dataprocess.m

Cyranose@320便携式电子鼻的检测数据,对象是中药材.在MATLAB上进上气味指纹图谱的分析,其4品种的中药材.结果表明其能将其完全分类.

3.08255533	2.65387914	5.2977821	4.70029564	0.82891155	3.11740586	2.18657687	5.82502334	2.07243397;
3.08262861	2.65381342	5.29836743	4.70054551	0.82889362	3.11708898	2.18652839	5.82616925	2.07259522;
3.0823491	2.65390402	5.29772737	4.6999402	0.82889337	3.11731018	2.18678855	5.82644968	2.07231725;
3.08241255	2.65390466	5.29840544	4.70075803	0.82890781	3.11758345	2.18682348	5.82661168	2.07236176;
3.08294745	2.65390402	5.29875517	4.70071295	0.82894567	3.11749313	2.18696322	5.82717611	2.07257247;
3.08234985	2.65384341	5.29844042	4.70029951	0.82897058	3.11766383	2.18682713	5.82686427	2.07273917;
3.08244126	2.65387786	5.29796757	4.70106203	0.82902936	3.1173829	2.18685633	5.82725974	2.07271691;
3.08224938	2.65408205	5.29801318	4.70084304	0.82908292	3.11761331	2.18693454	5.82760471	2.07265953;
3.0822932	2.65395889	5.29865785	4.70009731	0.82901168	3.11777178	2.18693715	5.8271012	2.07269811;
3.08220179	2.65381789	5.2993361	4.70027375	0.82901766	3.11792872	2.18687145	5.82693744	2.07286977;
3.08255986	2.65375855	5.29899848	4.70069234	0.8290336	3.11785982	2.18679324	5.827089	2.07276885;
3.08246694	2.65392572	5.29911254	4.70037292	0.82901193	3.11778938	2.18694653	5.82688866	2.0728485;
3.08249716	2.65405971	5.29979695	4.70088555	0.82900969	3.11782536	2.18690586	5.82789744	2.07297515;
3.08237478	2.65395889	5.30007988	4.70053134	0.82903958	3.11769828	2.18679376	5.82794623	2.0729044;
3.08258857	2.65398761	5.29971786	4.70062279	0.82912452	3.11794327	2.1871499	5.82749843	2.07295041;
3.08233625	2.65392061	5.300232	4.70032269	0.82908242	3.11800834	2.18698564	5.82830869	2.07305283;
3.08224863	2.65397293	5.30007835	4.70006641	0.82920822	3.11786747	2.18687927	5.82794797	2.07317157;
3.08259839	2.65409481	5.30027915	4.70085721	0.82917085	3.11796164	2.18692985	5.82827907	2.07299742;
3.08251831	2.65405716	5.29996275	4.70011019	0.82913996	3.11801217	2.18676508	5.82799154	2.07314387;
3.08237025	2.65408779	5.29954598	4.70044633	0.82919875	3.11795322	2.18702735	5.82780509	2.07296823;
3.08243219	2.65395826	5.29908364	4.7002158	0.82915765	3.11794709	2.18719161	5.82810306	2.07311467;
3.08243219	2.65413054	5.29991559	4.70002391	0.82919925	3.11788585	2.18724637	5.82826687	2.07321907;
3.08260142	2.65432709	5.29983498	4.70052361	0.82915391	3.11800145	2.18706594	5.82812049	2.07309587;
3.08256591	2.65438197	5.30034913	4.70079538	0.82914669	3.1180627	2.18704665	5.82848993	2.07310676;
3.08242313	2.65423583	5.30068687	4.70070908	0.82912377	3.11808797	2.18697156	5.82840803	2.07289649;
3.08256138	2.65422179	5.29996427	4.70109294	0.82911306	3.11793331	2.18683808	5.82839234	2.07300978;
3.08242313	2.65422371	5.29992472	4.70039481	0.8291716	3.117967	2.18690951	5.82813094	2.07311319;
3.08273136	2.6544343	5.30037956	4.70086752	0.82912178	3.11813621	2.1869262	5.8287287	2.07314832;
3.08238158	2.65428242	5.30126353	4.70120631	0.82909886	3.11789503	2.1869189	5.82770229	2.07310428;
3.08261653	2.65433347	5.29998404	4.70095511	0.82911879	3.11796394	2.18685894	5.82816928	2.07290193;
3.08235816	2.65412097	5.29995514	4.70125011	0.82914445	3.11755972	2.18696061	5.82877924	2.07283069;
3.08263239	2.65424732	5.2995688	4.70089328	0.82917982	3.11795934	2.18700702	5.82873567	2.07311121;
3.08267621	2.65417776	5.3002183	4.70111227	0.82922292	3.11793025	2.18707428	5.82810829	2.07314782;
