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• Electronic nose technique potential monitoring ma

  overthepastyears,electronicnosetechnologyopenedthepossibilitytoexploitinformationonbehavioraromatoassessfruitripeningstage.theob-jectiveinthisstudywastoevaluatethecapacityofelectronicnosetomonitoringthechangeinvolatileproductionofmandarinduringdifferentpicking-date,usingaspeci?celectronicnosedevicepen2.principalcomponentanalysispcaandlineardiscriminantanalysisldawereusedinordertoinvestigatewhethertheelectronicnosewasabletodistinguishamongdifferentpicking-dateripenessstates.theloadingsanalysiswasusedtoidentifythesensorsresponsiblefordiscriminationinthecurrentpattern?le.theresultsobtainedprovethattheelectronicnosepen2candiscriminatesuccessfullydifferentpicking-dateonmandarinusingldaanalysis.but,electronicnosewasnotabletodetectacleardifferenceinvolatilepro?leonmandarinusingpcaanalysis.duringexternalvalidationusingldawasobtainedtoclassi?ed92%ofthetotalsamplesproperly.somesensorshavethehighestin?uenceinthecurrentpattern?leforelectronicnosepen2.asubsetoffewsensorscanbechosentoexplainallthevariance.thisresultcouldbeusedinfurtherstudiestooptimizethenumberofsensors.[詳細(xì)]

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  an!°electronicnose!±hasbeenusedforthedetectionofadulterationsofsesameoil.thesystem,comprising10metaloxidesemiconductsensors,wasusedtogenerateapatternofthevolatilecompoundspresentinthesamples.priortodifferentsupervisedpatternrecognitiontreatments,featureextractiontechniqueswereemployedtochooseasetofoptimaldiscriminantvariables.principalcomponentanalysispca,fisherlineartransformationflt,stepwiselineardiscriminantanalysisstep-lda,se-lectionbyfisherweightssfwwereused,respectively.andthen,lineardiscriminantanalysislda,probabilisticneuralnetworkspnn,backpropagationneuralnetworksbpnnandgeneralregressionneuralnetworkgrnnwereappliedaspatternrecognitiontechniquesfortheelectronicnose.asforldaandpnn,fltwasthemosteffectivefeatureextractionmethod,whilestep-ldawasthemosteffectivewayforbpnnandfltwasmoresuitableforgrnn.withonlyonesamplemisclassi?edinourexperiment,ldaismorepowerfulthanpnn.excellentresultswereobtainedinthepredictionofpercentageofadulterationinsesameoilbybpnnandgrnn.aftertrainingforsometime,bpnncouldpredicttheadulterationquantitativelymorepreciselythangrnn,whereaswithfltasitsfeatureextractionmethodandwithoutiterativetraining,grnncouldalsoyieldratheracceptableresults.?2006elsevierb.v.allrightsreserved.[詳細(xì)]

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