Near Infrared Spectroscopy Facilitates Rapid Identification of Both Young and Mature Amazonian ...

15 Pages·2015·1.28 MB·English

RESEARCHARTICLE
Near Infrared Spectroscopy Facilitates Rapid
Identification of Both Young and Mature
Amazonian Tree Species
CarlaLang1*,FláviaReginaCapellottoCosta2,JoséLuísCampanaCamargo3,Flávia
MachadoDurgante4,AlbertoVicentini4
1 GraduatePrograminBotany,InstitutoNacionaldePesquisasdaAmazonia,Manaus,Brazil,
2 DepartmentofBiodiversity,InstitutoNacionaldePesquisasdaAmazonia,Manaus,Brazil,3 Biological
DynamicsofForestFragmentsProject(INPA/STRI),Manaus,Brazil,4 DepartmentofEnvironmental
Dynamics,InstitutoNacionaldePesquisasdaAmazonia,Manaus,Brazil
* [email protected]
Abstract
OPENACCESS Preciseidentificationofplantspeciesrequiresahighlevelofknowledgebytaxonomists
Citation:LangC,CostaFRC,CamargoJLC, andpresenceofreproductivematerial.Thisrepresentsamajorlimitationforthoseworking
DurganteFM,VicentiniA(2015)NearInfrared withseedlingsandjuveniles,whichdiffermorphologicallyfromadultsanddonotbearrepro-
SpectroscopyFacilitatesRapidIdentificationofBoth
ductivestructures.Near-infraredspectroscopy(FT-NIR)haspreviouslybeenshowntobe
YoungandMatureAmazonianTreeSpecies.PLoS
effectiveinspeciesdiscriminationofadultplants,soifyoungandadultshaveasimilarspec-
ONE10(8):e0134521.doi:10.1371/journal.
pone.0134521 tralsignature,discriminantfunctionsbasedonFT-NIRspectraofadultscanbeusedto
identifyleavesfromyoungplants.Wetestedthiswithasampleof419plantsin13Amazo-
Editor:DusanGomory,TechnicalUniversityin
Zvolen,SLOVAKIA nianspeciesfromthegeneraProtiumandCrepidospermum(Burseraceae).Weobtained
12spectralreadingsperplant,fromadaxialandabaxialsurfacesofdriedleaves,andcom-
Received:March19,2015
paredtherateofcorrectpredictionsofspecieswithdiscriminantfunctionsfordifferentcom-
Accepted:July9,2015
binationsofreadings.Weshowedthatthebestmodelsforpredictingspeciesinearly
Published:August27,2015
developmentalstagesarethosecontainingspectraldatafrombothyoungandadultplants
Copyright:©2015Langetal.Thisisanopen (98%correctpredictionsofexternalsamples),butevenusingonlyadultspectraitisstill
accessarticledistributedunderthetermsofthe
possibletoattaingoodlevelsofidentificationofyoung.Weobtainedanaverageof75%cor-
CreativeCommonsAttributionLicense,whichpermits
rectidentificationsofyoungplantsbydiscriminantequationsbasedonlyonadults,when
unrestricteduse,distribution,andreproductioninany
medium,providedtheoriginalauthorandsourceare themostinformativewavelengthswereselected.Mostspecieswereaccuratelypredicted
credited. (75–100%correctidentifications),andonlythreehadpoorpredictions(27–60%).These
DataAvailabilityStatement:Dataareavailablefrom resultswereobtaineddespitethefactthatspectraofyoungindividualsweredistinctfrom
Figshare:(http://dx.doi.org/10.6084/m9.figshare. thoseofadultswhenspecieswereanalyzedindividually.WeconcludedthatFT-NIRhasa
1483518).
highpotentialintheidentificationofspeciesevenatdifferentontogeneticstages,andthat
Funding:ThiscontributionispartofaMaster’s youngplantscanbeidentifiedbasedonspectraofadultswithreasonableconfidence.
thesisattheInstitutoNacionaldePesquisasda
Amazônia(INPA),whichwassupportedbya
fellowshipfromtheBrazilianNationalResearch
Council(CNPq),theCNPq-PELDprojectandCNPq
Universal481297/2011-1.FlaviaRCCostaisthe
recipientofConselhoNacionaldePesquisa(CNPq)
(http://www.cnpq.br/)Grant#403764/2012-2and
Grant#481297/2011-1.
PLOSONE|DOI:10.1371/journal.pone.0134521 August27,2015 1/15
NearInfraredSpectroscopyforYoungPlantIdentification
CompetingInterests:Theauthorshavedeclared Introduction
thatnocompetinginterestsexist.
