y. Epub 2019 Feb 23.
Adulteration is a recurrent issue found in fuel screening. Commercial diesel contamination by kerosene is highly difficult to be detected via physicochemical methods applied in market. Although the contamination may affect diesel quality and storage stability, there is a lack of efficient methodologies for this evaluation. This paper assessed the use of IR spectroscopies (MIR and NIR) coupled with partial least squares (PLS) regression, support vector machine regression (SVR), and multivariate curve resolution with alternating least squares (MCR-ALS) calibration models for quantifying and identifying the presence of kerosene adulterant in commercial diesel. Moreover, principal component analysis (PCA), successive projections algorithm (SPA), and genetic algorithm (GA) tools coupled to linear discriminant analysis were used to observe the degradation behavior of 60 samples of pure and kerosene-added diesel fuel in different concentrations over 60 days of storage. Physicochemical properties of commercial diesel with 15% kerosene remained within conformity with Brazilian screening specifications; in addition, specified tests were not able to identify changes in the blends' performance over time. By using multivariate classification, the samples of pure and contaminated fuel were accurately classified by aging level into two well-defined groups, and some spectral features related to fuel degradation products were detected. PLS and SVR were accurate to quantify kerosene in the 2.5-40% (v/v) range, reaching RMSEC < 2.59% and RMSEP < 5.56%, with high correlation between real and predicted concentrations. MCR-ALS with correlation constraint was able to identify and recover the spectral profile of commercial diesel and kerosene adulterant from the IR spectra of contaminated blends.
掺假是燃料筛选中经常出现的问题。通过市场上应用的物理化学方法，很难检测到商用柴油被煤油污染。尽管这种污染可能会影响柴油的质量和储存稳定性，但对于这种评估缺乏有效的方法。本文评估了使用红外光谱（MIR 和 NIR）结合偏最小二乘（PLS）回归、支持向量机回归（SVR）和交替最小二乘（MCR-ALS）多变量曲线分辨率校准模型来定量和识别商用柴油中煤油掺杂物的存在。此外，还使用主成分分析（PCA）、连续投影算法（SPA）和遗传算法（GA）工具结合线性判别分析，观察了 60 个纯柴油和不同浓度煤油添加柴油燃料样品在 60 天储存期间的降解行为。添加了 15%煤油的商用柴油的理化性质仍符合巴西筛选规范；此外，指定的测试无法识别随着时间的推移混合物性能的变化。通过使用多元分类，老化水平的纯燃料和污染燃料样品被准确地分为两个明确的组，并且检测到了一些与燃料降解产物相关的光谱特征。PLS 和 SVR 能够准确地在 2.5-40%（v/v）范围内定量煤油，达到 RMSEC<2.59%和 RMSEP<5.56%，真实浓度和预测浓度之间具有很高的相关性。具有相关约束的 MCR-ALS 能够从污染混合物的红外光谱中识别和恢复商用柴油和煤油掺杂物的光谱轮廓。
