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Performance evaluation of preprocessing techniques for near-infrared spectroscopy signals

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MicroprocessorsandMicrosystemsxxx(xxxx)xxxPleasecitethisarticleas:G.Rajalakshmi,A.Gopal,MicroprocessorsandMicrosystems,https://doi.org/10.1016/j.micpro.2020.103372Availableonline5November20200141-9331/©2020ElsevierB.V.Allrightsreserved.Performanceevaluationofpreprocessingtechniquesfornear-infraredspectroscopysignalsG.Rajalakshmia,*,A.GopalbaSathyabamaInstituteofScience&Technology,Chennai-119,IndiabCEERI,CSIRMadrascomplex,Taramani,Chennai-113,IndiaARTICLEINFOKeywords:ChemometricsDatapreprocessingZ-scoreF1scoreNearIRspectroscopyDeeplearningClassifiertechniquesABSTRACTNear-infraredspectroscopyisanon-destructiveandfirmanalyticaltechniquewhichprovidesuniqueinforma-tion.Chemometricsisapotentialtoolforbothqualitativeandquantitativeanalysisinthepharmaceuticalin-dustries,agrobasedchemicalindustries,petrochemicalindustryandinfoodindustries.NIRSisasuitablemethodforanalysisofsolid,liquidandgassamples.ThustheNearInfraredspectroscopyisfrequentlyusedanalyticalmethodespeciallyinfoodqualitycontrollaboratories.Thefoodindustryincludesgradingoffruits,seeds,spices,oilandmilk.Adulterationisaseriousissuefacedbytheconsumerspurchasingthefoodproducts.Theadul-terationaimsatincreasingthequantityofthefoodproductandtherebythequalityisreducedautomatically.Honeyisonesuchproductbeingcontaminatedbythesugar,beetandcornsyrupsinthemarket.ThepresentworkaimsatdeeplearningapproachesinstudyingtheperformanceofthedataobtainedfromNIR.NaiveGaussian,SVM,LogisticRegression,GradientBoosting,RandomForestasclassifiersan

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