The accuracy of the prediction model is affected by the near-infrared spectrum of flour and flour ash contents was quantitative analyzed. While the presence of outlier data seriously interfere with the reliability of the model, therefore, it is essential to identify and deal with the outlier samples to improve the predictive ability. Mahalanobis distance and the Monte Carlo cross validation (MCCV) methods were used to remove the outlier samples. When the weight coefficient was 1.5, excluding sample number was 3 with the former method it could get the best results, and the related coefficient (R²) was 92.67, cross-validation mean square error (RMSECV) was 0.0485. While with the latter method the correlation coefficient (R²) was 94.64, cross-validation mean square error (RMSECV) was 0.0411.Therefore, Mahalanobis distance method can improve the calibration model and prediction accuracy to a certain extent, while the calibration model and prediction accuracy of MCCV without outliers samples was better than that of the Mahalanobis distance method.