3.0824118	2.65414458	5.29942887	4.70058415	0.82919552	3.11787283	2.18708576	5.82859276	2.07293508;
3.08254022	2.65413246	5.29986996	4.70033943	0.82919552	3.11796088	2.18720726	5.82822679	2.07285691;
3.08235816	2.65409672	5.30022743	4.70033299	0.82916587	3.11804127	2.18699346	5.82848819	2.07303353;
3.08294367	2.65379811	5.30092118	4.70043216	0.82917783	3.11778479	2.18706907	5.82893959	2.07316168;
3.08275252	2.65415415	5.30019549	4.70039739	0.82926751	3.11783149	2.18711913	5.8286189	2.07295833];
%川薄荷
sb=[3.16717635	2.69558107	5.45930049	4.89144533	0.84947537	3.12199412	2.28010218	6.34288137	2.14860722
3.16713736	2.69529875	5.46028574	4.89120051	0.84949531	3.12198798	2.28014296	6.34628999	2.14872097
3.16715841	2.69542206	5.45870248	4.89052056	0.84954681	3.12223566	2.28045401	6.34752621	2.14912375
3.1670586	2.69532406	5.45897054	4.89032476	0.84960766	3.12235606	2.28071235	6.35025071	2.14938462
3.16735181	2.69509239	5.45888647	4.88951854	0.84963114	3.12229778	2.28098704	6.35193686	2.14946048
3.16729878	2.69516183	5.45840589	4.8900066	0.84969249	3.12248413	2.28141028	6.35383926	2.14967219
3.16765208	2.69530265	5.45859463	4.88988696	0.8498046	3.12251251	2.28154304	6.35595228	2.1497701
3.16764974	2.69529486	5.45865649	4.88946689	0.84987051	3.12240898	2.28168179	6.35728434	2.15019461
3.16756005	2.69535781	5.45911648	4.88933231	0.84993743	3.12267817	2.28189455	6.35887424	2.15013769
3.16790558	2.69538507	5.45901178	4.88878998	0.84998036	3.12254242	2.28235444	6.36111853	2.15053202
3.16833698	2.69559924	5.45926559	4.88899793	0.85005536	3.1226636	2.28242084	6.36426211	2.15055356
3.16840719	2.69538117	5.45815056	4.88807652	0.8502463	3.12250714	2.28291078	6.3655313	2.15102592
3.16844932	2.69543764	5.45908475	4.88884978	0.85018316	3.12273953	2.2831498	6.3673703	2.15090641
3.16860849	2.69530654	5.45919579	4.88826948	0.85030995	3.12284537	2.28338448	6.36891234	2.15118084
3.16856011	2.69561352	5.45871676	4.88787677	0.85034001	3.12267357	2.28376243	6.37164849	2.15126445
3.16877234	2.69555705	5.45920848	4.88782922	0.85038674	3.12286685	2.28370688	6.37262186	2.15158151
3.16887924	2.69554213	5.45861683	4.88727761	0.85051103	3.12284767	2.28400426	6.37435062	2.1513886
3.16894713	2.69529162	5.45892772	4.88691218	0.85048375	3.12291594	2.28388988	6.37711012	2.15154046
3.1689682	2.69534873	5.45958763	4.88710236	0.85056863	3.12306168	2.28431313	6.37885064	2.15185755
3.16914925	2.69538442	5.45986686	4.88650876	0.85062143	3.1227449	2.28464928	6.38043263	2.15179598
3.16899473	2.6953351	5.45936712	4.8867451	0.85065731	3.12296733	2.28474408	6.38231985	2.15232456
3.16935372	2.69544672	5.45977008	4.88692441	0.85079678	3.12294816	2.28466835	6.38403061	2.15242977
3.16940913	2.69560508	5.46020958	4.88685513	0.85080587	3.12344908	2.28489992	6.38500479	2.15257606
3.16953557	2.69519298	5.46041586	4.88599266	0.85089987	3.12323965	2.28518439	6.38694783	2.15277985
3.16955118	2.69576216	5.45984147	4.88645036	0.85099716	3.12338081	2.28528194	6.38918691	2.153035
3.16997503	2.69570634	5.45957177	4.88562737	0.85108283	3.12329028	2.28583683	6.39103009	2.15337388
3.17057541	2.6959504	5.46004297	4.88661199	0.85110355	3.12357644	2.2862708	6.39343699	2.15341188
3.17026857	2.69611593	5.46011279	4.88633083	0.85119226	3.12334705	2.2862599	6.39592114	2.1537539
3.17094085	2.69617955	5.45964316	4.8863743	0.85129892	3.12359792	2.28654944	6.39734758	2.15390849
