Improved prediction of tablet properties with near-infrared spectroscopy by a fusion of scatter correction techniques

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Improved prediction of tablet properties with

near-infrared spectroscopy by a fusion of scatter

correction techniques

Puneet Mishra, Alison Nordon, Jean-Michel Roger

To cite this version:

ContentslistsavailableatScienceDirect

Journal

of

Pharmaceutical

and

Biomedical

Analysis

Improved

prediction

of

tablet

properties

with

near-infrared

spectroscopy

by

a

fusion

of

scatter

correction

techniques

Puneet

Mishra

_,

_Alison

_Nordon

_,

_Jean-Michel

_Roger

a_{WageningenFoodandBiobasedResearch,BornseWeilanden9,P.O.Box17,6700AA,Wageningen,TheNetherlands}

b_{WestCHEM,DepartmentofPureandAppliedChemistryandCentreforProcessAnalyticsandControlTechnology,UniversityofStrathclyde,Glasgow,G1}

1XL,UnitedKingdom

c_{ITAP,INRAE,MontpellierSupAgro,UniversityMontpellier,Montpellier,France} d_{ChemHouseResearchGroup,Montpellier,France}

a

r

t

i

c

l

e

i

n

f

o

Articlehistory: Received22August2020

Receivedinrevisedform6October2020 Accepted7October2020

Availableonline10October2020 Keywords: Multiblock Fusion Spectroscopy Pre-processing Multivariate

a

b

s

t

r

a

c

t

Near-infrared(NIR)spectraofpharmaceuticaltabletsgetaffectedbylightscatteringphenomena,which masktheunderlyingpeaksrelatedtochemicalcomponents.Oftenthebestperformingscattercorrection techniqueisselectedfromapoolofpre-selectedtechniques.However,thedatacorrectedwithdifferent techniquesmaycarrycomplementaryinformation,hence,useofasinglescattercorrectiontechniqueis sub-optimal.Inthisstudy,theaimistoprovethatNIRmodelsrelatedtopharmaceuticalscandirectly benefitfromthefusionofcomplementaryinformationextractedfrommultiplescattercorrection tech-niques.Toperformthefusion,sequentialandparallelpre-processingfusionapproacheswereused.Two differentopensourceNIRdatasetswereusedforthedemonstrationwheretheassayuniformityand activeingredient(AI)contentpredictionwastheaim.Asabaseline,thefusionapproachwascompared topartialleast-squaresregression(PLSR)performedonstandardnormalvariate(SNV)correcteddata, whichisacommonlyusedscattercorrectiontechnique.Theresultssuggestthatmultiplescatter cor-rectiontechniquesextractcomplementaryinformationandtheircomplementaryfusionisessentialto obtainhigh-performancepredictivemodels.Inthisstudy,thepredictionerrorandbiaswerereducedby upto15%and57%respectively,comparedtoPLSRperformedonSNVcorrecteddata.

©2020TheAuthor(s).PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCC BY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction

Near-infrared(NIR)spectroscopyiswidelyusedforrapidand non-destructive analysis of pharmaceutical tablets [1,2]. How-ever,themainchallengewithNIRspectroscopyisrelatedtodata modelling,whichcaneasilybecomesub-optimalifnoproper inves-tigationisperformedduringthepre-processingstage[3–5].The interactionofNIRlightwithsamplesresultsintwomajor opti-calphenomena,i.e.,absorptionand scattering[6].Absorption is theresultofspecificchemicalmoleculesabsorbingcertain wave-lengths of light, whereas scattering is a result of the complex interactionoflightwiththephysicalstructureofsamples[6,7].NIR dataarewidelyaffectedbylightscattering,whichcanbeidentified asadditiveandmultiplicativeeffectsinspectra[6,8].

TraditionaldataanalysisrelatedtoNIRdatamodellinginvolves explorationofdifferentscattercorrectiontechniquestoremovethe

∗ Correspondingauthor.

E-mailaddress:[email protected](P.Mishra).

scatteringeffectsfromthespectrasuchthattheabsorption charac-teristicscanbeusedforproperestimationofchemicalcomponents [9].However,recentlyMishraetal.,(2020)demonstratedthatthe NIRmodellingshouldnotaimtoselectasinglescattercorrection techniquebutshouldutilisetheinformationextractedbyseveral scattercorrectiontechniques[5,10,11].Thisisbecausedifferent scattercorrectiontechniquesremovetheeffectsoflightscattering differentlyandmayrevealcomplementaryinformation,whichif modelledcanleadtohigherperformancemodels[5]. Complemen-taryinformation fromdatapre-processed withdifferentscatter correctiontechniquescanbefusedusingtherecentlydeveloped sequentialandparallelpre-processingfusionapproaches[10,12].

