Abstract
In this work, the effect of environmental factors on Staphylococcus aureus (ATCC 13150) biofilm formation in tryptic soy broth was investigated under different ranges of pH (3.0–9.5), ethanol concentration (EtOH 0.0–20.0%), and aw (NaCl, 0.866–0.992). Biofilm formation was quantified using the crystal violet staining method and optical density (OD: 590 nm) measurements. Biofilm formation was significantly stronger at pH and aw close to S. aureus optimal growth conditions, while it was high at EtOH around 2.5–3.5%. Data sets from the difference between the OD measurements of the test and control (ΔOD) were fitted to the cardinal parameter model (CPM) and cardinal parameter model with inflection (CPMI) to describe the effect of the environmental factors. The models showed good quality of fit for the experimental data in terms of calculated RMSE, with the latter ranging from 0.276 to 0.455. CPM gave a good quality of fit compared to CPMI for the environmental factors tested. Optimal pH was close to neutral (6.76–6.81) and biofilm formation was possible till pH = 3.81–3.78 for CPM and CPMI, respectively. Optimum EtOH and aw conditions for biofilm formation were in the range of 1.99–2.75 and 0.98–0.97, respectively. Predicted OD values observed using strain 13150 were very closely correlated to the OD values predicted with strain 12600 with R2 of 0.978, 0.991, and 0.947 for pH, EtOH, and aw, respectively. The cultivable bacterial cells within the biofilm were enumerated using standard plate counting and a linear model was applied to correlate the attached biofilm cells to ΔOD of biofilm formation. It was found that the biofilm formation correlated with S. aureus population growth. At 2.5–3.5% of EtOH the maximum population density was lower than that observed at 0.0% of EtOH. As 2.5–3.5% of EtOH initiated a stronger biofilm formation, biofilm formation seems to be induced by ethanol stress. The development of cardinal parameter models to describe the effect environmental factors of importance to biofilm formation, offers a promising predictive microbiology approach to decrypting the S. aureus population growth and survival ability on food processing surfaces.
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