The　Escherichia　coli　(E.　coli,　ATCC　25922),　Staphylococcus　aureus　(S.　aureus,　ATCC　29213),　and　Salmonella　(SE,　ATCC　14028)　are　three　common　bacterial　pathogens　of　BSIs　(Bloodstream　infection).　Accurately　identifying　these　three　bacterial　pathogens　will　greatly　help　doctors　to　reduce　the　number　of　days　to　cure　the　patients.　In　this　study,　the　identification　models　for　bloodstream　infection　are　studied.　Firstly,　Fourier　transform　near-infrared　spectroscopy　(FT-NIR)　and　multivariate　calibration　method　are　applied　to　detect　and　discriminate　these　bacterial　suspension　samples.　Four　preprocessing　methods　(multiplicative　scatter　correction　(MSC),　standard　normal　variate　correction,　Savitzky-Golay　firstderivative,　and　Savitzky-Golay　second-derivative　are　adopted　to　modify　the　raw　spectral　data.　Then,　three　discriminant　models　are　built　to　distinguish　unknown　bacterial　samples,　they　are　the　model　based　on　the　principal　component　analysis　and　mahalanobis　distance　discriminant　(PCA-MDD),　the　model　based　on　the　partial　least　squares-discriminant　analysis　(PLSDA),　and　the　back　propagation　neural　network　model.　Finally,　the　effectiveness　of　the　pre-processing　methods　and　models　are　discussed　and　the　comparison　results　are　given.　The　results　indicate　that　MSC　is　the　most　useful　pre-processing　method　for　the　bloodstream　infection　spectra.　By　using　MSC,　the　PLS-DA　model　and　the　BP　model　obtain　higher　accuracy　than　PCA-MDD.　Moreover,　although　both　the　prediction　accuracy　of　the　PLS-DA　model　and　the　BP　model　are　100%,　the　difference　between　the　predicted　values　and　the　real　value　are　the　minimal　in　the　BP　model.　Thus　BP　model　has　better　performance.　Hence,　FT-NIR　spectroscopy　combined　with　chemometrics　techniques　can　be　a　useful,　rapid,　and　nondestructive　tool　to　discriminate　clinical　bacterial　pathogens.