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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).