patent wo2007047299a1 - classification of fabrics by near-infrared spectroscopy - google patents

5/5/2015 PatentWO2007047299A1ClassificationoffabricsbynearinfraredspectroscopyGooglePatents

http://www.google.com/patents/WO2007047299A1?cl=en 1/6

Patents

Publicationnumber WO2007047299A1Publicationtype ApplicationApplicationnumber PCT/US2006/039702Publicationdate Apr26,2007Filingdate Oct11,2006Prioritydate Oct13,2005

Alsopublishedas US8190551,US20100036795

Inventors KennethWBusch,ChristopherBDavis,MariannaBusch

Applicant UnivBaylor,KennethWBusch,ChristopherBDavis,MariannaBusch

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PatentCitations(3),NonPatentCitations(2),Referencedby(2),Classifications(6),LegalEvents(4)

ExternalLinks:Patentscope,Espacenet

CLAIMS(OCRtextmaycontainerrors)

WHATISCLAIMEDIS:

1.Amethodforclassifyingunknownfabricsamples,comprising:collectingspectraldataofanunknownfabricsampletogiveunknownspectraldataandusingaknownstandardrepresentingknownfabrictypesandsoftindependentmodelingofclassanalogy("SIMCA")toclassifytheunknownfabricsampleintoeitheroneormoreoftheknownfabrictypesoratypeofunknownorigin.

2.Themethodofclaim1,whereintheknownstandardrepresentingknownfabrictypesisadatabaseofmodelprincipalcomponentanalysesforeachknownfabrictype.

3.Themethodofclaim2,whereinthedatabaseofmodelprincipalcomponentanalysesforeachknownfabrictypeispreparedby:collectingspectraldataofapluralityoffabricsamples,whereinthefabricsamplesareofknownfabrictypesandperformingaprincipalcomponentanalysisandaregressionofthespectraldataforeachofthefabricsamplesofeachknownfabrictypetocreateadatabaseofmodelprincipalcomponentanalysesforeachknownfabrictype.

4.Themethodofclaim1,whereinthespectraldataisdiffusenearinfraredreflectionspectraldata.

5.Themethodofclaim1,whereintheknownfabrictypescompriseoneormoreofacetate,acrylic,blends,cotton,linen,mohair,nylon,olefin,polyester,PVC,rayon,silk,andwool.

6.Amethodforclassifyingunknownfabricsamples,comprising:collectingspectraldataofapluralityoffabricsamples,whereinthefabricsamplesareofknownfabrictypesperformingaprincipalcomponentanalysisandaregressionofthespectraldataforeachofthefabricsamplesofeachknownfabrictypetocreateadatabaseofmodelprincipalcomponentanalysesforeachknownfabrictypecollectingspectraldataofanunknownfabricsampletogiveunknownspectraldataandusingthedatabaseofmodelprincipalcomponentanalysesandsoftindependentmodelingofclassanalogy("SIMCA")toclassifytheunknownfabricsampleintoeitheroneormoreoftheknownfabrictypesoratypeofunknownorigin.

7.Themethodofclaim6,whereinthespectraldataisdiffusenearinfraredreflectionspectraldata.

8.Themethodofclaim6,whereintheknownfabrictypescompriseoneormoreofacetate,acrylic,blends,cotton,linen,mohair,nylon,olefin,polyester,PVC,rayon,silk,andwool.

ClassificationoffabricsbynearinfraredspectroscopyWO2007047299A1

ABSTRACT

Amethodforclassifyingtextilesamplesandunknownfabricsintoknowncategoriesusingspectroscopy,chemometricmodeling,andsoftindependentmodelingofclassanalogies('SIMCA').Themethodinvolvescollectingspectraldata,preferablydiffusenearinfraredreflectancedata,foralibraryofknownfabricsamples,creatingadatabaseofprincipalcomponentanalysesforeachtypeoffabric,andusingSIMCAtoclassifyanunknownfabricsampleaccordingtothedatabase.

DESCRIPTION(OCRtextmaycontainerrors)

CLASSIFICATIONOFFABRICSBYNEARINFRAREDSPECTROSCOPY

BACKGROUND

[0001]ThisapplicationclaimsprioritytoU.S.ProvisionalPatentApplication,SerialNumber60/726,452,entitled"CLASSIFICATIONOFFABRICSBYNEARINFRAREDSPECTROSCOPY"filedonOctober13,2005,havingK.Busch,C.Davis,andM.Busch,listedastheinventor(s).

