关于电子鼻新技术的外文文献解读

Anovelelectronicnoseforsimultaneousquantitative

determinationofconcentrationsandodorintensityanalysisofbenzene,tolueneandethylbenzenemixturesShenJiang,JieminLiu,*DiFang,LuchunYanandChuandongWuReporter：2015.12.05September2015Volume5number96

IntroductionTheE-nosesystermVOCsGassensorarraySignalpretreatment(converter)patternrecognitionsystemresultconvertelectricalsignalstoresponsevaluesAsthemostsignificantcomponentofanartificialolfactionsystem,it'scomposedofmetaloxidesensors,CatalyticcombustiontypeandelectrochemicaltypesensorsPCA,SVM,PLSweremostusedforqualitativeanalysisofmultipleVOCs;ICA,SVDweremostappliedinquantitativeanalysisofasinglegas;ANNswerethemostcommonmethodforodoridentificationanddeterminationofodorintensityLOREMpatternrecognitonsystemBPneuralnetwork1.SensorarrayforE-nose2.E-nosesystemsetup3.Databasemeasurementmethod1.Selectionandcharacterizationofthesensorarray2.Concentrationdetermination3.OdorintensitydeterminationMaterialsandMethodsSensorarrayforE-nose:

workinggases:benzene,tolueneandethylbenzenewithapurity>99.9%(J&KChemicalTechnology,China)GC-FIDanalysiscondition:gaschromatography(GC-2014,Shimadzu,Japan)withaflameionizationdetectorandaRtx-5capillarycolumn(30m×0.25mmID,0.5μmfilmthickness).Acylindricalglasscontainer(volumeof17.3L)withahole(diameterof4cm)initslidworkedasthegasvesselbecomposedofgassensors,atemperatuer(25±0.5℃)sensorahumiditysensor(45-50%).selectsuitablesensors0.4μlworkingsolutioninjectinE-nose20mg/m3gasselectthesensorscanresponseinatleastonesolutiontargetstestthestabilityofthesensorarrayevaporatesensorarray20groupssinglegasestestrespectively.5-200mg/m3,intervalwas10mg/m3determinateconcentrationE-nosedeterminationtrainingdatabase(BPNs)testdata(intestdatabase)210groupsincluding60single,45binary105ternary.5-200mg/m380groupsincluding24single,27binary29ternary.5-200mg/m3testmodicateoptimiseGC-FIDdeterminatethesamesamples'concentrationcomparativeanalysisofGC-FLD'sandE-nose'sresults.thebestparametersoftheneuralnetworkwereascertainedandtheircodeswerewrittenintothefinalsoftwaresystem.pridictionofodorintensitytheodorsensorymethodtheodorintensityrelativeconcentrationsweresameasthetestdataeachcompoundtestedwasrespectivelyinjectedintoanolfactory-bag(3Lvolumeandfullofcleanair),whenallthecompoundshadcompletelyevaporated,anodorsamplewaspreparedbytransferringacertainquantityofthegasfromthepreviousolfactory-bagtoanewbagbyaninjector.Then6sniffingpanelistsevaluatedthetestinggasaccordingtoOIRSselecttherelativepredicationmodelsandconfirmthecontantsthen,predicationmodelswereemployedtopredicttheodorintensityandtheresultswerecomparedwiththesniffedvalues,thentheoptimummodelsweredetermined.RESULTSPART1：fig.2showsthatsuitablesensorsareMC119,MQ6,TGS2610,2M008andWSP2620.sothese5sensorsareselectedtocompriseinasensorarray.wecanfindAllRSDvalueswerelessthan7%,whichshowthattheexperimenthadgoodprecision.PART2

TheresultsshowthattheE-nosesystemcoulddeterminerespectiveconcentrationsofaromatichydrocarbonmixturessimultaneouslyandithadahighaccuracyrelativetoGC-FID.theBPneuralnetworkused'logsig'and'purelin'astransferfunctionsand'trainlm'

asthetrainingfunctionandwascomposedof210groupsoftrainingdata,a5dimensioninputlayeranda3dimensionoutputlayer,6hiddenlayersand20neuronsineverylayer.PART:3

Weber-FecherlawSothesethreemodelswereusedtopredicttheodorintensity.ThetotalAREwas5.31%,thePearsoncorrelationcoefficientwas0.947andsignificanceofpaired-sampleT-testwas0.175.Discussion(1)ComparedwithpreviousE-noses,thetestingtimeforonetestwaslessthantenminutes,whichhastheadvantageoffastdetermination.(2)TheconcentrationsweremeasuredbyaBPneuralnetworkwhiletheodorintensitywasmeasuredbyamodelprediction.therelativeerrorsofthechemicalconcentrationsandodorintensitywere9.71%and5.