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基于偏最小二乘回归的藻类荧光光谱特征波长选取.docx

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基于偏最小二乘回归的藻类荧光光谱特征波长选取 Abstract: Inrecentyears,theapplicationofremotesensingtechnologyinthestudyofwaterqualityhasattractedincreasingattention.Thestudyofalgalfluorescencespectrumcharacteristicscanreflectthechangesinwaterqualityandprovideabasisfortheearlywarningofwaterpollution.Thispaperappliesthepartialleastsquaresregression(PLSR)methodtoselectthecharacteristicwavelengthsofalgalfluorescencespectra.Theresultsshowthattheselectionofcharacteristicwavelengthscaneffectivelyreflectthechangesinwaterquality. Introduction: Wateristhesourceoflife,andwaterqualitydirectlyaffectshumanhealthandecologicalbalance.Withtherapiddevelopmentofmodernindustryandagriculture,waterpollutionhasbecomeincreasinglyserious,andthetraditionalmonitoringmethodscannotmeettheneedsofreal-time,accurate,andcomprehensivemonitoringofwaterquality.Remotesensingtechnologyhastheadvantagesofwidecoverage,real-timemonitoring,andadvancedinformationprocessingmethods,whichprovideanewwayforwaterqualitymonitoring.Fluorescencespectroscopyisasensitiveandrapidmethodforanalyzingwaterqualitatively,andthefluorescenceofchlorophyllcanreflectthechangesinaquaticprimaryproductivityandthecompositionofphytoplanktonspecies.Therefore,thestudyofalgalfluorescencespectrumcharacteristicscanprovideascientificbasisfortheearlywarningofwaterpollution. Methodology： ThestudyusedaPLSRregressionmodeltoselectthecharacteristicwavelengthsofalgalfluorescencespectrabasedonthespectraldataobtainedfromthelaboratory.Duringtheexperiment,thefluorescencespectraofalgaeweremeasuredunderdifferentwaterqualityconditions,andthespectralfeaturesofdifferentwatersampleswereanalyzed.Then,thePLSRmodelwasconstructed,andthecorrelationbetweenthespectralcharacteristicsandwaterqualityparameterswasanalyzed.Themodelwastrainedandvalidatedusingcross-validation,andtheR2androot-mean-squareerror(RMSE)valueswerecalculatedtoevaluatethemodel'saccuracy.Finally,thecharacteristicwavelengthswereselectedbasedontheimportanceofthespectralvariablesinthePLSRmodel. Results: ThePLSRmodelwasusedto