利用拉曼光谱检测技术，对甲醇柴油的甲醇含量和黏度进行定量检测研究。93个甲醇柴油样品作为被检测的对象，划分校正集（72个）和预测集（21个）。分析比较了光谱的不同预处理方法的全交互验证偏最小二乘（PLS）模型效果；然后以最优预处理方法得到的光谱数据为输入，结合连续投影算法(SPA)建立不同的回归校正模型，并进行比较分析。结果表明，甲醇含量的多元散射校正偏最小二乘(MSC-PLS)模型预测效果最优，其校正集相关系数RC为0.9761，交互验证相关系数RCV为0.9551，校正集均方误差（RMSEC）为1.5089，交互验证均方误差（RMSECV）为2.0630；黏度的MSC-PLS模型预测效果也是最优的，RC为0.9794，RCV为0.9580，RMSEC为0.0907 mPa·s，RMSECV为0.1292 mPa·s。

Methanol content and viscosity of methanol diesel oil were quantitatively detected by Raman spectroscopy. As the detected objects, 93 methanol diesel samples were divided into the calibration set (72 samples) and the prediction set (21 samples). The spectral full cross validation partial least squares (PLS) model were analyzed and compared with different pretreatment methods; The spectral data obtained by the optimal pretreatment method is used as inputs, combining with the successive projections algorithm（SPA）to establish different regressive correction models, and these models were comparatively analyzed. The results show that the multiplicative scatter correction partial least squares (MSC-PLS) model for prediction of methanol content is optimal. In this model, the calibration correlation coefficient RC is 0.9761, the interactive validation correlation coefficient RCV is 0.9551, the root mean square error of calibration set (RMSEC) is 1.5089, and the root mean square error of cross-validation（RMSECV）is 2.0630. For viscosity determination, MSC-PLS model also has the best prediction performance, whose RC is 0.9794, RCV is 0.9580, RMSEC is 0.0907 mPa·s, and RMSECV is 0.1292 mPa·s.