A　near-infrared　mathematical　model　for　the　detection　of　the　total　fatty　acid　(TFA),　saturated　fatty　acid　(SFA),　and　unsaturated　fatty　acid　(UFA)　contents　in　infant　formula　was　established　using　Fourier　near-infrared　(NIR)　spectroscopy　combined　with　partial　least　squares　(PLS)　regression　prediction.　The　reliability　of　the　model　was　verified　by　cross-validation　and　external　validation.　Different　wavelengths　and　different　correction　algorithms,　including　smoothing,　vector　normalization,　first　derivative,　second　derivative,　and　multiple　scatter　correction　(MSC),　were　used　to　process　the　NIR　spectra.　The　correction　model　correlation　coefficients　(R2)　for　TFA,　SFA,　and　UFA　contents　were　0.9337,　0.9374,　and　0.9020,　respectively.　The　coefficient　residual　predictive　deviations　(RPDs)　were　3.63,　3.65,　and　2.90,　respectively.　These　data　demonstrated　that　this　NIR　mathematical　model　had　good　predictive　performance.　Twenty　collected　infant　formula　samples　were　predicted　using　the　established　model.　Paired　sample　t　test　analysis　showed　that　the　chemically　measured　and　predicted　values　of　TFAs,　SFAs,　and　UFAs　had　no　distinct　statistical　differences.　©,　2015,　South　China　University　of　Technology.　All　right　reserved.