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An integral strategy was developed for systemic screening and identifying of metabolites from multiple biological samples.The data processing method of combination of MMDF and tool Progenesis QI based on UPLC-LTQ-Orbitrap MS was applied.The workload of metabolites identification with the new approach decreased 46%.67 metabolites of hirsutine in rat multiple biological samples were identified and an in-house library was built.Comprehensive metabolic profile of hirsutine was proposed for the first time.Drug metabolites identification and construction of metabolic profile are meaningful work for the drug discovery and development. The great challenge during this process is the work of the structural clarification of possible metabolites in the complicated biological matrix, which often resulting in a huge amount data sets, especially in multi-samples in vivo. Analyzing these complex data manually is time-consuming and laborious. The object of this study was to develop a practical strategy for screening and identifying of metabolites from multiple biological samples efficiently. Using hirsutine (HTI), an active components of Uncaria rhynchophylla (Gouteng in Chinese) as a model and its plasma, urine, bile, feces and various tissues were analyzed with data processing software (Metwork), data mining tool (Progenesis QI), and HR-MSn data by ultra-high performance liquid chromatography/linear ion trap-Orbitrap mass spectrometry (U-HPLC/LTQ-Orbitrap-MS). A total of 67 metabolites of HTI in rat biological samples were tentatively identified with established library, and to our knowledge most of which were reported for the first time. The possible metabolic pathways were subsequently proposed, hydroxylation, dehydrogenation, oxidation, N-oxidation, hydrolysis, reduction and glucuronide conjugation were mainly involved according to metabolic profile. The result proved application of this improved strategy was efficient, rapid, and reliable for metabolic profiling of components in multiple biological samples and could significantly expand our understanding of metabolic situation of TCM in vivo.