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A predictive shelf life model as a tool for the improvement of quality management in pork and poultry chains
ICPMF 7 – Dublin 2011
Stefanie Bruckner, Antonia Albrecht, Verena Raab, Rolf Ibald, Brigitte Petersen and Judith Kreyenschmidt
Cold Chain Management Group Department Preventive Health Management
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Background
Prediction of remaining shelf important to prevent food waste and economic losses mathematical models (predictive microbiology)
Majority of predictive models based on data obtained in liquid broth
Only a few models existing which are applicable for different types of fresh meat and at dynamic temperature conditions
Aim of the study development of a common predictive shelf life model for fresh pork and
fresh poultry based on the growth of Pseudomonas spp.
Background and aim
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Study design
Materials and methods
intrinsic factors pH-value, aw-value, texture, glucose, lactate, fat, protein
Storage tests for the characterisation of microbiological spoilage of fresh pork and poultry
constant temperature conditions 2, 4, 7, 10, 15 C
dynamic temperature conditions 9 scenarios
Development and validation of the model
Development of the software
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Storage tests
Product:
Sliced pork loins (ca. 200 g), poultry fillets (ca. 150 g)
Storage atmosphere
Aerobic
Investigated parameters:
Total Viable Count (pour plate technique, plate count agar, 72 h at 30°C)
Pseudomonas spp. count (spread plate technique, Pseudomonas Agar Base +
CFC supplement, 48 h at 25°C)
Sensory characteristics (colour, odour, texture; 3-point scoring system,
weighted sensory index)
Materials and methods
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Modelling
Primary model:
Materials and methods
)(
)(MtBeeCAtN
−⋅−−⋅+=Gompertz-Model:
Secondary model:
( ) ( )TR
EFB a 1lnln ⋅−=Arrhenius Model
N(t): microbial count [cfu/g] at time t B: relative growth rate at time M [1/h] A: initial bacterial count [cfu/g] M: reversal point [h] C: difference between Nmax (= maximum population level) and A [cfu/g] t: time [h] (Gibson et al., 1987)
B: relative growth rate at time M [1/h] F: pre-exponential factor [1/h] R: gas constant = 8.314 J/mol K T: absolute temperature [K] Ea: activation energy for bacterial growth [kJ/mol]
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Development of the common predictive model
Combination of primary and secondary model
Division of the time-temperature history of the product into several assumed time-temperature intervals with constant storage temperatures
Growth can be predicted with the Gompertz model in each interval – Nmax: means of observed maximum bacterial counts
– A: observed initial bacterial counts
– B: obtained from the Arrhenius plot
– M: derived from linear regression of M against temperature for the first interval (calculated with Gompertz for the other intervals)
Material and methods
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Growth of Pseudomonas spp. at constant temperatures
(primary modeling) Results
Good description of Pseudomonas spp. growth with the Gompertz function (R² ≥ 0.94 for both meat types)
High significant correlations between Pseudomonas spp. counts and sensory attributes (r > -0.90; p < 0.05)
Determination of a common spoilage level of 7.5 log10 CFU/g
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Growth of Pseudomonas spp. at constant temperatures
(primary modeling)
B and µmax increasing with increasing storage temperature Longer shelf life for pork than for poultry at all investigated constant storage
temperature Comparable decrease of shelf life with increasing temperatures
Similar microbiological spoilage processes for fresh pork and fresh poultry at constant storage temperatures
Temp. [ C]
B [1/h]
µmax
[1/h] Shelf life
[h]
pork poultry pork poultry pork poultry
2 0.012 0.014 0.032 0.034 165.8 126.4
4 0.018 0.020 0.043 0.041 122.2 98.6
7 0.025 0.033 0.054 0.081 92.9 63.9
10 0.033 0.058 0.086 0.121 75.4 41.5
15 0.051 0.103 0.130 0.212 45.5 27.1
Results
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Arrheniusplot (secondary modeling)
Good description of the temperature dependency of the growth rates with the Arrhenius equation (R² = 0.98 for pork, R² = 0.99 for poultry)
Results
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Model development
Good linearity of the fits of M values against temperature (R² = 0.97 for pork, R² = 0.94 for poultry) enabled the calculation of an adequate M value for the first interval in dynamic storage scenarios
Results
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Model development
B and M values for poultry could be related to pork values by linear fitting
Fits were good with R² values of 0.98 (for B) and 0.998 (for M)
Results
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Model validation
--- temperature profile observed counts ― prediction --- +/- 10 %
Periodically changing temperature (4 h at 12 C, 8 h at 8 C, 12 h at 4 C)
Results
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3 short-term temperature shifts for 4 h to 15 C
Model validation
--- temperature profile observed counts ― prediction --- +/- 10 %
predictions for both meat types matched to the observed Pseudomonas spp. counts as well as observed shelf lives
Results
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Integration of the model in an Internet-based software solution
(tertiary modeling)
Allows user-friendly simulations of shelf lives for specific products depending on dynamic and adjustable time-temperature-rows
Incorporation of a TTI kinetic model (based on OnVUTM TTIs) for the optimization of cold chain management
programmed with the widely known scripting language php and a mysql-
database compatible with most servers of commercial internet providers, easy to update and administrate
Freely accessible at: http://www.ccm-network.com
Results
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Software – Simulate Shelf Life
Results
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Simulate Shelf Life - Result
Results
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Quality improvement by TTIs
Results
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Summary
Similar spoilage processes for fresh pork and fresh poultry → enabled the development of a common predictive shelf life model
Predictions for both meat types matched to the observations
Incorporation of the common shelf life model as well as the TTI
kinetic model in a freely accessible software → calculation of remaining shelf life of the product at specific
control points along the chill chain
• BUT: still several existing challenges before a practical application
Summary
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Thank you for your attention!!
Cold Chain Management Group University of Bonn www.ccm.uni-bonn.de
www.ccm-network.com
The study was partly financed by the EU project Chill-On (FP6-016333-2) and the InterregIIIC project PromSTAP. Thanks to all companies involved as well as to students and technical assistants for supporting the study.
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References
Bruckner, S. (2010). Predictive shelf life model: A new approach for the improvement of quality management in meat chains. PhD thesis, Rheinische Friedrich-Wilhelms-Universität Bonn.
Kreyenschmidt, J., Christiansen, H., Huebner, A., Raab, V. & Petersen, B. (2010): A novel photochromic time–temperature indicator to support cold chain management. International Journal of Food Science & Technology, 45, 2, pp. 208 – 215.
Kreyenschmidt, J.; Hübner, A.; Beierle, E.; Chonsch, L.; Scherer, A.;B. Petersen (2010). Determination of the shelf life of sliced cooked ham based on the growth of lactic acid bacteria in different steps of the chain. Journal of Applied Microbiology, 108, 510-520
Raab, V., Petersen, B. & Kreyenschmidt, J. (2011). Temperature monitoring in meat supply chains. British Food Journal 113 (10) (in press).
Raab, V. (2011). Assessment of temperature monitoring systems for improving cold chain management in meat supply chains. PhD thesis, Rheinische Friedrich-Wilhelms-Universität Bonn (in press).
Raab, V., Ibald, R.; Reichstein, W., Haarer, D. & Kreyenschmidt, J. (2011). Novel solutions supporting inter-organizational quality and information management. In Popov, P. & Brebbia, C.A. (eds.), Proceedings of the Food and Environment 2011, 21-23 June 2011, WIT Transactions on Ecology and The Environment, Vol. 152, WIT Press, New Forest, UK, pp. 177-188 (in press).