earticle

영어

Bootstrap method, a computer-intensive statistical technique to estimate the distribution of a statistic was appliedto deal with uncertainty and variability of the experimental data in stochastic prediction modeling of microbial growth on achill-stored food. Three different bootstrapping methods for the curve-fitting to the microbial count data were compared indetermining the parameters of Baranyi and Roberts growth model: nonlinear regression to static version function withresampling residuals onto all the experimental microbial count data; static version regression onto mean counts at samplingtimes; dynamic version fitting of differential equations onto the bootstrapped mean counts. All the methods outputted almostsame mean values of the parameters with difference in their distribution. Parameter search according to the dynamic form ofdifferential equations resulted in the largest distribution of the model parameters but produced the confidence interval of thepredicted microbial count close to those of nonlinear regression of static equation.