Speciesidentificationrequiresahighleveloftaxonomicexpertise,confirmationbyspecialists
[1]andthepresenceofreproductivematerials[2],factorsthathinderaccurateidentificationof
plantsinareaswithhighbiologicaldiversityasinthetropics.Usingthemorphologyofdried
plantspecimensinanon-reproductivestatetodefinespeciescangenerateahighrateofmis-
identification[3],yetisafrequentprocedureinforestinventories.Theclassicuseofmorpho-
logicaltraitsforspeciesidentificationhasseverallimitations,includingphenotypicplasticity
andtheexistenceofcryptictaxa.Theidentificationofsterilesamplesofyoungplantsismost
oftennotpossiblewithconventionalidentificationkeys,sincemorphology-basedplantkeys
arealmostalwaysdesignedforreproductiveadultmaterial[4].Thisrepresentsamajorlimita-
tionforthosestudyingseedlingsandjuveniles,whichhavebeengenerallylittlestudiedfor
morphology.Theneedfortoolstoidentifyspeciesatalldevelopmentalstageshaslong-been
emphasized,butthereisstillmuchtodotoexpandthetaxonomicknowledgeofplantsinthe
earlystagesoflife,especiallyinareasliketheAmazonbasin[5].However,emergingtechnolo-
giescanhelp,providingeffectiveandaccuratemeanstoidentifyspeciesatanyoftheirdevelop-
mentalstages.
TheidentificationoftaxausingastandardizedregionofDNA(DNAbarcoding)[6]has
receivedattentionrecently,andisbeingdevelopedbyaninternationalinitiative.Molecular
datahavebeenveryusefulinsolvingproblemsofdiscriminationofspecies[7,8].Kressetal.
[9]showedthatabarcodecontainingthreeDNAlociwaseffectiveinidentifyingwell-defined
treespeciesonBarroColoradoIsland,achieving98%correctidentifications.However,for
highlydiverseenvironments,suchastheAmazon,DNAbarcoding,initscurrentform,has
stilllowpredictivepowerforspecies,evenwhenalargernumberofmarkersareused,because
manyspeciesareofrecentoriginandmanygeneradonotformmonophyleticgroups[10].For
closelyrelatedspecies,geneticandmorphologicaldifferentiationisdifficultandtheassignment
ofaspeciesisambiguous[11].Furthermore,themethodofDNAbarcodingisstillexpensive
forlarge-scaleapplicationsinhyperdiverseareas[12].Sinceknowledgeofthechemicaland
structuralbasisunderlyingthedistinctivesignalbetweenspeciesisstilllacking,FT-NIRcan
notsubstitutemolecularanalysisforsystematics,whichshouldbebasedongeneticdifferences.
However,easyandfastspeciesidentificationforvarietyofecologicalandotherstypesofstud-
ies,isthegreatestpromiseofFT-NIRandothersrelatedspectraltechniques.
Morphologicalandmoleculardataaresimilarinseveralpoints:theycanbebasedonsterile
material,useadvancedtechnology,generatealotofdataandneedtaxonomicexpertisetobe
calibrated.Nearinfraredspectroscopy(FT-NIR)isanalternativemethodthatishighlycost-
effective,quick,non-destructive,andrequiresnopretreatmentofsamples[13].Thecostofa
goodFT-NIRspectrometer,whichcanprocessanunlimitednumberofsamples,isaboutthe
sameofDNAbarcoding~800samples,andtheonlymaintenancecostistheexchangeof
lamps,andgeneralequipmentmaintenance.
TheFT-NIRcandetectwithhighaccuracyanymoleculeinwhichtheprincipalchemical
bondsareCH,OH,NH,SHorC=O[14,15].Whenanorganicsampleisirradiated,chemical
bondscontinuouslyvibratecausingawavemotionthatischaracteristicofthatfunctional
group[16].ThecontactoftheincidentFT-NIRlightonleaftissuegeneratesaspectralresponse
thatisafunctionofthechemicalcompositionandstructureofcellsandinternalmorphology
oftheleaf[17],whichmaybecharacteristicofthespecies.
