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Using fieldable spectrometers and chemometric methods to determine RON of gasoline from petrol stations: A comparison of low-field 1H NMR@80 MHz, handheld RAMAN and benchtop NIR
Fuel ( IF 7.4 ) Pub Date : 2019-01-01 , DOI: 10.1016/j.fuel.2018.09.006
Melanie Voigt , Robin Legner , Simon Haefner , Anatoli Friesen , Alexander Wirtz , Martin Jaeger

Abstract The Research Octane Number (RON) still is the major physical quantity for the characterization of fuels. Spectroscopy and multivariate data analyses have proven themselves alternatives to the traditional CFR motor. Yet, the utilization of handheld or fieldable instruments has been rarely reported rendering the feasibility of fast and simple near-pump RON determination debatable. In this study, the applicability of a handheld Raman and a portable 1 H NMR spectrometer in combination with chemometrics is demonstrated on a laboratory sample training set and compared to NIR spectroscopy. Qualitative classification of a fuel sample is achieved through Principal Component Analysis. The performance of the fieldable spectrometers using Support Vector Regression for RON prediction is found at least equivalent to earlier studies with more sophisticated and expensive instruments. The analytical method and the validated qualitative and quantitative models are then applied to samples from gas stations. The goodness of the method is expressed both in terms of computational residual mean squared errors and the common experimental reproducibility and repeatability limits. Depending on the method 40–50% of the samples are predicted within 0.2 and 80–90% with 0.7 RON.

中文翻译:

使用可现场光谱仪和化学计量学方法确定加油站汽油的 RON:低场 1H NMR@80 MHz、手持式拉曼和台式 NIR 的比较

摘要 研究辛烷值(RON)仍然是表征燃料的主要物理量。光谱学和多变量数据分析已证明它们是传统 CFR 电机的替代品。然而,很少有关于使用手持式或现场仪器的报道,这使得快速简单的近泵 RON 测定的可行性存在争议。在这项研究中,手持拉曼和便携式 1 H NMR 光谱仪与化学计量学相结合的适用性在实验室样品训练集上得到了证明,并与 NIR 光谱进行了比较。燃料样品的定性分类是通过主成分分析实现的。发现使用支持向量回归进行 RON 预测的现场光谱仪的性能至少等同于早期使用更复杂和昂贵仪器的研究。然后将分析方法和经过验证的定性和定量模型应用于加油站的样品。该方法的优点是通过计算残差均方误差和常见的实验再现性和重复性限制来表示的。根据方法的不同,40–50% 的样本预测在 0.2 以内,80–90% 的 RON 为 0.7。该方法的优点是通过计算残差均方误差和常见的实验再现性和重复性限制来表示的。根据方法的不同,40–50% 的样本预测在 0.2 以内,80–90% 的 RON 为 0.7。该方法的优点是通过计算残差均方误差和常见的实验再现性和重复性限制来表示的。根据方法的不同,40–50% 的样本预测在 0.2 以内,80–90% 的 RON 为 0.7。
更新日期:2019-01-01
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