取原油样品120个，分别按照GB/T 11133-2015和GB/T 17040-2008中所述方法测定了上述原油样品中的水分和硫的含量。通过优化的近红外光谱（NIRS）条件采集了上述原油样品的NIR光谱图。采用杠杆值算法剔除4个异常样品。在建立水分含量分析模型时，采用的条件为：用Savitzky-Golay法对光谱进行滤波预处理，建模光谱区间为6 200~8 200 cm^-1，主成分数为6，用偏最小二乘回归法（PLS）交叉验证建立分析模型。硫含量分析模型的建立条件为：采用二阶导数-Norris Derivative对光谱进行预处理，建模光谱区间为4 400~4 700 cm^-1，主成分数为6，用PLS交叉验证建立分析模型。水分和硫含量模型的预测值与测定值的相关性较好。水分模型的决定系数（R_c²）为0.989 9，校正标准偏差（RMSEC）为0.084 2，说明其预测效果较好，可用于原油中水分含量的预测。硫含量模型的R_c²为0.996 3，RESEC为0.069 6，说明此模型的预测效果也较好，可用原油中硫含量的预测。应用所建立的两个模型对10个未知原油样品中水分和硫含量进行了预测，并与其测定值比较，结果表明两者之间的相对偏差均小于10%。

Moisture and sulfur in 120 samples of crude oil were determined by the standard methods given in GB/T 11133-2015 and GB/T 17040-2008, respectively. And the transmission NIR spectra of the 120 samples were collected under the optimized condition. 4 abnormal samples were rejected by the algorithm of Leverage. Analytical model for prediction of moisture contents was established under the following conditions:① method of Savitzky-Golay was applied for wave-filtering pretreatment of the NIR spectra; ② spectal section in the range of 6 200 to 8 200 cm^-1 was selected for the model establishing; ③ number of principle components was 6; and ④ the algorithms of PLS and alternate verification were used in the final model-establishment. Conditions for establishing the analytical model for prediction of sulfur contents were as follows:① method of 2nd derivative-Norris Derivative was applied to the pretreatment of the NIR spectra; ② spectral section between 4 400 to 4 700 cm^-1 was selected for model establishing; ③ number of principle components was 6; and ④ the algorithms of PLS and alternate verification were adopted in the final model-establishment. Good correlationships between values of prediction and of determination of both the moisture and sulfur contents were obtained. And values of R_c² and RMSEC found were 0.989 9 and 0.084 2 respectively (for the model of moisture prediction), and 0.996 3 and 0.069 6 respectively (for the model of sulfur prediction), showing that those two models can be used effectively for the prediction of moisture and sulfur contents in the crude oil. Ten unknown samples of crude oil were analyzed by NIRS under the prescribed condition and contents of moisture and sulfur were predicted by the 2 models. It was found that the predicted values of the 2 components in the 10 sample were in consistent with the values of the 2 components in these samples obtained by the standard analytical methods, giving relative deviations less than 10%.