Peptide identification using tandem mass spectra through database searching is a key component in proteomic research. In this study, we developed a model-based scoring algorithm for peptide identification using mixture models. It effectively takes account of the spectral noise and incorporates the spectral intensity. In addition, our approach also provides a natural confidence measure for assessing the uncertainty of the score. We will present our results on several public available benchmark datasets.