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| Electronic nosedata analysis for detection of mai | ||||||||||||
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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. |
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