3.17062928	2.69620422	5.46108238	4.88675189	0.85130069	3.12347977	2.28687229	6.39913628	2.15424904
3.17066832	2.69610035	5.46071736	4.88747868	0.85141721	3.12353118	2.28694101	6.40056579	2.15437798
3.17110797	2.69597766	5.46087447	4.88718523	0.85136084	3.1236148	2.28722682	6.40210978	2.15458554
3.17083309	2.69592249	5.46105381	4.88692848	0.85150164	3.12377516	2.28756832	6.4031601	2.15460815
3.17129775	2.69613411	5.46068721	4.88748683	0.85160099	3.12387875	2.28776254	6.4053776	2.15463897
3.17165705	2.69631199	5.46159663	4.88698418	0.85163663	3.12395471	2.28782365	6.4059702	2.15472272
3.17148677	2.69626914	5.46113316	4.88675733	0.85164675	3.12385419	2.28793933	6.4078326	2.15490102
3.17147583	2.69627629	5.46097287	4.88632268	0.85167506	3.12409207	2.28799389	6.40886027	2.15514511
3.17144849	2.6961445	5.46046029	4.88657124	0.85170085	3.12407979	2.2879928	6.41058562	2.1550264
3.17166486	2.69616917	5.46053487	4.88693256	0.85180577	3.12425706	2.28828748	6.41209311	2.15540979
3.171568	2.69616462	5.46044283	4.88676819	0.85184901	3.12419643	2.28843429	6.41354898	2.15530494
3.1714977	2.69617631	5.46009057	4.8863743	0.85169959	3.12401994	2.28840918	6.41324012	2.1554982
3.17154222	2.69626525	5.46032541	4.88558935	0.85180704	3.12413811	2.28863132	6.41419285	2.155491
3.17168907	2.69614644	5.45977008	4.88534629	0.85182853	3.12420334	2.28861767	6.41558923	2.15563081
3.17163049	2.69592703	5.46000013	4.88493355	0.85192638	3.12423097	2.28890861	6.41638393	2.15572025
3.17131103	2.6961023	5.45988748	4.88533407	0.85195597	3.1242256	2.28906365	6.41727713	2.15582615
3.17148442	2.69600168	5.45979229	4.88383132	0.85190186	3.1244589	2.28907894	6.41790148	2.15575521
3.17157503	2.69613801	5.45966695	4.88429823	0.85192866	3.12455023	2.28919358	6.41976302	2.15597112
3.17161018	2.69583291	5.4587802	4.88341739	0.85196431	3.1247237	2.28912043	6.42031943	2.1563156
3.17122356	2.69569206	5.45966378	4.88281898	0.85202449	3.12476054	2.28913626	6.42010449	2.15614952
3.17120169	2.69596273	5.4594623	4.88252863	0.8520202	3.12470067	2.28908276	6.42110897	2.15641381
3.1715555	2.69589977	5.45893723	4.88302115	0.85206723	3.12467841	2.28927493	6.4225518	2.15633565
3.17179296	2.69605361	5.4583488	4.8827145	0.85207608	3.12457326	2.28935082	6.4242183	2.15659276
3.17197889	2.69603868	5.45904985	4.88316906	0.85209657	3.12477052	2.28972213	6.42538463	2.15673932
3.1723453	2.69622305	5.45967489	4.88301301	0.85214588	3.12467687	2.28980186	6.42728561	2.15657373
3.17236015	2.69658079	5.46004297	4.88422222	0.85227587	3.12489947	2.29006455	6.42882311	2.15683344
3.17245547	2.69664637	5.46004456	4.88400776	0.85230749	3.1250008	2.29029341	6.43069741	2.15725264
];
data=[gd;ch;jh;sb];

stdr=std(data);
sr=data./stdr(ones(223,1),:);
[pcs,newdata,variances,t2]=princomp(sr);

analy=newdata;

max1=max(max(analy));
min1=min(min(analy));

for n=1:223
    for  m=1:9
    data(n,m)=(newdata(n,m) - min1)/(max1 - min1);
    end 
end

% plot(newdata(1:55,1),newdata(1:55,2),'*');
figure;
plot(newdata(:,1),newdata(:,2),'+');
ylabel('PCA2 - 4.16%');
xlabel('PCA1 - 95.79%');
grid on;
% 
% axis equal;
% grid on;
% figure;
% d1=newdata(1:1,:);d2=newdata(55+2:55+3,:);
% d3=newdata(55+56+3:55+56+5,:);d4=newdata(55+56+56+5:55+56+56+20,:);
% da=[d1;d2;d3;d4];
% plot(da(:,1),da(:,2),'+');
% figure;
% 
% plot3(newdata(:,1),newdata(:,2),newdata(:,3),'+');