The significance of petroleum product consumption in most countries is growing owing to different reasons like urbanization, population increase, development events and life style changes, which in turn leads to prevalent environment pollution. Adulterant refers to the substance that gets added to another and may not be legally allowed in most of the cases. A petroleum fuel is one such case which is vulnerable to adulteration specifically for improving the profit margins. Detection of this petroleum fuels adulteration is challenging as they are naturally present in the compounds already. The compositional variations of these fuels are determined using various physico-chemical properties measurements. For discriminating the adulterated samples from the unaltered ones, the statistical designs along with the data mining help. Monitoring of the quality of fuel is essential at the distribution point for the prevention of adulteration. We propose to use a fuel adulterations setup that is portable, in expensive and is capable of providing the results in a short time. This includes the use of a light weight optical fiber sensor that gives high performance with low attenuation and there are no fire hazards, as well as they are resistant to harsh environments for testing. The distilled curves along with principal component analysis and support vector machine based classification helps us to build a model that is capable of this adulteration detection.

由于城市化，人口增加，发展事件和生活方式改变等不同原因，大多数国家的石油产品消费重要性正在增长，这反过来导致普遍的环境污染。掺假是指添加到另一种物质中的物质，在大多数情况下可能是法律不允许的。石油燃料就是这种情况，特别是为了提高利润率，很容易掺假。这种石油燃料掺假的检测具有挑战性，因为它们已经天然存在于化合物中。这些燃料的成分变化是使用各种物理化学性质测量值确定的。为了区分掺假样品和未更改的样品，统计设计以及数据挖掘会有所帮助。在分配点进行燃料质量监测对于防止掺假至关重要。我们建议使用便携式，昂贵的燃料掺假装置，并且能够在短时间内提供结果。这包括使用轻量级的光纤传感器，该传感器可提供高性能，低衰减，并且没有火灾隐患，并且可以抵抗恶劣的测试环境。提取的曲线以及基于主成分分析和支持向量机的分类有助于我们构建能够进行掺假检测的模型。这包括使用轻量级的光纤传感器，该传感器可提供高性能，低衰减，并且没有火灾隐患，并且可以抵抗恶劣的测试环境。提取的曲线以及基于主成分分析和支持向量机的分类有助于我们构建能够进行掺假检测的模型。这包括使用轻量级的光纤传感器，该传感器可提供高性能，低衰减，并且没有火灾隐患，并且可以抵抗恶劣的测试环境。提取的曲线以及基于主成分分析和支持向量机的分类有助于我们构建能够进行掺假检测的模型。