[0002]Thisinventionpertainstothedeterminationoftextilecompositionthroughtheuseofspectroscopyandchemometricmodeling.

[0003]Theidentificationoftextilesisaconcernaroundtheworld.Textilemanufactures,retailmarketers,andcustomsofficialswouldallappreciateandutilizeanew,fast,nondestructivemethodoffabricidentification.Formanufacturesandmarketers,thismethodwouldassurethatthegarmentsthatarebeingmadeandsoldarenotmadeofsubstandardmaterialsorbeingsoldatoverinflatedprices.Traditionally,analyticalmethodsfordeterminingfibercontentincludesimplevisualinspectionwiththenakedeye,burntesting,microscopy,andsolubilitytesting.Thesemethods,thoughproventobeeffective,havesomedrawbacks.Visualinspection,forinstance,requirestheexaminertohaveaworkingknowledgeofthetextileshewillbeanalyzing.Also,thefeelandappearanceofsomefiberscanbeextremelysimilartoanother.Microscopysharesthesamedrawbackasvisualinspection.Certainmanufacturingprocessescanchangetheappearanceofafiber.Fibersviewedunderthemicroscope,especiallymanufacturedfibers,oftenhavesimilarcharacteristicsmakingapositiveidentificationimpossible.Burnandsolubilitytestingcanidentifytheclassoffibers,butthetechniqueisdestructiveandproduceswaste.(Kadolphetal.,2002).

SUMMARY

[0004]Inoneaspect,thecurrentmethodforclassifyingfabricsinvolvesassemblingalibraryoffabricsamples,creatingadatabaseofdiffusenearinfraredreflectionspectraofthosefabricsamples,andusingchemometrictechniquestoclassifythefabricsonthebasisoftheirspectra.

[0005]AchemometrictechniqueknownasSoftIndependentModelingofClassAnalogy("SIMCA")classificationisusedinconjunctionwiththedatabaseofspectraldatafromthefabricsamplestosortunknownfabricsamplesintotheirappropriatefibertype.Nochemicalorotherpretreatmentisnecessary.Themethodworksregardlessofthefabriccolororpattern,orthepresenceorabsenceoffabricfinishes.Themethodhastheadvantageofbeingnondestructiveandtheoperatordoesnothavetohaveabackgroundintextilesinordertoidentifythepieceoffabric.Moreover,theinstrumentwillfitonatabletopanddoesnotrequirespecializedlaboratoryfacilitiestooperate.

Findpriorart Discussthisapplication

FrenchEnglish

+Edwin



[0006]Multivariateregressioniswidelyknowninmanyareasofchemistryandcanserveasaparticularlypowerfulcomputationaltoolforcorrelatingspectraldatawithknowncompositionalchangesinatestsetofsamples.ThebasicobjectiveofthemethodistodevelopamathematicalmodelthatrelatestwosetsofvariablestoeachothersothattheindependentorXvariablescanbeusedtodeterminethedependentorYvariable.

[0007]Toavoidproblemswithcolinearityinthedata,allmultivariateregressiontechniquesrequireanorthogonalbasissetorcoordinatesystemonwhichtorepresentthedata.Toachievethiscondition,modernregressiontechniquesemployprojectionmethodstoobtainaseriesofvariancescaledeigenvectorsthatcanserveasanewcoordinatesystemforthedata.Thisformofdatadecompositionassuresanorthogonalcoordinatesystemforthedata.Atthesametime,itprovidesawaytoreducethedimensionalityofthedatabecauseonlythemajoreigenvectorsareneededtorepresentthedata.Finally,whenthedataarerepresentedonthenewcoordinatesystem,newinsightisoftengainedasnewrelationshipsthatwereformerlyobscuredintheoldcoordinatesystemarerevealed.

