In　order　to　rapidly　determine　the　content　of　raw　juice　in　blending　pear　juice　by　near-infrared　spectroscopy　(NIR),　experiments　using　the　same　soluble　solids　content　of　fresh　pear　juice　and　juice　powder　were　conducted.　Four　common　swarm　intelligence　optimization　algorithms,　including　Genetic　Algorithm　(GA),　Particle　Swarm　Optimization　(PSO),　Glowworm　Swarm　Optimization　(GSO)　and　Firefly　Algorithm　(FA),　were　combined　with　PLS　to　select　wavelength　variables.　The　results　showed　that　the　four　kinds　of　models　could　remove　most　of　the　wavelength　variables,　and　the　FA-PLS　model　achieved　the　optimal　performance,　which　simplified　the　model　and　improved　the　accuracy　of　prediction.　Then,　the　successive　projections　algorithm　(SPA)　was　used　to　select　wavelength　variables　after　Firefly　Algorithm　(FA).　The　results　indicated　the　generalization　ability　were　as　follow:　FA-PLS＞PLS＞　FA-SPA-PLS＞SPA-PLS.　The　root　mean　square　errors　of　prediction　(RMSEP)　was　0.　029　1,　0.　033　3,　0.　033　9,　0.　137　0,　respectively,　and　the　corresponding　wavelength　variables　number　were　367,　765,　20,　18.　The　wavelength　variables　of　SPA-PLS　model　were　the　least,　but　RMSEP　was　much　higher　than　the　other　three　models.　Considering　the　prediction　precision　and　the　number　of　wavelength　variables,　the　FA-SPA-PLS　model　was　validly　improved　with　less　wavelength　variables　and　higher　prediction　accuracy.　This　study　provides　a　convenient　way　for　rapid　identification　of　blending　fruit　juice　using　NIR.