Modelling of the Behaviour of Salmonella enterica serovar Reading on Commercial Fresh-Cut Iceberg Lettuce Stored at Different Temperatures

Abstract

The aim of this study was to model the growth and survival behaviour of Salmonella Reading and endogenous lactic acid bacteria on fresh pre-cut iceberg lettuce stored under modified atmosphere packaging for 10 days at different temperatures (4, 8 and 15 °C). The Baranyi and Weibull models were satisfactorily fitted to describe microbial growth and survival behaviour, respectively. Results indicated that lactic acid bacteria (LAB) could grow at all storage temperatures, while S. Reading grew only at 15 °C. Specific growth rate values (μmax) for LAB ranged between 0.080 and 0.168 h−1 corresponding to the temperatures 4 and 15 °C while for S. Reading at 15 °C, μmax = 0.056 h−1. This result was compared with published predictive microbiology models for other Salmonella serovars in leafy greens, revealing that predictions from specific models could be valid for such a temperature, provided they were developed specifically in lettuce regardless of the type of serovars inoculated. The parameter delta obtained from the Weibull model for the pathogen was found to be 16.03 and 18.81 for 4 and 8 °C, respectively, indicating that the pathogen underwent larger reduction levels at lower temperatures (2.8 log10 decrease at 4 °C). These data suggest that this Salmonella serovar is especially sensitive to low temperatures, under the assayed conditions, while showcasing that a correct refrigeration could be an effective measure to control microbial risk in commercial packaged lettuce. Finally, the microbiological data and models from this study will be useful to consider more specifically the behaviour of S. Reading during transport and storage of fresh-cut lettuce, elucidating the contribution of this serovar to the risk by Salmonella in leafy green products.

Dimensional Analysis Model Predicting the Number of Food Microorganisms

“…Therefore, this undoubtedly simplified the method of predicting microorganisms. However, microbial prediction models generally required the N 0 value ( Park et al, 2020 ; Tarlak et al, 2020 ; Yu et al, 2020 ), which was obtained by direct measurement or curve fitting. Fitting was required at this time because the initial number of spoilage microorganisms in food was usually very low and difficult to detect.…”

“…Therefore, the model proposed in this study, which did not require the N 0 value (of course, this model did not object to the known N 0 ), undoubtedly improved the prediction accuracy. Another advantage of this model was that the specific growth rate that was required to be calculated in the general prediction model was not required here ( Park et al, 2020 ; Tarlak et al, 2020 ; Yu et al, 2020 ), thereby simplifying the calculation process. Moreover, general models require multiple equations to be combined for prediction.…”

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 ].…”

Modelo de crecimiento microbiano para predecir el comportamiento de Salmonella spp. en queso costeño colombiano

“…En términos generales, se pueden construir modelos predictivos con el fin de mejorar la calidad de los alimentos, ya que estos modelos permiten anticiparse al comportamiento de los microorganismos en determinadas matrices alimentarias y tomar así las acciones correctivas pertinentes. El desarrollo de modelos predictivos de crecimiento bacteriano es una herramienta importante para la industria de los alimentos (Tarlak et al, 2020); ya que a través de ellos se puede evaluar el riesgo de crecimiento de bacterias patógenas para prevenir intoxicaciones alimentarias. (Lee et al, 2014;Milkievicz et al, 2020).…”