Journal of Food Science · 2003 · 59 citations · 24 references
Life SciencesMicrobiologyBiological Systems ModelingBioprocess EngineeringFood MicrobiologyBioprocess KineticsBioprocess ModelingCell Growth KineticsEnzyme KineticsKinetic ModelingMicrobial GrowthBacterial Growth KineticsKinetic Parameter EstimationKinetic Parameter IdentificationKinetics Parameter EstimationMathematical Models Of Bacterial GrowthMicrobial Growth KineticsMicrobial KineticsParameter Estimation In Kinetic Models
ABSTRACT: Artificial neural networks (ANN) was evaluated and compared with Response Surface Model (RSM) results using growth response data for E.coli O157:H7 as affected by 5 variables: pH, sodium chloride, and nitrite concentrations, temperature, and aerobic/anaerobic conditions. The best ANN obtained, where the 2 kinetic parameters, growth rate and lag‐time, were estimated jointly, contained 17 parameters and displayed a slightly lower Standard Error of Prediction (% SEP) than those obtained with RSM. Mathematical lag‐time validation with additional data gave a lower %SEP for ANN (18%) than for RSM (27%), although growth‐rate values were the same (22%). ANN thus should provide the innovative possibility of obtaining a single predictive model for the estimation of several kinetic parameters.
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A non-autonomous differential equation to modelbacterial growth
József Baranyi, T.A. Roberts · Food Microbiology · 1993 · 525 citations