ISSN:
1750-3841
Source:
Blackwell Publishing Journal Backfiles 1879-2005
Topics:
Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
,
Process Engineering, Biotechnology, Nutrition Technology
Notes:
: 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.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1111/j.1365-2621.2003.tb05723.x
