Near-infrared spectroscopy is a fast and efficient analytical technique based on multivariate calibration model, which correlates near-infrared spectra with the property of samples (such as concentration). The reliability of analytical results depends mostly on the accuracy of measured spectra. But outliers do not make for reliable data. The authors combined RHM (Resampling by Half-Means) with SHV (Smallest Half-Volume) method to detect the outliers of the near-infrared spectra of milk samples, and the results were satisfactory. The performance of the new method is superior to the traditional outliers detecting algorithms such as Mahalanobis distances and hat matrix leverage. And this combined method is simple and fast to use, conceptually clear, and numerically stable, so it is recommended to be used for the detection of multiple outliers in multivariate data, especially the online measurement and discriminant analysis.