Electronic nose and data analysis for detection of m

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Electronic nose and data analysis for detection of m
北京盈盛恒泰科技有限责任公司更新于：2008-05-30

应用行业：玩具/文化/生活用品文体用品

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北京盈盛恒泰科技有限责任公司是一家专业从事食品分析检测仪器和环境应急检测仪器的销售技术支持和售后服务工作公司目前是日本INSENT日本JWP意大利VELP美国FTC德国AIRSENSE德国OWR美国UNITY美国Navas美国FiltationEngineering等世界仪器制造商的ZG区营销合作伙伴和技术服务ZX，产品覆盖食品感官分析食品营养分析食品安全检测及环境应急监测等公司在香港北京上海广州成都海口银川等地设有分公司和办事机 An!°electronicnose!±hasbeenusedforthedetectionofadulterationsofsesameoil.Thesystem,comprising10metaloxidesemiconductsensors,wasusedtogenerateapatternofthevolatilecompoundspresentinthesamples.Priortodifferentsupervisedpatternrecognitiontreatments,featureextractiontechniqueswereemployedtochooseasetofoptimaldiscriminantvariables.Principalcomponentanalysis(PCA),Fisherlineartransformation(FLT),stepwiselineardiscriminantanalysis(Step-LDA),selectionbyFisherweights(SFW)wereused,respectively.Andthen,lineardiscriminantanalysis(LDA),probabilisticneuralnetworks(PNN),backpropagationneuralnetworks(BPNN)andgeneralregressionneuralnetwork(GRNN)wereappliedaspatternrecognitiontechniquesfortheelectronicnose.AsforLDAandPNN,FLTwasthemosteffectivefeatureextractionmethod,whileStep-LDAwasthemosteffectivewayforBPNNandFLTwasmoresuitableforGRNN.Withonlyonesamplemisclassi?edinourexperiment,LDAismorepowerfulthanPNN.ExcellentresultswereobtainedinthepredictionofpercentageofadulterationinsesameoilbyBPNNandGRNN.Aftertrainingforsometime,BPNNcouldpredicttheadulterationquantitativelymorepreciselythanGRNN,whereaswithFLTasitsfeatureextractionmethodandwithoutiterativetraining,GRNNcouldalsoyieldratheracceptableresults.?2006ElsevierB.V.Allrightsreserved. 电子鼻-Z传感器生产商产品

内容节点: 概述; 实验/设备条件; 样品提取; 实验/操作方法; 实验结果/结论; 仪器/耗材清单

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