Câmara Anne B F, da Silva Wellington J O, Neves Ana C de O, Moura Heloise O M A, de Lima Kassio M G, de Carvalho Luciene S
Institute of Chemistry, Federal University of Rio Grande Do Norte, Energetic Technologies Research Group, 59078-900, Natal, Brazil.
Quality Control Laboratory for Oil and Derivatives, Ativo Industrial de Guamaré (ATI), Petrobras, Rio Grande do Norte, Brazil.
Talanta. 2024 Jan 1;266(Pt 2):125126. doi: 10.1016/j.talanta.2023.125126. Epub 2023 Aug 25.
The contamination of jet fuel has gained attention in the past years as a notable factor in aircraft accidents. Identifying the contamination sources is still a challenge, especially when they have a similar composition to the fuel, such as kerosene solvent (KS). A novel analytical methodology was developed by combining a set of excitation-emission matrix (EEM) fluorescence to area constrained multivariate curve resolution with alternating least-squares (MCR-ALS) and PARAllel FACtor (PARAFAC) analysis, in order to identify KS in blends with JET-A1. For this purpose, a dataset with 50 samples (KS and JET-A1 blends, 2.0-100% v/v) was used to build the multivariate models. Both PARAFAC and MCR-ALS allowed fuel quantification with 4.64% and 3.46% RMSEP, respectively; both models (PARAFAC and MCR-ALS) could quantify KS with high accuracy (RMSEP <5.36%). In addition, MCR-ALS model was able to recover the pure spectral profiles of KS, JET-A1 and interferers. GC-MS data of the samples proved the composition similarities between both petroleum distillates, thus being inefficient for identifying the contamination. These results indicate that the development of multivariate models using EEM was the key for contributing with a new low-cost and accurate method for on-line screening of jet fuel contamination.
在过去几年中，喷气燃料污染作为飞机事故中的一个显著因素受到了关注。识别污染源仍然是一项挑战，尤其是当它们的成分与燃料相似时，例如煤油溶剂（KS）。通过将一组激发-发射矩阵（EEM）荧光与区域约束多元曲线分辨交替最小二乘法（MCR-ALS）和并行因子分析（PARAFAC）相结合，开发了一种新颖的分析方法，以识别与JET-A1混合的KS。为此，使用了一个包含50个样本（KS和JET-A1混合物，2.0-100% v/v）的数据集来建立多元模型。PARAFAC和MCR-ALS分别以4.64%和3.46%的RMSEP实现了燃料定量；两种模型（PARAFAC和MCR-ALS）都能高精度地定量KS（RMSEP<5.36%）。此外，MCR-ALS模型能够恢复KS、JET-A1和干扰物的纯光谱轮廓。样品的气相色谱-质谱（GC-MS）数据证明了两种石油馏分之间的成分相似性，因此在识别污染方面效率低下。这些结果表明，使用EEM开发多元模型是为喷气燃料污染在线筛查贡献一种新的低成本且准确方法的关键。
