Various　kinds　of　adulterated　grape　seed　oil　and　concealed　adulterated　means　cause　a　severe　problem　in　food　safety　detection.　In　order　to　regulate　the　edible　oil　market,　it　is　especially　important　to　provide　a　convenient　and　reliable　method　for　identifying　the　quality　of　grape　seed　oil.　However,　traditional　methods　for　chromatography　and　mass　spectrometry　are　time　consuming,　reagent　intensive,　highly　specialized,　etc.　;　and　the　near　infrared　spectrometer　that　realizes　non-destructive　analysis　is　expensive　and　has　high　operating　environment　requirements.　Thus,　a　visible/near　infrared　spectrometer　with　low　cost　and　high　accuracy　was　designed　to　discriminate　grape　seed　oil　adulteration.　Firstly,　a　visible/near　infrared　spectrometer　hardware　platform　based　on　USB6500-Pro　detector　was　built,　and　a　simple　human-computer　interaction　interface　based　on　Qt　was　designed　to　realize　the　collection　and　processing　of　spectral　data　and　the　display　of　grape　seed　oil　adulteration　discrimination　results.　Secondly,　for　the　spectral　noise　brought　by　hardware　and　detection　environment,　wavelet　transform　was　used　to　filter　out　noise　and　reduce　spectral　distortion.　Finally,　considering　that　the　existing　quality　discrimination　models　based　on　machine　learning　often　rely　on　the　known　oil　training　sample　set　to　predict　the　different　adulterated　categories;　and　driven　by　interest　adulteration　means　will　emerge　in　endlessly　which　will　result　in　the　emerging　of　new　adulteration　categories　not　in　the　original　training　set,　the　existing　quality　identification　methods　are　difficult　to　give　accurate　results.　Therefore,　a　discrimination　method　for　known　and　new　adulterated　oil　spectra　was　designed　in　the　detection　system.　This　method　was　realized　by　two　steps　:　(1)　classification　:　the　extreme　learning　machine　(ELM)　classifier　model　was　established　by　using　the　training　set　in　the　modeling　database　to　realize　the　preliminary　judgment　of　the　preliminary　adulteration　category　;　(2)　correction:　the　automatic　clustering　algorithm　was　then　used　to　further　correct　the　prediction　result.　If　a　clustering　center　is　generated　with　the　correction　data　set,　it　is　proved　that　the　prediction　result　is　correct　and　belongs　to　the　known　adulteration　category　in　the　modeling　database;　if　two　cluster　centers　are　generated,　the　prediction　result　is　incorrect　and　the　sample　is　a　new　adulteration　category　which　does　not　appear　in　the　modeling　database.　The　result　of　the　accurate　adulterated　category　was　eventually　obtained.　In　order　to　test　the　performance　of　the　system,　five　classes　of　oil,　including　pure　grape　seed　oil,　and　grape　seed　oil　blended　with　different　proportions　of　soybean　oil,　corn　oil,　sunflower　oil　and　blend　oil　were　analyzed　by　the　visible/near　infrared　hardware　platform　and　their　spectroscopy　data　were　collected.　It　contains　30　sets　of　data　for　each　class　of　oil,　totals　150　sets.　Before　inputting　the　visible/near　infrared　spectroscopy　data　into　the　detection　system,　they　were　firstly　de-noised　by　wavelet　threshold　method　and　pre-processed　by　multiple　scattering　correction.　Assuming　that　the　first　four　classes　were　known　adulteration　class　in　the　modeling　database　and　the　fifth　class　was　new　adulteration　class,　samples　from　each　of　the　four　known　adulteration　classes　were　divided.into.20　training　sets　and　10　test　sets　by　usingK-S　algorithm.　Then,　ELM　classification　model　was　established　by　using　80　training　sets,　and　40　test　sets　were　input　into　ELM　for　preliminary　discrimination.　The　discrimination　results　were　further　analyzed　and　corrected　by　clustering.　There　was　one　clustering　center,　which　meant　that　the　ELM　model　discriminated　accurately　and　could　recognize　100%　of　the　known　classes.　However,　when　30　samples　from　the　new　adulterated　class　were　put　into　the　ELM　model,　all　of　them　were　discriminated　as　pure　grape　seed　oil.　The　discrimination　results　were　further　clustered　and　corrected.　There　were　two　clustering　centers,　which　showed　that　the　model　was　misjudged　and　the　fifth　class　was　qualitatively　determined　as　a　new　adulterated　class.　The　experimental　results　showed　that　the　designed　visible/near　infrared　spectroscopy　detection　system　was　simple　and　fast,　and　can　identify　not　only　the　known　adulteration　categories　but　also　the　new　adulteration　categories.