Choosing a primary model to describe the growth curves of mountain microorganisms (Bioles)

Mathematical models allow describing the dynamics of microbial populations under different environmental conditions. In this work, three models - Logistic, Gompertz and Baranyi-Roberts - were evaluated to characterize the growth of five beneficial bacteria present in a biological mountain soil. Model parameters were optimized using the particle swarm optimization (PSO) algorithm. Statistical analyses, based on the root mean square error (RMSE), the coefficient of determination $\left(\mathrm{R}^2\right)$, the bias factor $\left(B_f\right)$) and the precision factor $A_f$), showed that the results show that the variability between strains suggests that the choice of the optimal model may depend on the specific characteristics of each strain, however, the Logistic model proved to be the most robust and consistent, offering the best fit to the experimental data in most strains.