The impact of temperature on the growth of Pseudomonas aeruginosa in mineral waters originated from different wells: A predictive approach

Abstract

This study aimed to evaluate the behavior of Pseudomonas aeruginosa (PSA) in natural mineral water sourced from three different extraction wells and stored at various temperatures (10, 12, 20, 23, and 30 °C) to calculate the kinetic growth parameters of this microorganism through predictive modeling. The physicochemical characterization of waters was also evaluated at the time of collection, and included the analysis of 40 different minerals, and quality parameters such as pH, conductivity, oxidation-reduction potential (ORP), total dissolved solids (TDS), salinity (PSU), and temperature (T). PSA survived in raw mineral water incubated at 12, 20, 23, and 30 °C; however, no growth was observed at 10 °C. Growth curves started with an initial population of ~ 2.5–3 log CFU/mL, and final PSA populations ranged from 3.5 to 4.9 log CFU/mL. The maximum specific growth rate (μmax) at 30 °C varied among the wells, with Well P-07 showing the highest growth rate (0.2 h−1), followed by Well P-08 (0.195 h−1) and well P-01 (0.133 h−1). At 12 °C, well P-01 exhibited the highest growth rate (μmax = 0.22 h−1), indicating a influence of mineral composition in the growth of PSA. The lag time (λ) also varied, with minimum values of 2.4 ± 0.1 h at 30 °C and maximum values of 41.6 ± 0.2 h at 12 °C. From these primary estimated parameters, it was possible to obtain five robust secondary models to describe the influence of temperature on the maximum growth rates and lag phase of PSA in the well. The estimated PSA growth parameters at 20 and 23 °C were subjected to a hierarchical cluster analysis and correlation plots to verify the influence of the physicochemical composition of the waters on the PSA behavior at each well's specific annual average temperature. This analysis confirmed a positive relationship (p < 0.05) between the presence of minerals (Ca, Fe, Sr, Mn, Na) and ions (SO4−3, Cl−) and the PSA lag phase time. These results underscore the need for tailored water quality management strategies that consider chemical composition and temperature to address specific microbial contamination risks.