基于PLS-LSSVM的谷氨酸发酵产物浓度预测建模

基于PLS-LSSVM的谷氨酸发酵产物浓度预测建模Title: Modeling Glutamic Acid Fermentation Product Concentration Prediction

PLS-LSSVM 基于的谷氨酸发酵产物浓度预测建模 Title:ModelingGlutamicAcidFermentationProduct ConcentrationPredictionUsingPLS-LSSVM Abstract: Glutamicacidisanimportantaminoacidwidelyusedinthe foodandpharmaceuticalindustries.Efficientfermentation processesthatproducehighconcentrationsofglutamicacidare crucialforitscommercialproduction.Inthisstudy,weproposea modelingapproachbasedonPartialLeastSquares(PLS)with LeastSquaresSupportVectorMachine(LSSVM)topredict glutamicacidfermentationproductconcentrations.Theaimof thisresearchistodevelopareliableandaccurateprediction modelthatcanenablereal-timemonitoringandcontrolofthe fermentationprocess. 1.Introduction Glutamicacidisanon-essentialaminoacidthatplaysavital roleinvariousbiologicalprocesses.Itisalsowidelyusedasa flavorenhancerandnutritionaladditiveinthefoodindustry.The fermentationprocessisthemostcommonmethodforglutamic acidproduction,whichinvolvestheconversionofglucoseinto glutamicacidbymicroorganismssuchasCorynebacterium glutamicum. 2.LiteratureReview Previousstudieshavefocusedondevelopingregression modelstopredictglutamicacidfermentationproduct concentrations.Traditionalregressionmethodsoftensufferfrom limitationsduetothenon-linearandcomplexnatureofthe fermentationprocess.Toaddresstheselimitations,advanced machinelearningtechniquessuchasSupportVectorMachines (SVM)andPLShavebeenappliedinvariousfieldstomodel non-linearrelationships. 3.Methodology TheproposedapproachcombinestheadvantagesofPLSand LSSVMtoovercomethelimitationsoftraditionalregression models.PLSisusedtoextracttherelevantinformationfromthe inputvariablesandreducetheirdimensionality.LSSVM,avariant