Modeling the Effects of Temperature, Sodium Chloride, and Green Tea and Their Interactions on the Thermal Inactivation of Listeria monocytogenes in Turkey

Abstract

The interactive effects of heating temperature (55 to 65°C), sodium chloride (NaCl; 0 to 2%), and green tea 60% polyphenol extract (GTPE; 0 to 3%) on the heat resistance of a five-strain mixture of Listeria monocytogenes in ground turkey were determined. Thermal death times were quantified in bags that were submerged in a circulating water bath set at 55, 57, 60, 63, and 65°C. The recovery medium was tryptic soy agar supplemented with 0.6% yeast extract and 1% sodium pyruvate. D-values were analyzed by second-order response surface regression for temperature, NaCl, and GTPE. The data indicated that all three factors interacted to affect the inactivation of the pathogen. The D-values for turkey with no NaCl or GTPE at 55, 57, 60, 63, and 65°C were 36.3, 20.8, 13.2, 4.1, and 2.9 min, respectively. Although NaCl exhibited a concentration-dependent protective effect against heat lethality on L. monocytogenes in turkey, addition of GTPE rendered the pathogen more sensitive to the lethal effect of heat. GTPE levels up to 1.5% interacted with NaCl and reduced the protective effect of NaCl on heat resistance of the pathogen. Food processors can use the predictive model to design an appropriate heat treatment that would inactivate L. monocytogenes in cooked turkey products without adversely affecting the quality of the product.

The Inclusion of the Food Microstructural Influence in Predictive Microbiology: State-of-the-Art

“…This methodology was occasionally already applied in the 1980s and 1990s, but has been widely used in recent literature. An extensive list of example studies exploiting this approach is provided in Table 1 , both for microbial growth [ 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 ] and thermal inactivation [ 55 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ], with a focus on early and recent examples. Interestingly, however, this predictive microbiology approach bears some similarities to the traditional challenge testing approach, in which microbial growth/inactivation experiments were also conducted directly in/on the food product of interest [ 5 ].…”

Inactivation kinetics of food-borne pathogens subjected to thermal treatments: a review

“…under conventional and novel thermal technologies are reviewed but only the primary model (the food-borne pathogen evolution as a function of heating time) is concerned [28]. However, since the inactivation kinetics are actually influenced by several factors, such as different bacterial strains, age of the culture, food composition (fat, NaCl, pH and a w ), processing parameters, and physiological state of the organisms, some researchers have established secondary models for predicting survival curves under different conditions [20,24,[29][30][31][32]. Also omnibus models incorporating the primary and the secondary models are further considered for predicting survival curves of pathogens [23,33,34].…”