ApplicationsofFT-NIRasatoolfortheanalysisofchemicalandphysicalpropertiescanbe
foundinvirtuallyallareasofscience.Inwoodscience,FT-NIRhasbeenusedtopredictthe
physical,mechanicalandchemicalpropertiesofwood[18,19,20,21].Therearealsoapplica-
tionsfordeterminingthegeographicaloriginofindividualplants[14],andinplanttaxonomy,
PLOSONE|DOI:10.1371/journal.pone.0134521 August27,2015 2/15
NearInfraredSpectroscopyforYoungPlantIdentification
recentstudiesthathaveusedthetechniquehaveachievedsuccessfulidentificationratesrang-
ingfrom80to100%[22,23,24,25].Otherspectroscopictechniquesbasedondifferentbands
ofinfraredradiationhavebeenusedtoidentifyspeciesortheirchemicalproperties[26].
Spectroscopyhas,therefore,beenrevealedasapromisingtoolinthediscriminationand
identificationofadultindividualsofplantspecies.Thisencouragedustoaskifyoungandadult
plantsofthesamespecieshadsimilarspectralsignature,and,ifso,whetherFT-NIRspectraof
well-identifiedadultscouldthereforebeusedtopredictthespeciesidentityofyoungplants.
Theabilitytoidentifysaplingsandseedlingsreliablyandswiftlywouldopenmanyavenuesfor
studiesofdemographics,lifehistory,dispersal,andothersareasthatarecurrentlylimitedby
poorknowledgeofdevelopmentalchangesinthemorphologyofplantspecies.
MaterialsandMethods
LeafsamplesusedinthisstudycamefromtwoareasclosetothecityofManaus,Amazonas,
Brazil:theDuckeForestReserve(2°55'S,59°59'W)andanareawithintheBiologicalDynamics
ofForestFragmentsresearchproject(BDFFP)(2°30'S,60°W),locatedca.50kmNofDucke
Reserve.BothreservesarecoveredbydenselowlandAmazonianrainforest[27].Duckeand
BiologicalDynamicsofForestFragmentsresearchproject(BDFFP)areunderthejurisdiction
oftheBrazilianNationalInstituteforAmazonResearch(INPA),whichissuedpermitsforthe
samplinginvolvedinthepresentstudy.
Samplecollection
ThebotanicalmaterialusedwasfromherbariumspecimenscollectedinDuckeReservefrom
JanuarytoMarch2013(8.38%ofspecimens),andherbariumspecimensthatwerecollected
fromtreestaggedatpermanentplotsbelongingtheBDFFPprojectoverthepast35years
(91.6%).Theyrepresentthedevelopmentalstagesofseedlings,juvenilesandadultsofthespe-
ciesconcerned.Allleafsamples,boththenewlycollectedandtheBDFFPones,werenotpre-
servedinalcoholinthefield;theseleaveswerepreparedfollowingthenormalproceduresused
forherbariumspecimens,oven-dried(60°C)for1or2daysuntilcompletelydry.Fruitforthe
productionofseedlingsandvouchermaterialforproductionofdriedspecimenswerecollected
fromtheDuckeReserve.Fruitswereprocessedtoobtainseeds,whichweregerminatedina
greenhouse.Allseedlingsproducedweredriedbythesameprocessdescribeabove.Asgermi-
nationandseedlingproductionrateswereverylowformostspecies,itbecamenecessaryto
supplementthesebypreviouslycollectedandidentifiedherbariumspecimensofseedlings.
MostadultsampleswereidentifiedbyDouglasDalyoftheNewYorkBotanicalGarden,an
expertinBurseraceae.IdentificationofseedlingsandjuvenilesampleswasreviewedbyPaul
FineoftheUniversityofBerkeley,California.
ThespeciesusedinthestudybelongtotheBurseraceae,afamilyknownforthedifficulties
encounteredwhenattemptingspeciesidentificationbasedsolelyonmorphologicalcharacters.
Thisisnotableinthespecies-richgenusProtium,whereitisespeciallydifficulttoidentifyseed-
lingsandjuveniles.Atotalof346specimenswereused,196specimensfromtheadultstage
and150atthejuvenileorseedlingstage.Theserepresent13speciesofBurseraceae,12fromthe
genusProtium,andonefromthegenusCrepidospermum(Table1).Weconsideredtwoforms
ofProtiumhebetatumDCDaly,empiricallydistinctbutnotformallynamed.Theseare"form
A"(hairierandwithmorebulbousleaflets)and"FormB"(slenderglabrousleafletsandlightly
hairedpetioles)(A.Andrade.comm.).Weusedaminimumnumberof10individualsper
species.
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NearInfraredSpectroscopyforYoungPlantIdentification
Table1. NumberofspecimensusedtoobtainFT-NIRspectra.
