Modelling the impact of composition fluctuations and the cold chain on the safe shelf life of Hungarian meat products

Transcription

1 Modelling the impact of composition fluctuations and the cold chain on the safe shelf life of Hungarian meat products Sz. Pércsi, A. Sebők, E. Horváth, Cs. Baár 3rd International Workshop Cold Chain Management June 2-3, 2008 Bonn, Germany Campden & Chorleywood Hungary 1

2 Background Regulation on microbiological criteria of foods 2073/2005/EC- responsibility for safe shelf-life Shelf-life is usually designed for legal temperature limits and proper consumer practices Significant deviations: variation in product composition/brands, batches pressure to increase shelf-life as competitors do; temperature fluctuations in cold chain; consumer practices mishandling, misuse low consumer awareness of potential growth of pathogens at chilling temperatures decision about safety is based on visual observation of mould free/colour free status. 2

3 Objectives Assess impact of variations in composition fluctuations of temperatures in cold chain consumer practices on safe shelf life on vacuum-packed/map typical, popular, cooked meat products in Hungary Develop methods for validation of safe shelf-life for worst-case scenario. 3

4 Methods 1. Analysis of composition of bologna sausages, wieners, hams 2. Measuring temperatures in the cold chain from finished product storage to final consumption 3. Predictive modelling of growth: Listeria monocytogenes Initial value: -2.1 log cfu/g (0.008 cfu/g) Safety limit: 2 log cfu/g (100 cfu/g) 4. Risk profiling scoring system, comparative evaluation 5. Challenge testing 6. Probabilistic modelling Monte Carlo simulation 4

5 Variations in composition Boxplot of salt content vs Product Boxplot of nitrite content vs Product salt % nitrite % Product type Product A B A Bologna sausage B Ham C A B Wiener Product type Product A B Bologna sausage A B Ham C A B Wiener Product type: Boxplot of ph vs Product 7.0 A Between brands 6.5 B Within brands ph 6.0 C Within brands Product type A B Product Bologna sausage A B Ham C A B Wiener 5

6 Time-temperature fluctuations in cold chain 6.6±2.4 C 7.1 C±2.7 C over 5 C:60% over 5 C:73% over 8 C:15% over 8 C:22%

7 Consumer practices Questionnaire survey internet based + traditional hard copies Average storage at home: Top shelf (highest temperature!) is preferred for meat products Average storage time before use: 3 days Average storage time after opening 1.7 days Neglecting use by date - consumption before the end of use by date 50-60% 7

8 Risk profiling A simple screening method, decision tool Steps following the steps of classic risk assessment: hazard identification, hazard characterisation, exposure assessment, risk characterisation Step by step analysis from the raw material to consumption Risk and quality of information is scored Comparative study based on total scores/ scores related to different steps Identifies the gaps in the knowledge 8

9 Risk profiling, risk characterisation Risk profile total score Risk profile comparable score excluding toxin specific scores Information quality profile Listeria monocytogenes Wiener Ham Influence of slicing, packing operation, reheating Influence of composition Listeria monocytogenes represents a higher risk Bologna sausage Clostridium botulinum non-proteolitic Wiener Ham Bologna sausage

10 Challenge test, turkey rolls with cheese Scatterplot of Listeria vs T [days] Manufacturer s shelf-life estimation with traditional method (MAP) log cfu/g T [days] Estim atd shelf-life (days) , C Growth Predictor 8 C vacuum 5 C Growth Predictor 5 C vacuum 0 10

11 1 Challenge test, Bologna sausages - modelling temperature fluctuations in cold chain - Listeria monocytogenes log cfu/g Variable T [ C] MAP vacuum Predictive T [ C] Producer s shelf-life estimation with traditional method (MAP) Ware house 5 Retail cabinet 10 time (day) 15 Home storage Estimated shelf-life (days) ,5 Delivery 11

12 Bologna sausage Monte Carlo simulation for the cold chain 12

13 No Recommended procedure on the determination of safe shelf-life. Short description of the step Determination of growth factors (e.g. ph, salt, aw, nitrite etc.) within and between production. Collection of literature and previous research results about the growth or death factors of the relevant microorganisms. Comparison with validated safe shelf life data of similar product s. (*) Screening with predictive modelling at 8.5 C for average and worst case scenarios. Carry out traditional storage test in triplicates according to the regime shown in the next table. If the shelf-life estimated from this storage test is not longer than the estimated shelf-life of predictive modelling at 8.5 C for the worst case, then the shelf life estimated from the storage test can accepted with small risk. For a shelf-life longer than the outcome of step 4, (over 10 days) challenge tests are necessary. Note(*): It is advisable to summarise the results after step No. 2. If the compositional circumstances or the technological parameters are absolutely unfavourable for the relevant microorganisms (e.g. L. monocytogenes, non-prot. C. botulinum) to grow or the product complies with the food safety criteria of 2073/2005/EC within its shelf-life then step No. 3 should be omitted. 13

14 Recommendations for storage tests for meat products in the Hungarian cold chain STEP STORAGE TEMPERATURE (ºC) TIME (h) Storage in the plant 5 24 * Transport, storage at the distributor (depo) Retail cabinet * 48 * Shopping, transportation to home Home refrigerator Days to expiry Comment ( * ): Time periods have to be determined based on real data collected form storage in the plant, transport and distribution, and from the retail cabinet and/or the demands of retailer partner should be taken into account. 14

15 Conclusions (1) Handling/storage in retail cabinets and household refrigerators have the largest impact False consumer practices are frequent, low awareness of potential growth of pathogens Predictive model shows faster growth than challenge tests MAP showed additional inhibition effect at Bologna sausages, turkey ham The handling, slicing, packing area after cooking should meet high-risk area requirements 15

16 Conclusions (2) A simple screening procedure for safe shelf-life was developed for reducing the need for challenge tests which is based on storage at elevated temperatures and predictive modelling for Listeria monocytogenes This procedure needs further development and validation Acknowledgement: GVOP /3.0 16

17 Determination of physico-chemical data Further considerations Challenge test procedure used so far AFSSA guideline* Considers the changes in the chill chain Strains Inoculums Sampling frequency No. of samples Aim 1 strain normally isolated from the industry cfu/g, T and a w adjusted 9 10 times during and after the shelf life At least duplicate (often triplicate) A tool in the decision support system for determining safe shelf life 3-5 strains isolated from the food 10 3 cfu/g at stationary phase Day 0 and at the end of the shelf life Depends on the homogeneity of the product (1-3) A tool for understanding the Listeria criteria of 2073/2005/EC *AFFSA (2008) Guidance document for conducting shelf life studies to determine compliance with microbiological criteria for Listeria monocytogenes in ready-to-eat 17 foods set out EC regulation No. 2073/2005, version 1. - January 2008

18 Thank you for your attention! 18