以汽油、煤油和柴油的混合溶液为研究对象，将柴油作为干扰物，分析混合溶液中汽油和煤油的含量，提出了一种通过分析三维荧光光谱数据测量油类混合物成分及含量的新方法。该方法结合展开偏最小二乘法(U-PLS)与残差四线性分析(RQL)处理三维荧光一阶导数光谱。利用Savitzky-Golay多项式拟合微分法分别对油类混合物光谱数据的x轴和y轴求偏导，并将三维荧光光谱扩展为五维导数光谱，再通过U-PLS/RQL建立该四阶数据的校正模型，对预测样品进行分析，使光谱数据得到合理的分解与识别。预测相对误差减小到5.0%以下，预测精度高于三阶多元校正法。

The mixture of gasoline, kerosene and diesel is taken as the research object, in which diesel is considered as interferent, to analyze the content of gasoline and kerosene in the mixture. A new method is proposed to determinate the ingredient in the oil mixture and corresponding content of the component by analyzing the three-dimensional fluorescence spectral data. The method combines unfolded partial least-squares (U-PLS) with residual quadrilinearization (RQL) to process the first-order derivative three-dimensional fluorescence spectra of the oil mixture, which are extended to five-dimensional derivative spectra by using the Savitzky-Golay polynomial fitting differential to calculate the partial derivatives of the x axis and y axis of the spectral data. The calibration model of the fourth-order data is established by U-PLS/RQL to analyze the samples to be predicted, and the spectral data are decomposed and identified reasonably. The relative error of prediction is reduced to less than 5.0%, and the prediction accuracy is improved compared with that of the third-order multivariate calibration method.