Species Seedlings/Juveniles Mature
ProtiumapiculatumSwart 12 24
Protiumdecandrum(Aubl.)Marchand 10 13
ProtiumgrandifoliumEngl. 7 10
ProtiumhebetatumD.C.DalyformaA 20 19
ProtiumhebetatumD.C.DalyformaB 19 16
ProtiumkrukoffiSwart 18 15
ProtiumoccultumDaly 10 20
ProtiumpallidumCuatrec. 9 15
Protiumpaniculatum(Engl.)var.nova 12 13
Protiumpaniculatumvar.riedelianum(Engl.)Daly 8 13
ProtiumsagotianumMarchand 11 16
Protiumsubserratum(Engl.)Engl. 9 10
Crepidospermumrhoifolium(Benth.)Triana&Planch 5 12
Total 150 196
doi:10.1371/journal.pone.0134521.t001
FT-NIRspectroscopymeasurements
Atotalof12FT-NIRspectralreadingswereobtainedforeachspecimen.Weusedthreewhole
driedleavespervoucherspecimen.Fourspectralreadingswerecollectedperleaf,withtwo
readingseachontheadaxial(upper)andabaxial(lower)surfacesoftheleaf(atotalof12read-
ingsperplant).Thereadingpointsincludedthebaseandtheapexoftherespectivesidesofthe
leaf.Thisdatawereobtainedfor346samplesfrombothDuckeReserveandBDFFP.Leafspec-
trawerecollectedwithaThermoNicolletspectrophotometer,usingtheAntarisFT-NIRII
MethodDevelopmentSystem(MDS)[28].Thespectralreadingsareexpressedasabsorbance
valuesbetweenthewavelengths1000to2500nminthenear-infraredandeachspectrumcon-
sistsof1557absorbancevalues.Eachreadingproducedbytheinstrumentwastheaverageof
16scansatawavelength,aresolutionof8cm-1.Ablackbodywasplacedoverthepointwhere
thespectralreadingswerecollectedtoavoidlightscattering.Abackgroundcalibrationreading
wasperformedbeforeeachreadingwastaken.
Analyses
Theanalyseswereperformedusingtwosetsofdata,(1)theabsorbanceatallwavelengthsover
theentireFT-NIRspectrum(1000to2500nm),atotalof1557wavelengths,and(2)aselection
ofthemostinformativewavelengths.
Themostinformativewavelengthswereselectedbydiscriminantanalysis.Theprocedure
aimedtocapturethesetofindependentvariablesthatbestpredictedspeciesidentity,sincenot
allofthespectraarenecessarilyinformativefordiscrimination.Inspectraldata,itiscommon
thatsomespectralregionsvaryinaconsistentfashionbetweensamplestobediscriminated,
whileothersdonotvary(andthereforeareuninformative)andsomevaryinconsistently,pro-
vidingonlynoise.Cleaningthisnoisemayincreasethediscriminationpower.Weusedastep-
wisemethodtoselectthewavelengths,involvingaprocessofinclusionandexclusionof
independentwavelengthsinthediscriminantfunctiononeatatime,basedontheirdiscrimina-
torypower.TodothisweusedthestepclassfunctionoftheKlarpackage[29],wherethepro-
cessiscompletedwhenallindependentwavelengthsareincludedinthefunctionorthe
wavelengthsexcludedarejudgednottocontributesignificantlytothediscrimination[30].
However,becauseofthelargenumberofwavelengthsofthespectrum(1557),themaximum
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NearInfraredSpectroscopyforYoungPlantIdentification
numberofselectedwavelengthswasdefinedasone-thirdofthenumberofsamplesanalyzed,
followingthediscriminantanalysispremiseofWilliamsandTitus[31].
Discriminantfunctions(LDA)weregeneratedtoassessthecapacityofthespectratodistin-
guishspecies,regardlessofdevelopmentalstage.Discriminantanalyseswereconductedusing
eitherabsorbancevaluesatallwavelengthsoftheFT-NIR(1000–2500nm)spectrum,orthose
wavelengthsdeterminedbythestepwisemethodtobethemostinformative.Thefunctions
weregeneratedfromfourdifferentsetsofleafdatareadings:1)averageof12readings(abaxial
+adaxial);2)theaverageofsixadaxialreadings;3)theaverageofsixabaxialreadings,and4)
usingonereadingrandomlyselectedfromthe12takenperindividual(Table2).
Foreachofthesedatasets,afunctionwasgeneratedusingonlydatafromadultplants
(AdultModel).Theexternalsamplesofseedlings/juvenileswereusedtovalidatethemodel.
