Near-infrared　spectroscopy　(NIRS)　combined　with　pattern　recognition　technique　has　become　an　important　type　of　non-destructive　discriminant　method.　This　review　first　introduces　the　basic　structure　of　the　qualitative　analysis　process　based　on　near-infrared　spectroscopy.　Then,　the　main　pretreatment　methods　of　NIRS　data　processing　are　investigated.　Principles　and　recent　developments　of　traditional　pattern　recognition　methods　based　on　NIRS　are　introduced,　including　some　shallow　learning　machines　and　clustering　analysis　methods.　Moreover,　the　newly　developed　deep　learning　methods　and　their　applications　of　food　quality　analysis　are　surveyed,　including　convolutional　neural　network　(CNN),　one-dimensional　CNN,　and　two-dimensional　CNN.　Finally,　several　applications　of　these　pattern　recognition　techniques　based　on　NIRS　are　compared.　The　deficiencies　of　the　existing　pattern　recognition　methods　and　future　research　directions　are　also　reviewed.