Dergiler / Food and Health / 2021 / Cilt: 7 - Sayı: 3

Prediction of growth kinetics of Pseudomonas spp. in meat products under isothermal and non-isothermal storage conditions

Sayfa
194–202
Yayın Tarihi
2022-10-19
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The differential form of the Huang model provided satisfactorily statistical indexes ($1.075 > B_f > 1.014 and 1.080 > A_f > 1.047$) indicating reliably being able to use to describe the growth behaviour of Pseudomonas spp. in fish, pork and chicken meat subjected to non-isothermal storage conditions.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:The main objective of the present study was to develop and validate a new alternative modelling method to predict the shelf-life of food products under non-isothermal storage conditions.
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Temel Bulgular & Veri:Temperature dependent kinetic parameters (maximum specific growth rate ‘µmax’ and lag phase duration ‘λ’) were described as a function of storage temperature via the Ratkowsky model integrated with each primary model.
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Sonuç & Katkı:The differential form of the Huang model provided satisfactorily statistical indexes ($1.075 > B_f > 1.014 and 1.080 > A_f > 1.047$) indicating reliably being able to use to describe the growth behaviour of Pseudomonas spp. in fish, pork and chicken meat subjected to non-isothermal storage conditions.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

The main objective of the present study was to develop and validate a new alternative modelling method to predict the shelf-life of food products under non-isothermal storage conditions. The bacterial growth data of the Pseudomonas spp. was extracted from published studies conducted for aerobically-stored fish, pork and chicken meat and described with two-step and one-step modelling approaches employing different primary models (the modified Gompertz, logistic, Baranyi and Huang models) under isothermal storage temperatures. Temperature dependent kinetic parameters (maximum specific growth rate ‘µmax’ and lag phase duration ‘λ’) were described as a function of storage temperature via the Ratkowsky model integrated with each primary model. The Huang model based on the one-step modelling approach yielded the best goodness of fit results ($RMSE = 0.451 and adjusted-R^2 = 0.942$) for all food products at isothermal storage conditions, therefore, was also used to check it’s the prediction capability under non-isothermal storage conditions. The differential form of the Huang model provided satisfactorily statistical indexes ($1.075 > B_f > 1.014 and 1.080 > A_f > 1.047$) indicating reliably being able to use to describe the growth behaviour of Pseudomonas spp. in fish, pork and chicken meat subjected to non-isothermal storage conditions.

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Bu Makaleye Atıf Yap

TARLAK, F. (2022). Prediction of growth kinetics of Pseudomonas spp. in meat products under isothermal and non-isothermal storage conditions. Food and Health, Cilt: 7 - Sayı: 3, 194–202. https://ajindex.com/articles/517878

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Kaynakça (10)

  1. Baranyi, J., Roberts, T.A. (1994). A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology, 23, 277-294. https://doi.org/10.1016/0168-1605(94)90157-0
  2. Bovill, R.A., Bew, J., Baranyi, J. (2001). Measurements and predictions of growth for Listeria monocytogenes and Salmonella during fluctuating temperature: II. Rapidly changing temperatures. International Journal of Food Microbiology, 67, 131-137. https://doi.org/10.1016/S0168-1605(01)00446-9
  3. Bruckner, S. (2010). Predictive shelf life model for the improvement of quality management in meat chains. PhD thesis. Bruckner, S., Albrecht, A., Petersen, B., Kreyenschmidt, J. (2013). A predictive shelf life model as a tool for the improvement of quality management in pork and poultry chains. Food control, 29, 451-460. https://doi.org/10.1016/j.foodcont.2012.05.048
  4. Buchanan, R.L., Whiting, R.C., Damert, W.C. (1997). When is simple good enough: a comparison of the Gompertz, Baranyi, and three-phase linear models for fitting bacterial growth curves. Food Microbioogy, 14, 313-326. https://doi.org/10.1006/fmic.1997.0125
  5. Dominguez, S.A., Schaffner, D.W. (2007). Development and validation of a mathematical model to describe the growth of Pseudomonas spp. in raw poultry stored under aerobic conditions. International Journal of Food Microbiology, 120, 287-295. https://doi.org/10.1016/j.ijfoodmicro.2007.09.005
  6. Ghollasi-Mood, F., Mohsenzadeh, M., Hoseindokht, M.R., Varidi, M. (2017). Quality changes of air-packaged chicken meat stored under different temperature conditions and mathematical modelling for predicting the microbial growth and shelf life. Journal Food Safety, 37, e12331. https://doi.org/10.1111/jfs.12331
  7. Huang, L. (2008). Growth kinetics of Listeria monocytogenes in broth and beef frankfurters Determination of lag phase duration and exponential growth rate under isothermal conditions. Journal of Food Science, 73, e235-242. https://doi.org/10.1111/j.1750-3841.2008.00785.x
  8. Huang, L. (2017). IPMP Global Fit–A one-step direct data analysis tool for predictive microbiology. International Journal of Food Microbiology, 262, 38-48. https://doi.org/10.1016/j.ijfoodmicro.2017.09.010
  9. Jewell, K. (2012). Comparison of 1-step and 2-step methods of fitting microbiological models. International Journal of Food Microbiology, 160, 145-161. https://doi.org/10.1016/j.ijfoodmicro.2012.09.017
  10. Koutsoumanis, K. (2001). Predictive modeling of the shelf life of fish under nonisothermal conditions