Theconverseprocesswascarriedoutwheretheequationwasconstructedbasedondatafrom
juvenileplants(YoungModel),validatedwithexternalsamplesfromadults.Asafurthertest,a
functionwasgeneratedwith2/3ofsamplesincludingbothyoungandadults(Combined
Model),andusingtheremaining1/3forvalidation.Eachmodelwasrepeated100timeswith
randomizationofthesubassembliesandtheaverageofthe100iterationswascompared.
Finally,weevaluatedthecapacityofthespectraldatatodiscriminatedevelopmentstages.
Forthismodelthespeciesidentitywassuppressed,andthesamplesdividedonlyintoyoung
andadultgroups.Themodelwasbuiltwith2/3ofthedataandvalidatedwiththeremaining
third.
Foreachofthetestsdescribed,weobtainedthepercentageofcorrectidentifications.Analy-
seswereperformedinR2.10.0environment[32].
Results
Isitpossibletopredicttheidentityofyoungbasedonadults?
TestsusingallwavelengthsoftheFT-NIRspectrum(1000to2500nm). Theresultsof
thetestseriesareshowninTable3andFig1.WhenusingallFT-NIRwavelengthsthemodels
builtwithspectroscopicreadingsfromadultswereabletopredicttheidentityofyoungin48to
57%ofoccasions,averagedoverallspecies.Themodelconstructedwithsamplesofyoung
plants,wasabletopredicttheidentityofadultswith75–76%accuracy.Forthemodelcombin-
ingadultandyoungindividuals,theaverageprobabilityofsuccesswas98%forthethreesetsof
datatested.
Theaverageabsorbancevaluesofadaxialleafsurfaceprovidedbetterpredictionsofyoung´s
identityfrommodelsbasedonadultsthantheabaxialsurface.However,ingeneral,allmodels
basedonthecompletesetofwavelengthsfromadultspoorlypredicted(averageof53%accu-
racyoverallmodelsandspecies)theidentityofyoungindividuals.Onlythreespecies(23%)
achieved100%accuracywhenweusedtheaveragesof12readings,andonly5species(38.4%)
whenweusedtheaveragedreadingsfromtheadaxialsurface.InProtiumpallidumnoaccurate
identificationofyoungspecimenswasobtained,whileallindividualswerecorrectlyidentified
attheadultstage.Twospecies(ProtiumoccultumandP.paniculatumvar.riedelianum)were
consistentlypredictedcorrectlyinbothdevelopmentalstages.Ingeneral,usingallwavelengths,
thespecieswerepredictedcorrectlyinonlyonephaseofdevelopment,orwhenyoungusing
themodelbasedonadults,orwhenadultusingthemodelbasedonthespectralpropertiesof
youngplants.
Usingonlyonereadingfromrandomlyselectedindividualreadingreducedpredictiveabil-
ity.Themodelbasedonmeasurementsfromadultstopredictspeciesidentitiesinyoungplants
hadasimilarlevelofsuccesstothosealreadyobtainedwiththe12readingsaverage(51%accu-
racy).ThisalsooccurredwiththeYoungModel,wherethecorrecthitsaveragewas75%.For
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NearInfraredSpectroscopyforYoungPlantIdentification
Table2. DescriptionoftestsbasedonFT-NIRspectratodiscriminatebetweenBurseraceaespecies.
LinearDiscriminantAnalyses(LDAs)Models
Dataset AdultModel YoungModel CombinedModel
Meanofreadings Adaxial+Abaxial x x x
Adaxial x x x
Abaxial x x x
Singlespectrum x x
ModelAdult,functiongeneratedwithsamplesofadultsonly.Validation:samplesofseedlingsandyoungplants;YoungModel,functiongeneratedwith
samplesofseedlings/youngonly.Validation:samplesofadults;CombinedModel,functiongeneratedwith2/3ofthetotalsample,alldevelopmental
stagescombined.Validation:theremaining1/3ofthetotalsample.Alllistedcombinationsweretestedbothwithallwavelengthcomponents,aswiththose
identifiedbystepwiseanalysisasthemostinformative.
doi:10.1371/journal.pone.0134521.t002
Table3. ResultsofdiscriminatanalysisusingtheaverageofthereadingsandallwavelengthsofthespectrumFT-NIR.
