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1、ChemometricsandintelligentlaboratorysystemsELSEVIERChemometricsandIntelligentLaboratorySystems34(1996)69-83Plantseedclassificationusingpyrolysismassspectrometrywithunsupervisedlearning:Theapplicationofauto-associativeandKohonenartificialneuralnetworksRoystonGoodacre*,Jo
2、sephPygall,DouglasB.Kell1InstituteofBiologicalSciences,UniversityofWales,Aberystwyth,DyfedSY233DA,Wales,UKReceived3November1995;accepted29March1996AbstractPyrolysismassspectrometry(PyMS)wasusedtogainhighdimensional(150m/zvalues)biochemicalfingerprintsfromBegoniasemperfl
3、orensSummerRainbow,CampanulacarpaticaWhiteGem,LobeliaerinusWhiteFountain,andLobeliaerinusWhiteLadyplantseeds.Ratherthanhomogenizingtheseedsandanalysingtheextracts,thesamplepreparationoftheseedsinthisstudywasnovelandmerelyinvolvedcrimpingthemetalfoilsamplecarrieraroundth
4、eseeds.Comparedtoextractiveproceduresthetechniqueexploitedinthisstudywillgiveafairrepresentationoftheseed,israpidandthusamenabletotheanalysisofahighvolumeofsamples.Toobservetherelationshipbetweentheseseeds,basedontheirspectralfinger-prints,itwasnecessarytoreducethedimen
5、sionalityofthesedatabyunsupervisedfeatureextractionmethods.Theneuralcomputationalpatternrecognitiontechniquesofselforganisingfeaturemaps(SOFMs)andauto-associativeneuralnetworkswerethereforeemployedandtheclustersobservedcomparedwiththegroupsobtainedfromthemoreconventiona
6、lstatisticalapproachesofprincipalcomponentsanalysis(PCA)andcanonicalvariatesanalysis(CVA).WhenPCAwasusedtoanalyzetherawpyrolysismassspectrareplicatesampleswerenotrecoveredindiscreteclusters;CVA,whichminimisesthewithin-groupvarianceandmaximisesthebetween-groupvariance,th
7、ereforehadtobeemployed.AlthoughB.semperflorensandC.carpaticaseedswererecoveredseparatelyandawayfromtheL.erinusplantseeds,thetwotypesofL.erinusseedscouldstillnotbediscriminatedbetweenusingthisapproach.CVAusesaprioriinformationonwhichspectraarereplicates;wethereforeencode
8、dthisinformationbyemployinganovelpreprocessingregimewherethetriplicatemassspectrafromeachoftheseedswereaverage
