Use of Logistic Regression for Prediction of the Fate of Staphylococcus aureus in Pasteurized Milk in the Presence of Two Lytic Phages

Abstract

The use of bacteriophages provides an attractive approach to the fight against food-borne pathogenic bacteria, since they can be found in different environments and are unable to infect humans, both characteristics of which support their use as biocontrol agents. Two lytic bacteriophages, vB_SauS-phiIPLA35 (phiIPLA35) and vB_SauS-phiIPLA88 (phiIPLA88), previously isolated from the dairy environment inhibited the growth of Staphylococcus aureus. To facilitate the successful application of both bacteriophages as biocontrol agents, probabilistic models for predicting S. aureus inactivation by the phages in pasteurized milk were developed. A linear logistic regression procedure was used to describe the survival/death interface of S. aureus after 8 h of storage as a function of the initial phage titer (2 to 8 log 10 PFU/ml), initial bacterial contamination (2 to 6 log 10 CFU/ml), and temperature (15 to 37°C). Two successive models were built, with the first including only data from the experimental design and a global one in which results derived from the validation experiments were also included. The temperature, interaction temperature-initial level of bacterial contamination, and initial level of bacterial contamination-phage titer contributed significantly to the first model prediction. However, only the phage titer and temperature were significantly involved in the global model prediction. The predictions of both models were fail-safe and highly consistent with the observed S. aureus responses. Nevertheless, the global model, deduced from a higher number of experiments (with a higher degree of freedom), was dependent on a lower number of variables and had an apparent better fit. Therefore, it can be considered a convenient evolution of the first model. Besides, the global model provides the minimum phage concentration (about 2 ؋ 10 8 PFU/ml) required to inactivate S. aureus in milk at different temperatures, irrespective of the bacterial contamination level.

Resistance of foodborne pathogen coliphages to thermal and physicochemical treatments applied in food manufacture

“…Bacteriophages are the natural enemies of bacteria and have proved to be useful tools against pathogenic E. coli strains (Abuladze et al, 2008;Moradpour et al, 2009;O'Flynn, Ross, Fitzgerald, & Coffey, 2004) and several other foodborne pathogens, such as Listeria monocytogenes (Carlton, Noordman, Biswas, de Meester, & Loessner, 2005;Dykes & Moorhead, 2002;Guenther, Huwyler, Richard, & Loessner, 2009;Holck & Berg, 2009), Campylobacter jejuni (Bigwood, Hudson, Billington, Carey-Smith, & Heinemann, 2008), Salmonella enterica (Bigwood et al, 2008) and Staphylococcus aureus (Bueno, Garcia, Martínez, & Rodríguez, 2012;Garcia, Madera, Martinez, & Rodriguez, 2007;Obeso et al, 2010). However, viability and activity of these phages must be assessed under typical physicochemical conditions found in each food matrix in order to evaluate real effectiveness of phages on these complex environments.…”

The formation ofStaphylococcus aureusenterotoxin in food environments and advances in risk assessment

“…Similarly, Valero et al 95 developed a model for the probability of growth and found an abrupt transition of the interface between growth and no growth at low temperatures, where optimal levels of pH and water activity were required for growth. Obeso et al 115 described the effect of initial lytic phage titers and initial S. aureus contamination of pasteurized milk on the probability of S. aureus survival at different temperatures.…”

“…112,113 Available kinetic tertiary models can be used to predict growth and lag time based on various input values, for example, temperature, pH, water activity, nitrite concentration and aerobic/anaerobic conditions (PMP) (ComBase Predictor) and temperature in various types of meat (THERM). 114 An alternative approach is to describe the boundary between growth and no growth, 95 or survival/death, 115 the time before growth, 116 or the probability of growth, as a function of environmental parameters. Such models can be useful in the design of food processes to prevent growth and enterotoxin production.…”

Application of bacteriophages in post-harvest control of human pathogenic and food spoiling bacteria

“…Added to milk, phages were able to decrease rapidly the viable count of S. aureus during cheese curd manufacture. The estimated minimum phage concentration required for inactivating S. aureus in milk at different temperatures, irrespective of the bacterial contamination level, was approximately 2 Â 10 8 PFU/ml (Obeso et al, 2010). The fact that phages s88 and s35 were fully active in milk, could be related to their milk origin and adaptation to be infective in milk environment (García et al, 2007).…”