[0008]Broadly,oneaspectofthepresentinventioninvolvesamethodforclassifyingfabrics,comprisingthestepsof:[0009](1)Collectingspectraldataofapluralityoffabricsamples,whereinthefabricsamplesareofdifferentknownfabrictypes

[0010](2)Performingaprincipalcomponentanalysisandregressionofthespectraldataforeachofthefabricsamplesofeachknownfabrictypetocreateadatabaseofmodelprincipalcomponentanalysesforeachknownfabrictype

[0011](3)Collectingspectraldataofanunknownfabricsampletogiveunknownspectraldataand

[0012](4)Usingthedatabaseofmodelprincipalcomponentanalysesandsoftindependentmodelingofclassanalogy("SIMCA")toclassifytheunknownfabricsampleintoeitheroneormoreofthedifferentknownfabrictypesoratypeofunknownorigin.

[0013]Inthecurrentinvention,thepreferredspectraldataisdiffusenearinfraredreflection("NIR")spectraldata.Themethodisquitegeneralandcanapplytoadiversityoffabrictypes,includingacetate,acrylic,blends,cotton,linen,mohair,nylon,olefin,polyester,PVC,rayon,silk,andwool.

[0014]Thismethodisusefulfortherapididentificationofunknownfabricsamples.Itisnondestructiveanddoesnotrequirehazardouschemicalsandsolvents.Anyentitiesinneedofthistechnology,suchascustomsofficials,couldpurchaseastandardizednearinfraredspectrometer,thespectraldatabase,andthesoftwareforperformingtheclassification.Becausespectraldatabasesareinstrumentspecific,theycannotbetransferredtootherinstruments.Subscriberscouldreceiveperiodicdatabaseupdatesasmoresamplesareaddedtothelibrary.Althoughthereisnominimumormaximumnumberofsamplestobeincludedinthelibrary,itshouldbeofsufficientlyhighanumberofsamplestocreateaccuratemodelsforprediction.

BRIEFDESCRIPTIONOFFIGURES

[0015]Figure1showstheNIRspectra,(log1/R)versuswavelength,ofacetatesamples

[0016]Figure2showstheNIRspectra,(log1/R)versuswavelength,ofcottonsamples

[0017]Figure3showstheNIRspectra,(log1/R)versuswavelength,ofpolyestersamples

[0018]Figure4showstheNIRspectra,(log1/R)versuswavelength,ofwoolsamples

[0019]Figure5showstheNIRspectra,(log1/R)versuswavelength,ofallsamplesinthefabricdatabase

[0020]Figure6showstheprincipalcomponentanalysisforthecottonsamples:(A)Scoresplot(B)Regressioncoefficientsplot(C)Residualsplotand(D)Residualvarianceplotand

[0021]Figure7showsthespectraofrepresentativepolyesterandsilksamples,aswellasasamplefalselyidentifiedasbeingsilk.

DETAILEDDESCRIPTIONOFPREFERREDEMBODIMENTS

[0022]Thepresentinventionrelatestotheclassificationoftextilesusingspectroscopyandchemometricmodeling.Inparticular,thepresentinventioninvolvesthedevelopmentofaspectraldatabaseoffabricsamplesandtheuseofsupervisedsoftindependentmodelingofclassanalogies("SIMCA")toclassifyunknownfabricsamples.

[0023]Broadly,thefirststepinthecurrentmethodpertainstothedevelopmentofadatabaseofspectraldataofknownfabricsamples.Preferably,thespectraldatacollectedisdiffusenearinfraredreflection("NIR")spectra.AnysuitablespectrometercapableofcollectingdiffuseNIRspectracanbeused.Preferably,theNIRspectrometerincludesaquartzhalogensource,monochromator,leadsulfidedetectors,andanintegratingsphere,coatedwithbariumsulphate(Soyemietal,2001).Examplesofthefabriccategoriestobesampledforinclusioninthedatabaseincludeacetate,acrylic,blends,cotton,linen,mohair,nylon,olefin,polyester,PVD,rayon,silk,andwool.

[0024]ThefabricsamplesshouldbescannedbytheNIRspectrometerinasinglelayerandshouldnotbefoldedorcrumpledinthesampleholder.Nosamplepretreatmentisrequired.Theapertureoftheintegratingsphereshouldbefullycoveredbythefabricsample.Aftercollectionofthespectraldataforeachfabrictype,thedataisenteredintoasuitablechemometricanalysisprogram(Unscrambler9.1,Camo,Inc.,Corvallis,OR).Althoughnosamplepretreatmentisrequired,aSavitzkyGolaysmoothingroutineispreferablyusedasadatapretreatment.



