Thermal Inactivation, Growth, and Survival Studies of Listeria monocytogenes Strains Belonging to Three Distinct Genotypic Lineages

Abstract

Twenty-one Listeria monocytogenes strains belonging to three different genotypic lineages were evaluated for differences between lineages and between individual strains with respect to thermal inactivation, growth, and survival. Three sets of heat inactivation conditions (60°C, pH 6.0, and 0.5 M lactate; 55°C, pH 6.0, and 0.5 M lactate; and 50°C, pH 4.0, and 0.5 M lactate) were used on strains grown in modified brain heart infusion (BHI) broth with and without glucose. Two sets of growth conditions (35°C, pH 6.5, and 0.1 M lactate and 5°C, pH 6.5, and 0.1 M lactate) were used with modified BHI broths to determine lag phases and exponential growth rates. Two sets of conditions (28°C, pH 4.0, and 1 M lactate and 28°C, pH 4.5, and 0.5 M lactate) were used with modified BHI broth to determine survival times (D-values). Thermal inactivation D-values were consistently lowest for lineage III, but differences were not significant for any set of conditions tested. Some significant differences were observed between lineages with respect to some of the growth and survival conditions tested. Extensive strain-to-strain variation was observed for all parameters tested. Average coefficients of variation for the thermal inactivation, growth, and survival studies were 0.31, 0.18, and 0.26, respectively. Strain-to-strain variations were approximately equal to the uncertainties associated with the analytical procedures. The results obtained indicate a diversity among strains encountered in food processing that must be accounted for in process calculations and risk assessments.

Impact of microbial variability on food safety and quality

“…This result was in agreement with the result of an Escherichia coli study where the magnitude of strain variation was greater than the uncertainties calculated from experimental procedures using single strains or cocktails (Whiting and Golden, 2002). In contrast, strain variability was reported to be similar to the biological variability in another study using L. monocytogenes (De Jesús and Whiting, 2003). Similarity between strain variability and biological variability was also reported from a recent publication using Salmonella enterica strains, where the coefficient of variation (CV) of strain variability on specific inactivation rate (k heat ) was 18.3% compared to the CV among replicates of 10.2±6.5% ).…”

“…In both studies however, the factors reported to influence the D-value were smaller than the variability of all published data, except for the effect of high salt concentration. To date, several studies have focused on investigating the effect of strain variability on thermal resistance of vegetative cells De Jesús and Whiting, 2003;Mackey et al, 1990;Ng et al, 1969;Sörqvist, 1994) and spores (Berendsen et al, 2014;Luu-Thi et al, 2014). However, the results between studies, even those using the same species, were different.…”

“…Similarly to growth kinetics, strain variability in thermal inactivation kinetics has also been studied for vegetative cells De Jesús and Whiting, 2003;Mackey et al, 1990;Ng et al, 1969;Sörqvist, 1994;Whiting and Golden, 2002) and spores (Berendsen et al, 2015;Luu-Thi et al, 2014). Although differences between strains were reported, strain variability was suggested to be more or less similar to within strain (reproduction) variability in previous studies using L. monocytogenes and Salmonella (De Jesús and Whiting, 2003;.…”

“…Moreover, the strain variability is obtained by taking measurement using different strains from the same species or more widely known as between strain variability. Although belonging to one species, each strain might show different genotypic and phenotypic characteristics (De Jesús and Whiting, 2003;Donaldson et al, 2009;Nelson et al, 2004;Sanders et al, 2015;Siezen et al, 2010;Siezen and van Hylckama Vlieg, 2011;Whiting and Golden, 2002). Since one species consists of a wide range of strains, one might expect that strain variability is higher than reproduction variability and experimental error.…”

“…However, these parameters, such as the maximum specific growth rate ( max ) (Barbosa et al, 1994;Begot et al, 1997;, lag phase (Aguirre et al, 2013), growth limit Van der Veen et al, 2008), D-value (De Jesús andWhiting, 2003;Linton et al, 1990;Lou and Yousef, 1996), and z-values , vary depending on several factors amongst others within and between strain, cells history, physiological state of the cells and food matrices. Therefore, the prediction results might vary from one study to the others due to differences in the parameters used to generate the prediction.…”