Growth Kinetics and Predictive Model of Aeromonas hydrophila in a Broth-based System

Introduction: Aeromonas hydrophila has attracted attention as an emerging human pathogen because of its ability to grow at refrigeration temperatures like Listeria monocytogenes and Yersinia enterocolitica. A. hydrophila is neither salt (<5%) nor acid (min. pH 6) tolerant. It can be isolated from many kinds of foods including vegetables, meat (7), fish, seafood, raw milk, and chicken. A. hydrophila has a long survival time in the environment. Based on these reasons, A. hydrophila is of public health significance. 

Purpose: Predictive food microbiology provides quantitative estimation of microbial growth in foods using mathematical modeling. This study provides a predictive model to describe the effect of temperature, pH and concentration of NaCl on the growth of A. hydrophila by response surface methodology (RSM). The model can be used as a reference in controlling A. hydrophila growth without the need for detection of the organism and may be of use for controlling growth. In this study, the growth characteristics of A. hydrophila were determined and a predictive model that could be used practically was developed.

Methods: This study was conducted to evaluate the survival characteristics and growth of A. hydrophila as a function of storage temperature (5 to 40 °C), pH value (6 to 8) and NaCl concentration (0 to 5%) with the aim of building a predictive model. The growth curves generated using a Gompertz equation and the relationship of the growth rate to the growth curves was modeled using a quadratic polynomial equation of RSM.

Results:   A. hydrophila in TSB tended to grow well within a pH range of 6.0 to 8.0 and could not tolerate NaCl concentrations up to 5.0%. The interaction of pH and NaCl concentrations did not significantly affect the SGR. The primary model that we developed to obtain the SGR showed a good fit (R² ≥ 0.980) with the Gompertz equation. A secondary polynomial model was obtained by non-linear regression analysis and calculated as: SGR model = 0.4577+0.0529X₁-0.1641X₂-0.1493X₃-0.0016X₁X₂-0.0001X₁X₃+0.0115X₂X₃0.0006X₁²+0.0114X₂²+0.0150X₃² (X₁=temperature, X₂=pH, X₃=NaCl). The appropriateness of the secondary polynomial model was verified by the mean square error (MSE = 0.0023), bias factor (B_f = 0.922), accuracy factor (A_f = 1.343) and coefficient of determination (R² = 0.937).

Significance: The model was found to be significant and the predicted values that were in agreement with previous studies. The model may be used as a reference in controlling A. hydrophila growth without the need for detection of the organism, but the model needs validation in a food environment before it can be practically applied. Therefore, it is necessary to develop effective control techniques to reduce the risk of A. hydrophila in food samples.