Clotting and anticoagulation of blood samples may give rise to different metabolic profiles of serum and plasma samples, respectively. The anticoagulant used for blood plasma preparation may affect the resulting metabolic profile due to different mechanisms involved in anticoagulation by various agents, e.g. heparin, EDTA and citrate. In the present study, we looked into metabolite and other differences in matched serum and plasma samples and different plasma preparations by using untargeted UPLC-ESI-QTOF/MS profiling and multivariate data analysis (PCA and OPLS-DA). Metabolite differences between serum and plasma samples were mainly related to small peptides reflecting presence or absence of coagulation. Only subtle metabolite differences between the different plasma preparations were noticed, which were primarily related to ion suppression or enhancement caused by citrate and EDTA anticoagulants. For the first time, we also report that anticoagulant counter cation (Na+ or K+) in Na-citrate and K-EDTA plasma can make some metabolites more dominant in ESI-MS. Polymeric material residues originating from blood collection tubes for serum preparation were observed only in serum samples. Hypoxanthine and xanthine were found at higher levels in serum than in plasma samples, possibly due to release from the clot. Mass spectral features of sodium formate and potassium formate ion clusters were detected in citrate and EDTA plasma samples, respectively, originating from formate in mobile phase and Na(+) (in Na-citrate tubes) and K(+) (in K-EDTA tubes). Among the anticoagulants, heparin is recommended for plasma samples used for LC-ESI/MS-based metabolomics of hydrophilic compounds because no plasma interferences or matrix effects were noticed for this polarity range. Citrate and EDTA should be avoided since interferences and serious matrix effects were encountered on some co-eluting polar metabolites. Serum is recommended as a second choice and an alternative to plasma. In conclusion, heparin plasma or serum should be the order of best choice for LC-ESI/MS-based metabolomics research.