Sym Previus : Predictive microbiology tools for cold chain management. Dominique THUAULT

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1 Sym Previus : Predictive microbiology tools for cold chain management Dominique THUAULT dominique.thuault@adria.tm.fr Olivier COUVERT Bonn - June 2008 olivier.couvert@adria.tm.fr

2 According to the Regulation EC n 2073/2005 for microbiological criteria in food The food business operators shall take measures to ensure that the food safety criteria applicable throughout the shelf life of the products can be met under reasonably foreseeable conditions of distribution, storage and use. Bonn June

3 According to the Annex II of the Regulation EC n 2073/2005 The food business operators shall conduct additional studies which may include: predictive mathematical modelling established for the food in question tests to investigate the ability of the appropriately inoculated micro-organism of concern to grow or survive in the product during the shelf-life Bonn June

4 INTRODUCTION Food business operators need microbiological data in order to: Formulate and launch on the market new stable products Ensure food shelf-life Provide reliable information for microbiological hazard, CCP identification and record the critical limits Bonn June

5 Objectives Our objectives are to describe and to predict the microbiological growth with a simple and convenient models for microbiologists. Bonn June

6 The specifications The specifications included: Models and software easy to use: Software primarily designed for food industries Adapted to the food products and processes Model Parameters with biological meaning Bonn June

7 The specifications The models have to take into account: The biological variability ( strains...) The interaction between the food (structure, composition, ) and the micro-organism, The industrial context: the level of initial contamination of micro-organisms the impact of the process (the lag time) the batch and process variability We can underline that challenge-test est can never fullfill such specifications and that only a modelling approach can answer the requirement of the European Regulation Bonn June

8 Probabilistic software Microbiological variability Initial contamination Foodstuff and batch variability Reference data in food Many strains studied for each species Industrial data Industrial measure Challenge-test data Probabilistic Simulation Growth simulations Probability to reach the critical limit Bonn June

9 Models and concept Modular models taking into account food matrix effect and environmental factors ex : Growth µ max = µ opt γ(t) γ(aw) γ(ph) γ(ah) γ(interaction) Environmental factors Food specific µopt 1 µopt 2 2 food types with identical T, ph, a w, inhibitor characteristics, might not induce similar microbial growth. Bonn June

10 Models and concept Modular models taking into account food matrix effect and environmental factors ex : Growth µ max = µ opt γ(t) γ(aw) γ(ph) γ(ah) γ(interaction) Cardinal models are applied for each factor All γ functions are based on biological parameters called cardinal values (absolute biological limits for growth). T o ph aw acid T min T opt T max ph min ph opt ph max aw min aw opt aw max alpha MIC Bonn June

11 Tools package Database & Query system - bibliographic data - Industrial data - Research projects data Growth / no growth boundary simulation Growth software - Fitting - Simulation Bonn June

12 Tools package Database & Query system - bibliographic data - Industrial data - Research projects data Growth / no growth boundary simulation Growth software - Fitting - Simulation Bonn June

13 Tools package Database & Query system - bibliographic data - Industrial data - Research projects data Growth / no growth boundary simulation Growth software - Fitting - Simulation Bonn June

14 Tools package Database & Query system - bibliographic data - Industrial data - Research projects data Growth / no growth boundary simulation Growth software - Fitting - Simulation Bonn June

15 Tools package Database & Query system - bibliographic data - Industrial data - Research projects data Growth / no growth boundary simulation Growth software - Fitting - Simulation Bonn June

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17 Which data are needed for growth simulation?. Growth parameters of bacterial species. Food characteristics to estimate the µ opt. Initial contamination. Simulation conditions (new environmental conditions) Bonn June

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25 µ opt Cardinal values Bonn June

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40 Cardinal values Bonn June

41 Distribution for product of 120g(+/-10) Bonn June

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45 minced meat Listeria monocytogenes industrial data : 7 positifs /1000 in 25g packaging : 100g +/- 5g µopt = 2 h -1 lag = 0 4 C Bonn June

46 minced meat Listeria monocytogenes industrial data : 7 positifs /1000 in 25g packaging : 100g +/- 5g µopt = 2 h -1 lag = 0 8 C constant Bonn June

47 minced meat Listeria monocytogenes industrial data : 7 positifs /1000 in 25g packaging : 100g +/- 5g µopt = 2 h -1 lag = 0 1/3 à 4 C + 2/3 à 8 C Bonn June

48 minced meat Listeria monocytogenes industrial data : 7 positifs /1000 in 25g packaging : 100g +/- 5g µopt = 2 h -1 lag = 0 2/3 à 2 C + 1/3 à 8 C Bonn June

49 Temperature profil and Listeria evolution : comparison in minced meat Shelf-life: 7 days Portion: 100 g ± 5 g µopt: 2 h-1 Lag= 0 Temperature: 4 C 7 C 1/3 time 4 C, 2/3 8 C 2/3 time 2 C, 2/3 8 C 2/3 time 2 C, 3 h 25 C, 1/3 8 C Bonn June

50 Over the critical point: 100 CFU/g Temperature profile Percentage over the critical point 4 C 0,4 % 8 C 100 % 1/3 time at 4 C, 2/3 time at 8 C 2/3 time at 2 C, 1/3 time at 8 C 2/3 time at 2 C, 3 h at 25 C, 1/3 time at 8 C 95 % 3,1 % 26 % Bonn June

51 Conclusions Growth curves and contamination density are calculated according to : - Challenge-test (= reference curve). To take food matrix into account - physico-chemical analysis : Factors variability. To take food and process variability into account - Microbiological analysis : bacterial counts. To evaluate initial contamination of product Sym Previus allows to estimate the shelf life taking into account industrial microbiological counts and physico-chemical variability measured in industries. Bonn June

52 How to use Sym Previus? By contracting a subscription By contacting the network of experts centres which can provide you with advises and simulations based on your own data set For any information, contact: olivier.couvert@adria.tm.fr Bonn June

53 Sym Previus : prediction of process and environment impacts on microorganisms in food Dominique THUAULT Olivier COUVERT Bonn - June 2008