Many　traditional　Chinese　herb　medicines　containing　aristolochic　acid　(AA)　have　been　implicated　in　multiple　cancer　types,　especially　in　upper　tract　urothelial　carcinomas.　The　detection　of　AA　and　its　analogues　is　of　significant　for　the　correct　use　of　the　drugs　in　the　clinical　Chinese　medicine.　In　this　paper,　a　nondestructive　identification　method　based　on　the　near-infrared　spectroscopy　(NIRS)　technique,　and　the　support　vector　machine　(SVM)　combined　with　the　principal　component　analysis　(PCA)　is　investigated　to　rapidly　discriminate　the　AA　and　its　analogues　(denoted　as　PCA-SVM　model).　Firstly,　PCA　is　developed　to　extract　the　effective　wavelength　variables　according　to　the　loading　plots.　The　SVM　model　optimized　by　the　grid　search　algorithm　(Grid)　is　then　applied　to　establish　the　qualitative　analysis　model.　The　experimental　results　demonstrate　that　the　SVM　model　based　on　the　Grid　presents　the　excellent　discrimination　rate　(100%)　and　minimum　time　comparing　to　the　SVM　optimized　by　the　genetic　algorithm　and　particle　swarm　optimization.　Additionally,　the　variable　number　of　the　PCA　model　is　validly　reduced　with　a　wavelength　variable　of　65.　The　PCA-SVM　model　based　on　the　Grid　is　a　suitable　model　to　rapidly　and　efficiently　discriminate　AA　and　its　analogues.　©　2019　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.