Background　Mass　spectra　are　usually　acquired　from　the　Liquid　Chromatography-Mass　Spectrometry　(LC-MS)　analysis　for　isotope　labeled　proteomics　experiments.　In　such　experiments,　the　mass　profiles　of　labeled　(heavy)　and　unlabeled　(light)　peptide　pairs　are　represented　by　isotope　clusters　(2D　or　3D)　that　provide　valuable　information　about　the　studied　biological　samples　in　different　conditions.　The　core　task　of　quality　control　in　quantitative　LC-MS　experiment　is　to　filter　out　low-quality　peptides　with　questionable　profiles.　The　commonly　used　methods　for　this　problem　are　the　classification　approaches.　However,　the　data　imbalance　problems　in　previous　control　methods　are　often　ignored　or　mishandled.　In　this　study,　we　introduced　a　quality　control　framework　based　on　the　extreme　gradient　boosting　machine　(XGBoost),　and　carefully　addressed　the　imbalanced　data　problem　in　this　framework.　Results　In　the　XGBoost　based　framework,　we　suggest　the　application　of　the　Synthetic　minority　over-sampling　technique　(SMOTE)　to　re-balance　data　and　use　the　balanced　data　to　train　the　boosted　trees　as　the　classifier.　Then　the　classifier　is　applied　to　other　data　for　the　peptide　quality　assessment.　Experimental　results　show　that　our　proposed　framework　increases　the　reliability　of　peptide　heavy-light　ratio　estimation　significantly.　Conclusions　Our　results　indicate　that　this　framework　is　a　powerful　method　for　the　peptide　quality　assessment.　For　the　feature　extraction　part,　the　extracted　ion　chromatogram　(XIC)　based　features　contribute　to　the　peptide　quality　assessment.　To　solve　the　imbalanced　data　problem,　SMOTE　brings　a　much　better　classification　performance.　Finally,　the　XGBoost　is　capable　for　the　peptide　quality　control.　Overall,　our　proposed　framework　provides　reliable　results　for　the　further　proteomics　studies.