Matrix-assisted　laser　desorption/ionization　mass　spectrometry　(MALDI-MS)　has　become　a　robust　tool　for　analyzing　a　variety　of　biomacromolecules.　However,　the　strong　background　interference　produced　by　conventional　organic　matrices　hinders　the　detection　of　small　molecule　analytes,　which　restricts　the　widespread　application　of　MALDI-MS　in　metabolomics　studies.　Consequently,　developing　new　organic　matrices　is　urgently　needed　to　overcome　these　issues.　In　this　study,　1,4-Dioxo-1,2,3,4-tetrahydrophthalazine-6-carboxylic　acid　(DTCA)　was　firstly　employed　as　a　new　matrix　for　MALDI-MS　to　enhance　the　detection　of　low　molecular　weight　compounds　because　of　its　strong　UV　absorption,　less　matrix　background　interference,　high　ionization　efficiency　for　metabolites,　and　good　reproducibility.　Considering　these　advantages,　DTCA　was　used　to　analyze　endogenous　metabolites　in　the　serum　samples　of　imidacloprid　(IMI)-exposed　mice　via　MALDI-MS　in　positive　ion　mode.　By　combining　with　machine　learning,　the　differentiation　between　imidacloprid-exposed　mice　and　control　mice　was　successfully　achieved,　and　39　metabolites　were　estimated　as　potential　biomarkers.　Additionally,　potentially　disrupted　metabolic　pathways　were　revealed.　These　results　indicate　that　DTCA,　as　a　new　and　powerful　matrix　in　positive　ion　mode,　has　great　potential　for　applications　in　the　detection　of　small　molecules.　©　2025　Elsevier　B.V.