The　application　of　wavelength　variable　selection　before　partial　least　squares　(PLS)　regression　to　rapidly　discriminate　the　adulteration　of　apple　juice　by　Fourier　transform　near-infrared　(FT-NIR)　was　investigated　in　this　study.　Successive　projections　algorithm　(SPA)　combined　with　four　swarm　intelligence　optimization　algorithms,　including　genetic　algorithm　(GA),　particle　swarm　optimization　(PSO),　group　search　optimizer　(GSO),　and　firefly　algorithm　(FA),　was　applied　to　extract　effective　wavelength　variables.　The　results　demonstrated　that　the　variable　number　of　SPA-PSO-PLS　models　was　validly　improved　with　a　wavelength　variable　of　four.　The　accuracy　of　model　was　satisfactory　with　the　coefficients　of　determination　of　prediction　(R-p(2)　=　0.9986)　and　good　root　mean　square　errors　of　prediction　(RMSEP　=　0.0628).　The　results　suggested　that　SPA　combined　with　swarm　intelligence　optimization　algorithms　for　wavelength　variable　selection　could　rapidly　and　efficiently　discriminate　the　adulteration　of　apple　juice.