[0025]Thechemometrictechniquecalledsoftindependentmodelingofclassanalogy("SIMCA")isusedinconjunctionwiththespectraldatabasetosortunknownfabricsamplesintotheirappropriatefibertype.SIMCAisaclassificationmethodbasedondisjointPCA(principalcomponentanalysis)modeling.Aprincipalcomponentanalysisisdoneforagivengroupofsampleswithinafabriccategory.Thisstepcalibratesthemultivariateprogramsoitcandiscernwhatsamplebelongstowhichcategory.IntheSIMCAapproach,classificationinPLSisperformedinordertoidentifylocalmodelsforpossiblegroupsandtopredictaprobableclassmembershipfornewobservations.Atfirst,thisapproachrunsaglobalPCAorPLSregression(accordingtotheavailabledatastructure)onthewholedatasetinordertoidentifygroupsofobservations.Localmodelsarethenestimatedforeachclass.Finally,newobservationsareclassifiedtooneoftheestablishedclassmodelsonthebasisoftheirbestfittotherespectivemodel.

[0026]Thisapproach,enforcesthecompositionoftheclassestobethesameastheoneinitiallychosenonthebasisoftheglobalmodel,computesthedistanceofeachobservationfromthemodelwithrespecttotheexplanatoryvariable,andinordertocomputetheclassmembershipprobabilities,referstoadistributionofthisdistancewhoseshapeanddegreesoffreedom,arenotyetcompletelyclearanddemonstrated.

[0027]InSIMCA,aPCAisperformedoneachclassinthedataset,andasufficientnumberofprincipalcomponentsareretainedtoaccountformostofthevariationwithineachclass.Hence,aprincipalcomponentmodelisusedtorepresenteachclassinthedataset.Thenumberofprincipalcomponentsretainedforeachclassisusuallydifferent.Decidingonthenumberofprincipalcomponentsthatshouldberetainedforeachclassisimportant,asretentionoftoofewcomponentscandistortthesignalorinformationcontentcontainedinthemodelabouttheclass,whereasretentionoftoomanyprincipalcomponentsdiminishesthesignaltonoise.Aprocedurecalledcrossvalidationensuresthatthemodelsizecanbedetermineddirectlyfromthedata.Toperformcrossvalidation,segmentsofthedataareomittedduringthePCA.Usingone,two,three,etc.,principalcomponents,omitteddataarepredictedandcomparedtotheactualvalues.Thisprocedureisrepeateduntileverydataelementhasbeenkeptoutonce.Theprincipalcomponentmodelthatyieldstheminimumpredictionerrorfortheomitteddataisretained.Hence,crossvalidationcanbeusedtofindthenumberofprincipalcomponentsnecessarytodescribethesignalinthedatawhileensuringhighsignaltonoisebynotincludingthesocalledsecondaryornoiseladenprincipalcomponentsintheclassmodel.Thevariancethatisexplainedbytheclassmodeliscalledthemodeledvariance,whichdescribesthesignal,whereasthenoiseinthedataisdescribedbytheresidualvarianceorthevariancenotaccountedforbythemodel.

[0028]Bycomparingtheresidualvarianceofanunknowntotheaverageresidualvarianceofthosesamplesthatmakeuptheclass,itispossibletoobtainadirectmeasureofthesimilarityoftheunknowntotheclass.Thiscomparison,isalsoameasureofthegoodnessoffitofthesample,toaparticularprincipalcomponentmodel.

[0029]Whenanewunknownsampleisintroduced,SIMCAwillcomparethespectralfeaturesofthenewsamplewiththoseprincipalcomponentanalysestheoperatorwishestoinvestigate.Iftheprogramdeterminesthesampleissimilarenoughtooneormorecategories,itwillclassifythesampleassuch.However,ifthesampleisconsiderednottofallintoanyofthecategoriesdefinedbythegivenprincipalcomponentanalysesitwillberejectedandnotclassified.TheSIMCAapproachtoclassificationdiffersfromthatofpartialleastsquares(PLS)discriminantanalysis.Adiscriminantanalysismakestheassumptionthatanewunknownisamemberofoneoftheclassesincludedintheanalysis.SIMCAcanclassifyasampleasbeinginssinglegroup,multiplegroups,ornotinanyofthegroupspresented.Thismethodologyisadvantageousinasituationwherethesampleinquestionisofunknownorigin,andthereisabsolutelynoinformationaboutitavailable.APLSdiscriminantanalysiscouldclassifythesampleasamemberofaclassevenifitwasn'tduetotheassumptionthatthesamplemustfallintooneoftheprearrangedcategories.

