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Model Psychrotolerant Lactobacillus spp. - growth and growth boundary in seafood and meat products
References

Mejlholm, O. and Dalgaard, P. (2007b). Modeling and predicting the growth of lactic acid bacteria in lightly preserved seafood and their inhibiting effect on Listeria monocytogenes  J. Food Prot. 70, 2485-2497.

Mejlholm, O, Dalgaard, P. (2013). Development and validation of an extensive growth and growth boundary model for psychrotolerant Lactobacillus spp. in seafood and meat products. Int. J. Food Microbiol. 167, 244-260.

 

Primary growth model Logistic model with delay
Secondary growth model Simplified cardinal parameter type model
Environmental parameters in model Temperature, atmosphere (CO2), water phase salt/aw, pH, smoke components/phenol, nitrite and organic acids in water phase of product (acetic acids, benzoic acid, citric acid, diacetate, lactic acid and sorbic acids)
Product validation studies Fresh and processed seafood and meat products (Mejlholm & Dalgaard 2007b; 2013, 2015). 
Range of applicability Temperature (0-25C), atmosphere (0-100 % CO2), water phase salt (0.1-6.4 %), pH (5.3-7.7), smoke components/phenol (0-21.2 ppm), nitrite (0-209 ppm in product), acetic acid (0-12600 ppm in water phase), benzoic acid (0-1800 ppm in water phase), citric acid (0-7300 ppm in water phase), diacetate (0-3000 ppm in water phase), lactic acid (0-67000 ppm in water phase) and sorbic acid (0-1600 in water phase).

This model includes the effect of 12 environmental parameters (See Table above) on the growth of psychrotolerant Lactobacillus  spp. in fresh and processed seafood and meat products. FSSP predicts growth of these psychrotolerant lactic acid bacteria (LAB) without using a lag phase as numerous studies have confirmed their grow without a significant lag time in fresh and lightly preserved seafood and meat products. See the FSSP dialog box and output window below (Fig. 1).

Fig1

Fig. 1. The graph above shows how a combination of benzoic and sorbic acid can prevent growth of LAB at 5C in a product with pH 5.5 (blue curve). FSSP specifically indicates  the time needed for the concentrations of LAB to increase 100-fold and to reach 7.0 log (cfu/g) under the selected product characteristics and storage conditions. The concentrations of LAB shown in the bar at the bottom of the output window was obtained by using the mouse to click on the graph at a specific point in time.

 

FSSP can predict growth of LAB under changing temperature storage conditions. Simple temperature profiles can be typed in as 'Series of constant temperatures' whereas actual product temperature profiles most often are entered as 'Temperature profiles from data loggers' (Fig. 2).

 

Fig2

Fig. 2. Effect of two simple temperature profiles on predicted growth of psychrotolerant Lactobacillus spp.

 

Secondary growth model:
Eqn. 2 below shows the extensive secondary growth model for psychrotolerant Lactobacillus spp. This simplified cardinal parameter type model describe how the maximum specific growth rate  (max, h-1) at a reference temperature of 25C is reduced when environmental parameters become less favourable for growth. The terms for each of the environmental parameters all has a value between 0 and 1. FSSP predicts the growth boundary of psychrotolerant Lactobacillus spp. as the combination of environmental conditions resulting in max = 0 and a specific term (ξ) is included in the cardinal parameter models to take into account the effect of interaction between all the different environmental parameters (Eqn. 2).

 

Eqn2

 

 

 

Eqn. 2. Secondary growth and growth boundary models psychrotolerant lactic acid bacteria
 
Evaluation and validation of the psychrotolerant Lactobacillus spp. growth model: 
The model included in FSSP for prediction of psychrotolerant LAB growth has been evaluated by comparison of observed and predicted growth in inoculated challenge tests and in naturally contaminated products as shown in the Table below. The model performed very well and the bias factors was within the ranges for successful model validation of 0.85 < Bias factor < 1.25 (Mejlholm and Dalgaard, 2013).
Model Data used for evaluation and validation of the model Indices of performance
Lactic acid bacteria growth rate model (Mejlholm & Dalgaard, 2007b, 2013)

229 growth curves for lactic acid bacteria in inoculated challenge tests or naturally contaminated  seafood and meat products (Mejlholm & Dalgaard 2013)

Bias factor            = 1.1

Accuracy factor    = 1.3