A computer-implemented method of sample analysis, the method comprising: accessing at least one product ion data set generated via mass analyzing a sample comprising at least one target precursor; accessing precursor composition information for a plurality of constituent elements of the at least one target precursor, the precursor composition information comprising nominal mass information and mass defect information for at least a portion of the plurality of constituent elements; determining nominal mass -mass defect relationship information comprising NM versus MD information for a plurality of ion fragments associated with the at least one target precursor, the NM-MD relationship information based on the precursor composition information; determining at least one ion fragment upper boundary of the NM versus MD information for the plurality of ion fragments and at least one ion fragment lower boundary of the NM versus MD information for the plurality of ion fragments, the at least one ion fragment upper boundary comprising a first regression line fit between a Bmax ion and a B1 ion, the first regression line fit associated with a first upper boundary and a first lower boundary, each a threshold distance from the first regression line fit; and the at least one ion fragment lower boundary comprising a second regression line fit between a Ymax ion and a Y1 ion, the second regression line fit associated with a second upper boundary and a second lower boundary, each a threshold distance from the second firo regression line fit; extracting a plurality of candidate ion fragments from the plurality of ion fragments by applying the at least one ion fragment upper boundary and the at least one ion fragment lower boundary to the NM versus MD information and selecting the candidate product ions that were captured between the at least one ion fragment upper boundary and the at least one ion fragment lower boundary; and determining a plurality of target ion fragments from the plurality of candidate ion fragments based on fragmentation efficiency information associated with the plurality of candidate ion fragments, the plurality of target ion fragments belonging to the at least one target precursor. accessing at least one product ion data set generated via mass analyzing a sample comprising at least one target precursor; accessing precursor composition information for a plurality of constituent elements of the at least one target precursor, the precursor composition information comprising nominal mass information and mass defect information for at least a portion of the plurality of constituent elements; determining nominal mass -mass defect relationship information comprising NM versus MD information for a plurality of ion fragments associated with the at least one target precursor, the NM-MD relationship information based on the precursor composition information; determining at least one ion fragment upper boundary of the NM versus MD information for the plurality of ion fragments and at least one ion fragment lower boundary of the NM versus MD information for the plurality of ion fragments, the at least one ion fragment upper boundary comprising a first regression line fit between a Bmax ion and a B1 ion, the first regression line fit associated with a first upper boundary and a first lower boundary, each a threshold distance from the first regression line fit; and the at least one ion fragment lower boundary comprising a second regression line fit between a Ymax ion and a Y1 ion, the second regression line fit associated with a second upper boundary and a second lower boundary, each a threshold distance from the second firo regression line fit; the at least one ion fragment upper boundary comprising a first regression line fit between a Bmax ion and a B1 ion, the first regression line fit associated with a first upper boundary and a first lower boundary, each a threshold distance from the first regression line fit; and the at least one ion fragment lower boundary comprising a second regression line fit between a Ymax ion and a Y1 ion, the second regression line fit associated with a second upper boundary and a second lower boundary, each a threshold distance from the second firo regression line fit; extracting a plurality of candidate ion fragments from the plurality of ion fragments by applying the at least one ion fragment upper boundary and the at least one ion fragment lower boundary to the NM versus MD information and selecting the candidate product ions that were captured between the at least one ion fragment upper boundary and the at least one ion fragment lower boundary; and determining a plurality of target ion fragments from the plurality of candidate ion fragments based on fragmentation efficiency information associated with the plurality of candidate ion fragments, the plurality of target ion fragments belonging to the at least one target precursor.