Scientific journal
Journal of Food and Nutrition Research
Summary No. 4 / 2021
Tarlak, F.
Development of a new mathematical modelling approach for prediction of growth kinetics of Listeria monocytogenes in milk
Journal of Food and Nutrition Research, 60, 2021, No. 4, s. 285-295
Fatih Tarlak, Department of Nutrition and Dietetics, Faculty of Health Sciences, Istanbul Gedik University, Cumhuriyet Street 1, 34876 Kartal, Istanbul, Turkey. E-mail: ftarlak@gtu.edu.tr
Received 15 July 2021; 1st revised 28 August 2021; 2nd revised 10 September 2021; accepted 14 September 2021; published online 12 October 2021.
Summary: The main objective of the present study was to develop a new modelling method, inverse dynamic modelling approach, as an alternative to two-step modelling approach, which is traditionally used n predictive food microbiology. For this purpose, the growth data of Listeria monocytogenes in milk subjected to isothermal and non-isothermal storage conditions were gathered from previously published growth curves. The bacterial growth data were described as a function of time and temperature using the direct two-step, direct one-step and inverse dynamic modelling approaches based on the Baranyi and Huang models. Maximum specific growth rate and lag phase duration estimated by different modelling approaches and primary models were statistically compared. Results revealed that there was no significant difference (p > 0.05) between the growth kinetic parameters obtained from direct and inverse modelling approaches. The prediction capability of inverse dynamic modelling approach was validated by externally gathering growth curves. The inverse dynamic modelling approach provided satisfactory statistical indices (0.99 > Bias factor > 1.10 and 1.16 > Accuracy factor > 1.19), meaning that it can be reliably used as an alternative way of describing the growth behaviour of Listeria monocytogenes in milk in a fast way with a minimal labour requirement.
Keywords: inverse dynamic modelling; milk; growth kinetic; food safety; predictive microbiology
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