Modeling the influence of temperature, water activity and water mobility on the persistence of Salmonella in low-moisture foods

Abstract

Salmonella can survive in low-moisture foods for long periods of time. Reduced microbial inactivation during heating is believed to be due to the interaction of cells and water, and is thought to be related to water activity (a(w)). Little is known about the role of water mobility in influencing the survival of Salmonella in low-moisture foods. The aim of this study was to determine how the physical state of water in low-moisture foods influences the survival of Salmonella and to use this information to develop mathematical models that predict the behavior of Salmonella in these foods. Whey protein powder of differing water mobilities was produced by pH adjustment and heat denaturation, and then equilibrated to aw levels between 0.19±0.03 and 0.54±0.02. Water mobility was determined by wide-line proton-NMR. Powders were inoculated with a four-strain cocktail of Salmonella, vacuum-sealed and stored at 21, 36, 50, 60, 70 and 80°C. Survival data was fitted to the log-linear, the Geeraerd-tail, the Weibull, the biphasic-linear and the Baranyi models. The model with the best ability to describe the data over all temperatures, water activities and water mobilities (f(test)<F(table)) was selected for secondary modeling. The Weibull model provided the best description of survival kinetics for Salmonella. The influence of temperature, aw and water mobility on the survival of Salmonella was evaluated using multiple linear regression. Secondary models were developed and then validated in dry non-fat dairy and grain, and low-fat peanut and cocoa products within the range of the modeled data. Water activity significantly influenced the survival of Salmonella at all temperatures, survival increasing with decreasing a(w). Water mobility did not significantly influence survival independent of a(w). Secondary models were useful in predicting the survival of Salmonella in various low-moisture foods providing a correlation of R=0.94 and an acceptable prediction performance of 81%. The % bias and % discrepancy results showed that the models were more accurate in predicting survival in non-fat food systems as compared to foods containing low-fat levels (12% fat). The models developed in this study represent the first predictive models for survival of Salmonella in low-moisture foods. These models provide baseline information to be used for research on risk mitigation strategies for low-moisture foods.

Modeling and Statistical Issues Related to Salmonella in Low Water Activity Foods

“…The influence of temperature began to show around day 30, after which a significant decline was observed for the data at 22°C, while no significant decline was observed at either −24 or 4°C (Figure 10.1). These observed survival kinetics for Salmonella on raw peanuts are in line with other published literature, where survival curves for Salmonella in low water activity foods do not follow log-linear kinetics and show significant asymptotic tails (Uesugi, Danyluk, and Harris, 2006;2010, Blessington, Mitcham, andKimber et al, 2012;Blessington et al, 2013a;Blessington et al, 2013b;Keller et al, 2013;Santillana Farakos, Frank, and Schaffner, 2013). The data presented in Figure 10.1 indicate that the best descrip tion of Salmonella survival under these conditions requires a model that includes a nonlinear inactivation rate and the ability to incorporate tailing.…”

“…Various primary models are available and can be used to fit microbial survival data: the log-linear model (Bigelow and Esty, 1920), the Geeraerd-tail model (Geeraerd, Herremans, and Van Impe, 2000), the Weibull model (Mafart et al, 2002), the Coroller model (Coroller et al, 2006), and the biphasic linear model (Cerf, 1977). Of the aforementioned models, the Weibull model has been shown to provide the best description of Salmonella survival kinetics in low water activity foods (Mattick et al, 2001;Ma et al, 2009;Abd, McCarthy, and Harris, 2012;Santillana Farakos, Frank, and Schaffner, 2013). All these models are available in GInaFiT (Geeraerd, Valdramidis, and Van Impe, 2005), a free software fitting tool (http://cit.kuleuven.be/biotec/software/GinaFit [last accessed 10 February 2017]).…”

“…The Weibull model has been determined to be the best applicable model to describe survival of Salmonella in low water activity foods (Santillana Farakos, Frank, and Schaffner, 2013;Santillana Farakos et al, 2016). Predictive models developed for Salmonella survival in these foods include models for survival on almonds at −20, 4, and 24 °C assuming log-linear declines developed by Danyluk, Harris, and Schaffner (2006) and Lambertini et al (2012).…”

“…Predictive models developed for Salmonella survival in these foods include models for survival on almonds at −20, 4, and 24 °C assuming log-linear declines developed by Danyluk, Harris, and Schaffner (2006) and Lambertini et al (2012). Santillana Farakos, Frank, and Schaffner (2013) developed secondary models using a Weibull pri mary model to predict survival in a low-fat food model system at temperatures ranging from 21 to 80°C and a w <0.6. More recently, a Weibull-type model able to predict survival of Salmonella in tree nuts at typical storage temperatures, incor porating variability and uncertainty separately was developed (Santillana Farakos et al, 2016).…”

Temperature Resistance of Salmonella in Low-Water Activity Whey Protein Powder as Influenced by Salt Content

“…Salmonella is able to survive in low-aw foods for very long periods of time, even when subject to high temperature (1,10,11). Because most low-aw food products require no further cooking and have a long shelf life, the presence of Salmonella causes outbreaks of long duration that impact large numbers of people.…”