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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 B S T R A C T Six essential elements, cadmium and lead were determined in (0429 mg k g -and 0.221 mg kg-respectively) are taken into account, the daily intake contribut*ion of these metals will be 1.4 p g day-' for cadmium and I1 pg day-' f o r lead.