Inthisstudy,theaimistoprovethatNIRmodelsof pharmaceuti-calproductscandirectlybenefitfromthefusionofcomplementary informationextractedfrommultiplescattercorrectiontechniques. Toperformthefusion,sequentialandparallelpre-processingfusion approaches wereused.TwodifferentopensourceNIRdatasets wereusedforthedemonstrationwherethepredictionof assay uniformityandactiveingredient(AI) contentwastheaim.Asa baseline,thefusionapproachwascomparedtopartialleast-squares

https://doi.org/10.1016/j.jpba.2020.113684

2 P.Mishra,A.NordonandJ.-M.Roger/JournalofPharmaceuticalandBiomedicalAnalysis192(2021)113684

regression(PLSR)performedonstandardnormalvariate(SNV) cor-recteddata,whichisacommonlyusedscattercorrectiontechnique.

2. Materialsandmethods

2.1. Dataset

Twoopensourcetabletdatasetswereusedforthisstudy.The firstwasthe‘NIRshootout2002’datasetpublishedbythe Inter-nationalCouncil for Near-infrared Spectroscopy.The datawere downloadedfromtheofficialwebsiteofEigenvectorResearchInc, USAinMATALABreadableformat(http://www.eigenvector.com/ data/tablets/index.html).Thedatainthedownloaded.matfile con-tainedspectrafrom twoinstruments.In this study,thespectra correspondingonlytoinstrument1wasused.Intotal,654tablets weremeasuredwithNIR(600–1898nm)andreferenceanalysis (assayuniformity).TheseconddatasetcomprisedNIRtransmission datarelatedtomeasurementof310tabletsandthe correspond-ingactiveingredient(AI)content(%).Thedatawereobtainedfrom http://www.models.life.ku.dk/Tabletsandwerethesameas pre-sentedintheoriginalwork[13]. Thespectral rangeofthisdata setwas7398–10507cm−1.Forbothdatasets,thesampleswere dividedintocalibration(60%)andtest(40%)setusingthe Kennard-Stonealgorithm[14].

2.2. Dataanalysis

2.2.1. Scattercorrectionmethods

Inthestudy,fourofthemostcommonlyusedscattercorrection techniqueswereselected[5].Thetechniqueswere2ndderivative [4], variablesorting for normalization (VSN) [8], standard nor-malvariate(SNV)[15]andmultiplicativescattercorrection(MSC) [7]. The second derivativeestimation wasperformed usingthe Savitzky-Golaymethodwitha2ndorderpolynomialand21-point smoothing filter. All the pre-processing methods were imple-mentedasdiscussedin[4]andinMATLAB,Natick,MA,USA. 2.2.2. Partialleast-squaresregression

PLSRisacommonregressionmethodusedforNIRdata mod-elling[16].BymaximisingthecovarianceoftheNIRdatawiththe response(s),PLSRidentifiesthesubspaceoflatentvariables(LVs) onwhichthehigh-dimensionaldatacanbetransformedtogive alow dimensioninformation concentratedspace.This

transfor-mationguaranteesthat thedataarerelevant forpredictingthe responsevariables.Inthestudy,PLSRwasperformedusing MAT-LAB’sbuilt-infunction‘plsregress’,witha10-foldcrossvalidation procedureapproachusedtoselecttheoptimalnumberoflatent variables(LVs).

2.2.3. Sequentialandparallelpre-processingthrough orthogonalisation

Thesequentialandparallelpre-processingthrough orthogonali-sation(SPORTandPORTO)approachesareinspiredfrommultiblock dataanalysisinchemometrics[12].Thesequentialapproachcalled SPORTisbasedonsequentialorthogonalizedpartialleast-squares regressionandthePORTOapproachonparallelorthogonalized par-tialleast-squaresregression.AschematicoftheSPORTandPORTO approachesispresentedinFig.1.InSPORT(seeFig.1A),initially, aPLSregressionmodelisfittedbetweenYandtheXblockafter applicationofthefirstpre-processingmethod,andthescoresfor thefirstblock(T1)areobtained.Then,theXblockafter applica-tionofthesecondpre-processingmethodisorthogonalizedwith respecttothescores(T1)ofthefirstregression.Thenthe residu-alsofYarefittedtotheorthogonalizedXblockafterapplication ofthesecondpre-processingmethodandthescores(T2)are esti-mated.Theprocedureiscontinuedforasmanyblocks(4inthis case)astherearepre-processingmethods.Inthiswork,theorder ofapplicationofthescattercorrectionmethodswas2ndderivative, VSN,SNVandMSC,makingatotalof4blocksofdata.Inthecase ofPORTO,acombinationofPLSregression,generalizedcanonical analysis(GCA)andmultipleorthogonalizationstepsareperformed withtheaimofextractingthecommonanddistinctinformation presentedindatapre-processedwithdifferentscattercorrection methods.TheconceptofPORTOistoidentifycommonand dis-tinctinformationasshowninFig.1B.Thethreecirclesrepresent datapre-processedwiththreedifferentscattercorrection meth-odsandthelettersDandCindicatethedistinctandthecommon information,respectively.