AdultModel YoungModel CombinedModel
Dataset Dataset Dataset
Species MeanAd MeanAd MeanAb Single MeanAd MeanAd MeanAb Single MeanAd MeanAd MeanAb
+Ab Spectrum +Ab Spectrum +Ab
Protiumapiculatum 66 50 59 54 100 100 100 96 100 100 100
Swart
Protiumdecandrum 71 87 71 86 0 8 31 65 100 100 100
(Aubl.)Marchand
Protiumgrandifolium 80 100 100 100 70 50 70 20 100 100 100
Engl.
ProtiumhebetatumD. 25 15 30 12 95 100 100 95 100 91 91
C.DalyformaA
ProtiumhebetatumD. 47 57 21 53 12 0 19 12 98 98 90
C.DalyformaB
ProtiumkrukoffiSwart 22 55 94 47 85 77 73 76 100 100 100
Protiumoccultum 100 100 90 98 100 95 95 100 100 100 100
Daly
Protiumpallidum 0 0 0 0 100 100 100 80 100 100 100
Cuatrec.
Protiumpaniculatum 66 100 58 27 69 92 69 100 100 100 100
(Engl.)var.nova
Protiumpaniculatum 100 100 100 100 92 100 100 73 100 100 100
var.riedelianum
(Engl.)Daly
Protiumsagotianum 36 36 45 64 100 87 87 86 100 100 100
Marchand
Protiumsubserratum 11 33 33 11 80 70 80 70 100 100 100
(Engl.)Engl.
Crepidospermum 100 100 100 100 41 58 33 75 100 100 100
rhoifolium(Benth.)
Triana&Planch
Percentageof 48 57 56 51 76 75 76 75 98.6 98.4 98.6
correct
identification
MeanAd+Ab,theaverageof12readings;MeanAd,theaverageofthereadingsoftheadaxialsurface;MeanAb,theaverageofthereadingsabaxial
surface;Singlereading,arandomlyselectedreading.Hitpercentageforeachspeciesandaveragepercentageformodelsgeneratedwitheachdataset.
doi:10.1371/journal.pone.0134521.t003
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NearInfraredSpectroscopyforYoungPlantIdentification
Fig1.MatriceswiththeresultsofDiscriminantAnalysisforthethreemodels(Adult,Youngand
Combined)andusingaverageof12readings(abaxial+adaxial),andforbothallwavelengths(left)and
stepwiseselectedwavelengths(right).Theobservedspeciesnamesaregivenincolumns,whilepredicted
namesaregiveninrows.Therefore,thevaluesonthediagonalarecorrectpredictions,andoff-diagonal
valuesarewrongpredictions.Abbreviations:C.rho=Crepidospermumrhoifolium;P.api=Protium
apiculatum;P.dec=Protiumdecandrum;P.gra=Protiumgrandifolium;P.heb.A=Protiumhebetatum
formaA;P.heb.B=ProtiumhebetatumformaB;P.kru=Protiumkrukoffi;P.occ=Protiumoccultum;P.pal
=Protiumpallidum;P.pan.n=Protiumpaniculatumvar.nova;P.pan.r=Protiumpaniculatumvar.
reidelianum;P.sag=Protiumsagotianum;P.sub=Protiumsubserratum.
doi:10.1371/journal.pone.0134521.g001
mostspecies,thepercentageofcorrectresponseswitharandomlyselectedreadingremained
similartotheresultspreviouslyobtainedwiththeaverageof12readings.
Testsusingthemostinformative,stepwise-selected,wavelengths. Theresultsofthistest
seriesareshowninTable4andFig1.Twentyvariableswiththehighestdiscriminatingpower
wereselectedbetweenthewavelengths2072.53to2436.77nm,fromtheinitial1557measure-
ments.Selectionwasmadeirrespectiveofdevelopmentalstage.Theabilitytopredictspecies
identityofyoungindividualsusingtheAdultModelimprovedinthethreedatasetsofaveraged
readings,increasingfrom48–57%to60–75%correctidentifications.Mostspecieswerewell
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NearInfraredSpectroscopyforYoungPlantIdentification
Table4. Resultsofdiscriminantanalysisbasedontheaverageof12readingsandthemostinformativewavelengthsselectedbystepwise
modelling.
AdultModel YoungModel CombinedModel
Dataset Dataset Dataset
Species MeanAd MeanAd MeanAb Single MeanAd MeanAd MeanAb Single MeanAd MeanAd MeanAb
+Ab Spectrum +Ab Spectrum +Ab
Protiumapiculatum 75 100 33 67 91 96 83 96 100 100 100
Swart
Protiumdecandrum 100 100 100 93 61 54 69 0 100 100 100
(Aubl.)Marchand
Protiumgrandifolium 100 100 100 90 30 20 30 70 87 100 75
Engl.
