Rapid screening of designer fuel frauds by Raman spectroscopy

IF 4.1 Q1 CHEMISTRY, ANALYTICAL Talanta Open Pub Date : 2024-05-23 DOI:10.1016/j.talo.2024.100333
Gennaro Picardi , Fabrizio Cattaruzza , Daniela Mangione , Francesco Manzo , Alessandro Terracciano , Alessandro Proposito
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Abstract

Designer fuel fraud consists in the smuggling of modified diesel blends as engine lubricant oils and their illegal trade avoiding payment of the excise duty applied to energy products. The fraudulent mixture contains regular diesel fuel plus a heavier hydrocarbon fraction, originating from waste automotive lubricant or cheap, residual base oils.

Raman spectroscopy was tested as a rapid in-situ screening method to separate regular diesel fuel samples from those suspected to contain a heavier component, and thus demanding a more extensive characterization. The Raman fingerprint region from the screened sample is matched to purposely created spectral libraries of compliant and non-compliant diesel fuels using the instrumental search algorithm. Overall, 177 compliant fuel samples and 28 non-compliant samples (all designer fuels with a confirmed heavier fraction and/or anomalous distillation parameters) were measured. The designer fuels were all positively identified, with ∼18 % false positives.

Subsequently, the Raman data-set was studied by Principal Component Analysis (PCA) and then classified as either compliant or non-compliant using Linear Discriminant Analysis (LDA). PCA using up to three principal components for data visualization shows only an incipient separation but still a partial overlap between compliant and non-compliant samples. LDA, on the opposite, performed superiorly in the binary classification task, with no false negatives and less than 4 % false positives.

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利用拉曼光谱学快速筛查人造燃料欺诈行为
设计者燃料欺诈是指将改良柴油混合物作为发动机润滑油走私并进行非法贸易,以规避对能源产品征收的消费税。这种欺诈性混合物含有普通柴油和较重的碳氢化合物,这些碳氢化合物来自废弃的汽车润滑油或廉价的残留基础油。拉曼光谱是一种快速原位筛选方法,用于将普通柴油样品与疑似含有较重成分的柴油样品区分开来,因此需要进行更广泛的特征描述。利用仪器搜索算法,将筛选样品的拉曼指纹区域与特意创建的符合标准和不符合标准柴油的光谱库进行匹配。总体而言,共测量了 177 个符合标准的燃料样品和 28 个不符合标准的样品(均为已确认重馏分和/或馏分参数异常的特制燃料)。随后,通过主成分分析(PCA)对拉曼数据集进行了研究,然后使用线性判别分析(LDA)将其分为符合标准和不符合标准两种。使用多达三个主成分进行数据可视化的 PCA 显示,符合标准和不符合标准的样品之间只有初步的分离,但仍有部分重叠。相反,线性判别分析在二元分类任务中表现优异,没有误判,误判率低于 4%。
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来源期刊
Talanta Open
Talanta Open Chemistry-Analytical Chemistry
CiteScore
5.20
自引率
0.00%
发文量
86
审稿时长
49 days
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