In the present paper, the outlier detection methods for determination of oil yield in oil shale using near-infrared (NIR) diffuse reflection spectroscopy was studied. During the quantitative analysis with near-infrared spectroscopy, environmental change and operator error will both produce outliers. The presence of outliers will affect the overall distribution trend of samples and lead to the decrease in predictive capability. Thus, the detection of outliers are important for the construction of high-quality calibration models. The methods including principal component analysis-Mahalanobis distance (PCA-MD) and resampling by half-means (RHM) were applied to the discrimination and elimination of outliers in this work. The thresholds and confidences for MD and RHM were optimized using the performance of partial least squares (PLS) models constructed after the elimination of outliers, respectively. Compared with the model constructed with the data of full spectrum, the values of RMSEP of the models constructed with the application of PCA-MD with a threshold of a value equal to the sum of average and standard deviation of MD, RHM with the confidence level of 85%, and the combination of PCA-MD and RHM, were reduced by 48.3%, 27.5% and 44.8%, respectively. The predictive ability of the calibration model has been improved effectively.