ProtiumhebetatumD. 60 20 65 29 100 100 95 92 91 77 71
C.DalyformaA
ProtiumhebetatumD. 84 68 79 47 0 0 6,0 53 60 70 41
C.DalyformaB
ProtiumkrukoffiSwart 89 83 89 16 35 40 35 69 100 100 100
Protiumoccultum 60 50 100 100 80 70 95 100 100 100 100
Daly
Protiumpallidum 89 11 55 0 80 60 87 80 100 100 100
Cuatrec.
Protiumpaniculatum 67 58 92 11 92 85 54 84 100 100 86
(Engl.)var.nova
Protiumpaniculatum 75 0 100 98 100 100 100 84 100 100 100
var.riedelianum
(Engl.)Daly
Protiumsagotianum 27 36 18 45 81 75 75 68 100 87 87
Marchand
Protiumsubserratum 89 100 67 44 50 80 10 30 100 100 100
(Engl.)Engl.
Crepidospermum 100 100 100 100 80 80 80 43 100 100 100
rhoifolium(Benth.)
Triana&Planch
Percentageof 75 60 73 52 64 58 61 73 97 94 96
correct
identification
MeanAd+Ab,theaverageof12readings;MeanAd.theaverageofthereadingsadaxialsurface(Ad);MeanAb,theaverageofthereadingsadaxial
surface;SingleReading,arandomlyselectedreading.Hitpercentageforeachspeciesandaveragepercentageformodelsgeneratedwitheachsetof
dataaregiven.
doi:10.1371/journal.pone.0134521.t004
predicted(75–100%correctidentifications),andonlythreehadpoorpredictions(27–60%).
Thepredictionsofadultidentitiesbasedonspectroscopicresponsesofyoungplantsdecreased
from75–76%to61–64%acrossallmodels,andthesameoccurredforthemodelcombining
youngandadultplants.
Themodelforpredictionoftheidentityofyoungbasedonadultspectrawithbestresults
wasthatusingtheaverageof12readings(Ad+Ab).Thishadanaverageof75%ofcorrect
speciesidentificationsofyoungplants.Onlyonespecies(P.sagotianum)hadlessthan60%of
correctidentifications,andthemajority(ninespecies)had75%ormorecorrectresponses.
Forarandomlyselectedspectralreading,stepwiseanalysisretained39wavelengthsbetween
1002.60to2495.41nmasbeingthemostinformative.Formostmodelstherewasnosignificant
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NearInfraredSpectroscopyforYoungPlantIdentification
differencesbetweenthisandtheresultsobtainedpreviouslywithallFT-NIRspectrumwave-
lengths(1000to2500nm).
DoyoungandadultplantsdifferintheirNIRspectra?
Whentestingthecapacityofnear-infraredspectroscopytodiscriminatebetweendevelopmen-
talstages,independentofspecies,discriminantanalysishadanaverageaccuracyof99.9%,indi-
catingthatdevelopmentalstagesconsistentlydifferintheirspectralsignatures.Alsowithin
speciesthedifferenceinspectralvaluesbetweenyoungandadultplantscanbereadilyseen,
bothforthefullspectraandforanordinationintwodimensions(Fig2).
Discussion
Ourresultsindicatedthatnear-infraredspectroscopy(FT-NIR)isaneffectivetoolfordiscrimi-
natingtreespeciesatdifferentstagesofdevelopment.Inourstudy,weindicatedthatthebest
modelsforpredictingspeciesinearlydevelopmentalstagesarethosecontainingspectraldata
frombothyoungandadultplants,butevenusingonlyadultspectraisstillpossibletoattain
goodlevelsofidentificationofyoung.Andfinally,wehavedemonstratedthatyoungandadults
ofthesameamazontreesspeciesclearlydifferintheirspectra.
Canyoungbeidentifiedbasedonlyonspectrafromadults?
Inthisstudywehaveshownthatitispossibletoidentifyyoungtreespeciesbasedonspectral
signaturesofadultsofthesamespecies,althoughthelevelofaccuracyobtainedisstillnotas
highasobservedwhenidentificationsaremadewithinthesamedevelopmentalstage,orwhen
bothdvelopmentalstagesareusedtoproducethemodels.Weobtainedanall-speciesaverage
of75%correctidentificationsforyoungplantswhendiscriminantequationswereconstructed
withthemostinformativewavelengths.Comparingtheseresultswithratesofcorrectidentifi-
cationsevenwithinthesamedevelopmentalstageobtainedbytraditionalmethods(40–50%
[3,33])orbarcoding(70%,[34]),weconcludethatthecost-benefitofspectroscopy-based
methodishigher,givenitsspeed,lowcostandabilitytoachievesuccessfulhits.Wearenot
advocatingthatthesemethodsshouldbeabandoned,butthatFT-NIRcanprovidequick
answersthatwillmakepossiblealargeexpansionofplantecologystudies.
