In　recent　years,　food　adulteration　of　various　kinds　has　become　a　severe　problem　in　food　safety　detection.　In　order　to　get　rid　of　the　limitations　of　traditional　qualitative　identification　of　new　food　adulteration,　Fourier　transform　near-infrared　spectroscopy　(FT-NIR)　was　used　to　collect　the　spectrum　ranging　from　12400　to　4000　cm(-1).　The　pure　ganoderma　lucidum　spore　oil　adulterated　with　peanut　oil,　corn　oil,　coix　seed　oil,　and　hogwash　oil　were　investigated　in　this　study,　where　the　ganoderma　lucid　urn　spore　oil　adulterated　with　hogwash　oil　was　taken　as　the　new　category　of　food　adulteration.　Then,　Multiple　Relevance　Vector　Machine　(RVM)　classifiers　were　constructed　with　calibration　samples　of　the　first　4　categories.　The　prediction　samples　and　ganoderma　lucidum　spore　oil　adulterated　with　hogwash　oil　were　discriminated　by　the　4　kinds　of　classifier.　In　addition,　the　discriminated　results　were　further　verified　with　new　clustering　algorithm.　Results　showed　that　the　discriminant　accuracy　of　the　first　four　categories　was　close　to　93.75%　with　RVM　classifier,　but　the　ganoderma　lucidum　spore　oil　adulterated　with　hogwash　oil　was　mistaken　for　pure　ganoderma　lucidum　spore　oil　because　of　the　limitations　of　model.　So　a　new　clustering　algorithm　based　on　local　density　and　distance　decision　graph　was　applied　to　verify　that.　It　was　found　that　the　cluster　centers　were　1　when　the　samples　only　contained　pure　ganoderma　lucidum　spore　oil,　however,　the　cluster　centers　were　2　when　the　samples　mixed　with　pure　ganoderma　lucidum　spore　and　adulterated　with　hogwash　oil.　The　results　demonstrated　the　FT-NIR　in　combination　with　RVM　classifier　and　new　clustering　algorithm　could　be　used　for　the　identification　of　the　adulterant　in　the　pure　ganoderma　lucidum　spore　oil　and　qualitatively　identify　new　category　of　food　adulteration,　providing　a　new　method　to　solve　the　problem　of　food　diversified　adulteration.