[0030]Thespectraldataarepreferablygovernedbylog1/R.ThedatacanbetransformedtotheKubelkaMunk("KM")functionusingthecomputermodelingprogram.Thenewsetofspectracanthenbeenteredintotheprincipalcomponentanalysesforthefabriccategories.Eithertypeofanalysescanbeused,althoughlog1/Rismoreaccurateforpredictingunknownfabricclassifications.

[0031]Inparticular,themethodforclassifyingfabricsbynearinfraredspectroscopyinvolvesthefollowingsteps.First,adatabaseofspectraldatafromanumberoffabricsamplesofvariousknownfabrictypesisprepared.Topreparethedatabase,spectraldatafromapluralityoffabricsamplesofdifferentknownfabrictypesiscollected.Thenaprincipalcomponentanalysisandpartialleastsquaresregressionisperformedusingthespectraldatacollectedforeachofthedifferentknownfabrictypes.Thedatabasethencontainsmodelprincipalcomponentanalysesforeachoftheknownfabrictypesandcanbeusedtoclassifyanunknownfabricsample.Thespectraldataoftheunknownfabricsampleisthencollected.Finally,thedatabaseofmodelprincipalcomponentanalysesisusedinassociationwithSIMCAtoclassifytheunknownfabricsampleintoeitheroneormoreofthedifferentknownfabrictypesoratypeofunknownorigin.TheSIMCAclassificationmethodmayclassifytheunknownsampleintomorethanonefabrictype.IftheSIMCAclassificationmethoddoesnotidentifyasuitablefabrictypeintowhichtheunknownfabricsamplecanbeclassified,itwillnotplacethesampleintoanyoftheknowncategories.[0032]Thisstrategyisusefulfordeterminingthefibercontentandtextilecompositionofvariousunknownfabrics,includingacetate,acrylic,blends,cotton,linen,mohair,nylon,olefin,polyester,PVC,rayon,silk,andwool.Preferably,thespectraldatacollectedisdiffusenearinfraredreflectionspectraldata.

EXAMPLE1.DEVELOPMENTOFSPECTRALDATABASEOFFABRIC

SAMPLES

[0033]Fabricsamplesweregatheredfromsamplecards,swatchcatalogs,andclippingsfromactualgarmentsandfabricbolts.Inall,atotalof826sampleswerecollectedandplacedintothefabricNIRdatabase.Abreakdownoftheindividualfabriccategories,andthenumberoffabricsamplesinthosecategoriescanbeseeninTable1below.



FabricSamples11,TotSamples',PCASamples0,p^

Acetate615110

Acrylic4

Blends50

Cotton27426311

Linen5

Mohair2

Nylon4

Olefin1

Polyester1099910

PVC1

Rayon776710

Silk463610

Wool19218210

Table1.Fabricdatabasesummary(a,totalsamplesindatabaseb,samplesincludedin

PCAc,samplespredicted)

[0034]TheNIRspectrometerusedhadaquartzhalogensource,monochromator,leadsulfidedetectors,andanintegratingsphere,coatedwithbariumsulphate,whichalloweddiffusereflectancemeasurementstobecollected(Soyemietal.,2001).TheNIRspectrometerwasattachedtoandcontrolledbyapersonalcomputerrunningLabviewsoftware.Nosamplepretreatmentwaspreformed.Thefabricswerescannedasasinglelayerandnotfoldedorcrumpledinthesampleholder.Theapertureoftheintegratingspherewasfullycoveredbythefabricsample.Eachsamplewasscannedfrom1100to2200rrmatevery2nm.

[0035]ThespectraldataforeachfabricgroupwerecombinedintoonelargespectraldatafileintheUnscrambler9.1(Camo,Inc.,Corvallis,OR)chemometricanalysisprogram.Althoughtherewasnosamplepretreatment,aSavitzkyGolaysmoothingroutinewasusedasadatapretreatment.Thewavelengthregionsmoothedwasfrom13241916nmwithfiveaveragingsidepointsoneitherendofthedata.

