以汽油、煤油和柴油为研究对象，并将其作为整体研究，不考虑其中具体组分，利用荧光光谱分析技术，提出一种通过将光谱数据矩阵首尾相连，求取其包络线，利用其表观统计量（选用标准偏差、峰度系数和偏度系数）将三维光谱降为二维光谱的方法，该方法对于光谱有一定程度重叠但光谱形状相似性较低的体系有较好的适用性。结合聚类方法，对油种进行识别，识别率达98%，依据朗伯比尔定律，测定三种油类的荧光强度与浓度的拟合曲线，并对其进行定量分析，加标回收率可达95%以上。并与目前常用的平行因子法进行分辨效率的比较，在计算时间上提高了43%。

Kerosene, diesel and gasoline are taken as research objects without considering each oil′s components. By fluorescence spectroscopy analysis, a dimension reduction method that turns three-dimensional spectrum into two-dimensional spectrum is proposed by linking the spectral datum matrixes end to end , drawing its envelope and extracting the apparent statistic (the standard deviation, coefficient of kurtosis and coefficient of skewness are chosen). The method is applical to the systems of certain degree of spectral overlap but low spectral shape similarity. The recognition rate of the oils is over 98% with clustering analysis. According to the Lambert-Beer law, fitting curve of fluorescence intensity and concentration is used for quantitative analysis and standard addition recovery rate is over 95%. Distinguish efficiency is increased by 43%, compared with the parallel factor method used commonly.