Optimisingthe number of LVs is highly important for both SPORTandPORTO.InthecaseofSPORT,allpossiblecombinations of LVswereexploredwith theoptimum numberchosen based onthelowestRMSECV.InPORTO,severallocalcross-validations (CVs)wereperformedinsequence,asdiscussedin[17].SPORTwas implementedwiththealgorithmpresentedin[12]andwithfreely availablemultiblockdataanalysistoolbox[18].PORTOwas imple-mentedusingthemulti-blockdataanalysiscodesfromNOFIMA

Fig.2.SummaryofPLSRappliedtoSNVpre-processeddata,SPORTandPORTOmodels.(A)PLSRpredictionforassay(mg),(B)PLSRpredictionforAPIcontent(%),(C)SPORT predictionforassay(mg),(D)SPORTpredictionforAPIcontent(%),(E)PORTOpredictionforassay(mg),and(F)PORTOpredictionforAPIcontent(%).

(https://nofima.no/en/)fortheimplementationofparallel orthog-onalisedpartialleast-squares(POPLS).Allanalysiswasperformed inMATLAB2017b(TheMathWorks,Natick,USA).

3. Results

3.1. PLSRmodellingvsSPORTvsPORTO

TheresultsfromPLSR withSNVpre-processed dataandthe pre-processingfusionapproaches(SPORTandPORTO)areshown inFig.2.Itcanbeseenthatfusionofinformationfromdifferent scattercorrectionmethodsimprovedthepredictiveperformance ofthemodels.Inthecaseofpredictionofassayuniformitywith NIRdata (Fig.2A,C,E),theSPORT approach decreased theerror and biasby 9% and 47 %,respectively, compared toPLSR with

SNVpre-processeddata.ThePORTOapproachfurtherdecreased theerrorandbiasby 15%and 57%,respectively,compared to PLSRwithSNVpre-processeddata.ForpredictionoftheAI con-tent with NIR data(Fig. 2B,D,F), the main improvements were observedinerrorreductionwitha6%and3%decreaseusingSPORT andPORTOapproach,respectively.Theimprovementwasattained withthesamenumberofLVsasforthePLSRmodelconstructed usingSNVpre-processeddata(3LVs).Further,inallcasesthebest modelswereobtainedusingLVsfromdatacorrectedusing mul-tiplescattercorrectionmethods,explainingthatcomplementary informationisbeingextractedandmodelledbythepre-processing fusionapproaches(PORTOandSPORT).

com-4 P.Mishra,A.NordonandJ.-M.Roger/JournalofPharmaceuticalandBiomedicalAnalysis192(2021)113684

Fig.3.LoadingscorrespondingtoPLSRappliedtoSNVpre-processeddata(A), SPORT(B)andPORTO(C)modellingapproaches.(A)Threeloadingswereextracted byPLSRforoptimalmodelling,(B)threeloadingswereextractedbySPORTapproach (2fromVSNpre-processeddataand1fromSNVpre-processeddata),and(C)three componentswereextractedbythePORTOapproach(1commoncomponentforall pre-processingmethodsand2distinctcomponentsfrom2ndderivative).

paredtotheloadingsofstandardPLSR,theloadingsofSPORTand PORTOarelessnoisy,andespeciallyinthecaseofPORTOthe load-ingweightsinthespectralregionaround10500cm−1arecloseto 0withtheregion<9000cm−1,whicharisesfromtheC_≡N over-tonesfromtheactiveingredient,havingthehighestloadings[13]. Suchefficientmodellingcanbeunderstoodasthereasonforthe improvementinthemodelperformance.

4. Conclusions

This study has proved that NIR models of pharmaceuticals tabletscan beimprovedwithafusion ofcomplementary infor-mation present in data pre-processed using different scatter correctionmethods.Bothsequential and parallelapproaches to pre-processingcanbeusedforthefusion.Inthisstudy,the paral-lelapproachperformedslightlybettercomparedtothesequential approach.Theerrorandbiasdecreasedbyupto15%and57%, respectively.Basedontheresultsobtained,itisrecommendedthat fusion of multiplescattercorrection techniquesshouldbe

per-formedratherthanspendingtimeonexploringandidentifyingthe bestscattercorrectiontechnique.

CRediTauthorshipcontributionstatement

PuneetMishra:Conceptualization,Datacuration,Investigation.

AlisonNordon:Formalanalysis,Writing-review&editing. Jean-MichelRoger:Formalanalysis,Writing-review&editing.

DeclarationofCompetingInterest

Theauthorsdeclarethattheyhavenoknowncompeting finan-cialinterestsorpersonalrelationshipsthatcouldhaveappearedto influencetheworkreportedinthispaper.

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