Weexpectedthatarandomreadingperindividualwouldprovidemoreaccuratepredictions,
giventhechancethatseveralreadingsfromthesameleafcouldcontainlocalcontamination,
epiphylls,thatmightspoilthespectralpatternofaspecies.However,therewasnoimprovement
inhitratewiththisprocedure,anditseemsthatuseoftheanaverageofreadingsisamoreeffec-
tivewayofsmoothingpossiblelocalvariationsbetweenleaves.Wavelengthselectionindicated
themostinformativeregionoftheFT-NIRspectrumlaybetween2000and2500nm.Asimilar
range(1666.66to2500nm)wasreportedbyDurganteetal.[22].Thisregionisrelatedtothe
presenceofcarbohydratessuchascellulose,ligninandpolysaccharides[35],compoundsthat
areassociatedwiththestructureofplantcellwall[36].Asaswhole,wesuggestthatthebestpro-
tocolforbuildingawiderangemodelforspeciesdiscriminationbasedonFT-NIRspectraisto
useanaverageofreadingsfrombothadaxialandabaxialleafsurfacesofeachspecimen,andthe
2000–2500nmregionofthespectra.
Ourstudyindicatedthatthebestmodelsforpredictingspeciesinearlydevelopmentalstages
arethosethatcontainspectraldataforyoungandadultplants.Ourbestresultgavea98.6%
correctidentificationofthespecies,regardlessofdevelopmentalstage,whenweusedtheaver-
agedreadingsandthewholeFT-NIRspectrum(1000–2500nm)forthemodelgeneratedwith
samplesofadultsandyoungplants.Earlystudiesofplantsusingdiffusereflectancemeasure-
mentsrecognizedthattheplantcuticleandtheunderlyingcellwallaretheprincipalcauseof
PLOSONE|DOI:10.1371/journal.pone.0134521 August27,2015 9/15
NearInfraredSpectroscopyforYoungPlantIdentification
Fig2. (a,c,e)GraphicrepresentationofatwodimensionalPrincipalComponentAnalysis(PCA)ofFT-NIR
spectraofyoungandadultindividuals.(b,d,f)Representationoffullspectraofindividuals.Spectrais
composedof1557wavelengths,fortheaverageof12readingsperindividual.Youngplantsinblack,adult
plantsinred.(a,b)Protiumgrandifolium;(c,d)Protiumsubserratum(e,f)Protiumapiculatum.
doi:10.1371/journal.pone.0134521.g002
spectralfeatures[37,38].Castro-Esauetal.[39]demonstratethatleavesofthesamespecies,
butofdifferentagesorhealth,willvarywidelyintheirspectralreflectanceproperties,and
showedthatinternalleafstructureinfluencesleafreflectanceinthenear-infraredregion.From
thiswecaninferthatdiscriminantfunctionmodelscontainingsamplesofyoungplantscan
bettercaptureallpossiblespectralvariability,bothchemicalorstructural,betweenyoungand
adults,andthesemodelswillbemoreefficientinpredictingspeciesidentityatearlystagesof
development.However,evenintheabsenceofsamplesofyoungplantsinthemodel,itisstill
possibletopredicttheiridentitybasedonadults,withaslightlyhigherdegreeofuncertainty,as
demonstratedabove.Becauseitisdifficultandlongtermworktoobtainsamplesofwellidenti-
fiedyoungplantsforbuildingcombinedmodels,ourresultsshowingthepossibilityofusing
onlyadultsasabaseformodelsshouldprovidethecapacitytoattainahigherlevelofidentifi-
cationforseedlingsthanwecurrentlyhave.
TheseresultsarecomparabletopreviousstudiesusingFT-NIRforbotanicaltaxonomypur-
poses.However,wehavedemonstratedforthefirsttimethatthespectralsignaturesofdifferent
developmentalstagescanbeusedforspeciesdetermination.Previousworkhasachieved100%
accuracyindiscriminatingleavesEucalyptusglobulusLabillandEucalyptusnitensMaiden[25]
PLOSONE|DOI:10.1371/journal.pone.0134521 August27,2015 10/15