[0036]Thewavelengthregionusedintheprincipalcomponentanalyseswasfrom13341906nm,andeachPCAwaspreformedusingfullcrossvalidation.ForeachPCA,sixprincipalcomponentswereusedinthecalculation.Someofthefabricgroupssuchascottonandpolyestercouldmanagewithfour.However,acetateandwooldidnothaveasmuchspectralvarianceexplainedintheirfirstfourPC's.

[0037]Examplesofgroupspectraforacetate,cotton,polyester,andwoolcanbeseeninFigures1through4.Figure5showsthespectraofall826samplesinthetextiledatabase.Anexampleprincipalcomponentanalysis("PCA")forthecottonfabricsamplesisshowninFigure6.

EXAMPLE2.CLASSIFICATIONOFUNKNOWNFABRICSAMPLES

[0038]ThemodelPCA'sforacetate,cotton,andwoolwereusedtoclassifyasetofunknowns.Theunknownsconsistedof10acetate,polyester,andwool,aswellas11cottonsamples.TheresultofthisanalysiscanbeseeninTable2below.Allofthesampleswereclassifiedintheappropriatecategorywithnoadditionalmisclassifications.

Table2



[0039]Thespectrainthisstudyweregeneratedbylog1/R.ThereflectancedatacouldbeconvertedtotheKubelkaMunk(KM)functionhowever,theconversionshowednoimprovementintheclassificationanalysis.EXAMPLE3.CLASSIFICATIONOFMISLABELEDSAMPLE

[0040]Bycoincidence,itwasfoundthatoneofthefabricsamplesinthedatabasecreatedinExample1wasapparentlymislabeled.AspecificsilksamplewasrepeatedlynotclassifiedasasilkwhencomparedtothesilkPCA.Undertheassumptionthatthesilksamplemaynotbeasilk,itwasscreenedagainstallthemodelsthathadbeenproducedforthisstudy.ThesampledidnotgetapositiveclassificationuntilitwascomparedtothepolyesterPCA,asshowninTable4below.

Table4

[0041]Thispromptedacloserexaminationofthespectrumofthe"fake"silkandthespectraofseveralsilkandpolyestersamples.TheprincipalNIRbandat1672nmpresentinthetwoacceptedpolyestersamplescanclearlybeseeninthequestionablesilksample,asshowninFigure8.Thisindicatesthatthesampleisinfactapolyesterandnotasilk.Thosewishingtofakeasilkgarmenttypicallyusepolyesterduetothefinishesthatareusedonthefibers.

REFERENCESCITED

Theentirecontentofeachofthefollowingdocumentsisherebyincorporatedbyreference.

OTHERPUBLICATIONS

Kadolph,etal.,TextileFibersandTJieirProperties,vol.9,pp.1731,2002



Soyemi,etal.,Spectroscopy,vol.16,pp.2433,2001

PATENTCITATIONS

CitedPatent Filingdate Publicationdate Applicant TitleWO2004053220A1* Nov21,2003 Jun24,2004 UnileverNv Methodandapparatusfortheidentificationofatextileparameter

DE19920592A1* May4,1999 Nov9,2000CetexChemnitzerTextilmaschin

Methodtoautomaticallyrecognisefibrousmaterialormixturesinvolvesusingnearinfraredspectroscopytostudyunmodifiedmaterialsample,andusingneuralnetworktoevaluateresults

EP0807809A2* May7,1997 Nov19,1997PerstorpAnalytical,Inc.

SystemforindentifyingmaterialsbyNIRspectrometry

*Citedbyexaminer

NONPATENTCITATIONS

Reference

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REFERENCEDBY

CitingPatent Filingdate Publicationdate Applicant Title

WO2008151779A2* Jun10,2008 Dec18,2008 BionoricaAg Methodforqualitativelyclassifyingcompositionscontainingsurfactants

CN102564966A* Feb3,2012 Jul11,2012 Nearinfraredrapidnondestructivedetectionmethodfortextilecomponents

*Citedbyexaminer

CLASSIFICATIONS

InternationalClassification G01N21/35,D06F39/00

CooperativeClassification G01N21/3563,G01N2201/129,G01N21/359

EuropeanClassification G01N21/35G

LEGALEVENTS

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patent wo2007047299a1 - classification of fabrics by near-infrared spectroscopy - google patents

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