The near-infrared spectroscopy (NIR) was combined with pattern recognitions method and applied to the quality assessment of toothpaste samples of four different brands. Several chemometrics approaches, such as principal component analysis (PCA), clustering analysis (CA), partial least squares (PLS), artificial neural networks (ANN) and K-nearest neighbor (kNN) were used to investigate the quality of toothpastes samples. The obtained results showed that the four clustering groups can be observed after the pretreatment of multiple scatter correction for the NIR data. It was also found that the quality of toothpastes of all the four brands was relatively stable, however, there is a significant difference in the quality between two brand kinds of toothpaste samples.