Modelling biofilm formation of Salmonella enterica ser. Newport as a function of pH and water activity

•Biofilm formation by Salmonella enterica as a function of pH and aw was assessed.•The developed biofilm formation model was validated against independent data.•Probabilistic modelling of the pathogen's biofilm formation boundaries was conducted.•The effect of environment on biofilm formation was quantitatively expressed.

The effect of pH and water activity (aw) on the formation of biofilm by Salmonella enterica ser. Newport, previously identified as a strong biofilm producer, was assessed. Biofilm formation was evaluated in tryptone soy broth at 37 °C and at different combinations of pH (3.3–7.8) and aw (0.894–0.997). In total, 540 biofilm formation tests in 108 pH and aw combinations were carried out in polystyrene microtiter plates using crystal violet staining and optical density (OD; 580 nm) measurements. Since the individual effects of pH and aw on biofilm formation had a similar pattern to that observed for microbial growth rate, cardinal parameter models (CPMs) were used to describe these effects. CPMs described successfully the effects of these two environmental parameters, with the estimated cardinal values of pHmin, pHopt, pHmax, awmin and awopt being 3.58, 6.02, 9.71, 0.894 and 0.994, respectively. The CPMs assumption of the multiplicative inhibitory effect of environmental factors was validated in the case of biofilm formation using additional independent data (i.e. 430 OD data at 86 different combinations of pH and aw). The validation results showed a good agreement (r2 = 0.938) between observed and predicted OD with no systematic error. In the second part of this study, a probabilistic model predicting the pathogen's biofilm formation boundaries was developed, and the degree of agreement between predicted probabilities and observations was as high as 99.8%. Hence, the effect of environmental parameters on biofilm formation can be quantitatively expressed using mathematical models, with the latter models, in turn, providing useful information for biofilm control in food industry environments.