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

Yıl 2021, Cilt 7, Sayı 3, 194 - 202, 01.07.2021

Fatih TARLAK

https://doi.org/10.3153/FH21021

Öz

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-R2 = 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 > Bf > 1.014 and 1.080 > Af > 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. 

Anahtar Kelimeler

Dynamic condition, Microbiological quality, Pseudomonas spp, Growth kinetic, Spoilage, Predictive microbiology

Kaynakça

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Koutsoumanis, K. (2001). Predictive modeling of the shelf life of fish under nonisothermal conditions. Applied and Environmental Microbiology, 67, 1821-1829. https://doi.org/10.1128/AEM.67.4.1821-1829.2001
  • Le Marc, M., Plowman, J., Aldus, C. F., Munoz-Cuevas, M., Baranyi, J., Peck, M.W. (2008). Modelling the growth of Clostridium perfringens during the cooling of bulk meat. International Journal of Food Microbiology, 128, 41-50. https://doi.org/10.1016/j.ijfoodmicro.2008.07.015
  • Lytou, A., Panagou, E.Z., Nychas, G.J.E. (2016). Development of a predictive model for the growth kinetics of aerobic microbial population on pomegranate marinated chicken breast fillets under isothermal and dynamic temperature conditions. Food Microbioogy, 55, 25-31. https://doi.org/10.1016/j.fm.2015.11.009
  • Martino, K.G., Marks, B.P. (2007). Comparing uncertainty resulting from two-step and global regression procedures applied to microbial growth models. Journal of Food Protection,70, 2811-2818. https://doi.org/10.4315/0362-028X-70.12.2811
  • Milkievicz, T., Badia, V., Souza, V. B., Longhi, D. A., Galvão, A. C., da Silva Robazza, W. (2020). Development of a general model to describe Salmonella spp. growth in chicken meat subjected to different temperature profiles. Food Control, 112, 107151. https://doi.org/10.1016/j.foodcont.2020.107151
  • Pérez-Rodríguez, F., Valero, A. (2013). Predictive Microbiology in Foods. Springer, New York. ISBN: 978-1-4614-5520-2 https://doi.org/10.1007/978-1-4614-5520-2
  • Ratkowsky, D.A., Olley, J., McMeekin, T.A., Ball, A. (1982). Relationship between temperature and growth rate of bacterial cultures. Journal of Bacterioogy, 149, 1-5. https://doi.org/10.1128/JB.149.1.1-5.1982
  • Robinson, T.P., Ocio, M.J., Kaloti, A., Mackey, B.M. (1998). The effect of the growth environment on the lag phase of Listeria monocytogenes. International Journal of Food Microbiology, 44, 83-92. https://doi.org/10.1016/S0168-1605(98)00120-2
  • Ross, T. (1996). Indices for performance evaluation of predictive models in food microbiology. Journal of Applied Bacterioogy, 81, 501-508. https://doi.org/10.1111/j.1365-2672.1996.tb03539.x
  • Tarlak, F. (2020). Development and validation of one-step modelling approach for prediction of mushroom spoilage. Journal of Food and Nutrition Research, 59(4), 281-289.
  • Whiting, R.C. (1995). Microbial modeling in foods. Critical Reviews in Food Science and Nutrition, 35, 467-494. https://doi.org/10.1080/10408399509527711
  • Zwietering, M. H., De Wit, J. C., Cuppers, H. G. A. M., van't Riet, K. (1994). Modeling of bacterial growth with shifts in temperature. Applied and Environmental Microbiology, 60, 204-213. https://doi.org/10.1128/AEM.60.1.204-213.1994
  • Zwietering, M.H, Jongenburger, I., Rombouts, F.M, van’t iet, K. (1990). Modeling of the bacterial growth curve. Applied and Environmental Microbiology, (56), 1875-1881. https://doi.org/10.1128/AEM.56.6.1875-1881.1990

Yıl 2021, Cilt 7, Sayı 3, 194 - 202, 01.07.2021

Fatih TARLAK

https://doi.org/10.3153/FH21021

Öz

Kaynakça

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Koutsoumanis, K. (2001). Predictive modeling of the shelf life of fish under nonisothermal conditions. Applied and Environmental Microbiology, 67, 1821-1829. https://doi.org/10.1128/AEM.67.4.1821-1829.2001
  • Le Marc, M., Plowman, J., Aldus, C. F., Munoz-Cuevas, M., Baranyi, J., Peck, M.W. (2008). Modelling the growth of Clostridium perfringens during the cooling of bulk meat. International Journal of Food Microbiology, 128, 41-50. https://doi.org/10.1016/j.ijfoodmicro.2008.07.015
  • Lytou, A., Panagou, E.Z., Nychas, G.J.E. (2016). Development of a predictive model for the growth kinetics of aerobic microbial population on pomegranate marinated chicken breast fillets under isothermal and dynamic temperature conditions. Food Microbioogy, 55, 25-31. https://doi.org/10.1016/j.fm.2015.11.009
  • Martino, K.G., Marks, B.P. (2007). Comparing uncertainty resulting from two-step and global regression procedures applied to microbial growth models. Journal of Food Protection,70, 2811-2818. https://doi.org/10.4315/0362-028X-70.12.2811
  • Milkievicz, T., Badia, V., Souza, V. B., Longhi, D. A., Galvão, A. C., da Silva Robazza, W. (2020). Development of a general model to describe Salmonella spp. growth in chicken meat subjected to different temperature profiles. Food Control, 112, 107151. https://doi.org/10.1016/j.foodcont.2020.107151
  • Pérez-Rodríguez, F., Valero, A. (2013). Predictive Microbiology in Foods. Springer, New York. ISBN: 978-1-4614-5520-2 https://doi.org/10.1007/978-1-4614-5520-2
  • Ratkowsky, D.A., Olley, J., McMeekin, T.A., Ball, A. (1982). Relationship between temperature and growth rate of bacterial cultures. Journal of Bacterioogy, 149, 1-5. https://doi.org/10.1128/JB.149.1.1-5.1982
  • Robinson, T.P., Ocio, M.J., Kaloti, A., Mackey, B.M. (1998). The effect of the growth environment on the lag phase of Listeria monocytogenes. International Journal of Food Microbiology, 44, 83-92. https://doi.org/10.1016/S0168-1605(98)00120-2
  • Ross, T. (1996). Indices for performance evaluation of predictive models in food microbiology. Journal of Applied Bacterioogy, 81, 501-508. https://doi.org/10.1111/j.1365-2672.1996.tb03539.x
  • Tarlak, F. (2020). Development and validation of one-step modelling approach for prediction of mushroom spoilage. Journal of Food and Nutrition Research, 59(4), 281-289.
  • Whiting, R.C. (1995). Microbial modeling in foods. Critical Reviews in Food Science and Nutrition, 35, 467-494. https://doi.org/10.1080/10408399509527711
  • Zwietering, M. H., De Wit, J. C., Cuppers, H. G. A. M., van't Riet, K. (1994). Modeling of bacterial growth with shifts in temperature. Applied and Environmental Microbiology, 60, 204-213. https://doi.org/10.1128/AEM.60.1.204-213.1994
  • Zwietering, M.H, Jongenburger, I., Rombouts, F.M, van’t iet, K. (1990). Modeling of the bacterial growth curve. Applied and Environmental Microbiology, (56), 1875-1881. https://doi.org/10.1128/AEM.56.6.1875-1881.1990

Ayrıntılar

Birincil Dil İngilizce
Konular Gıda Bilimi ve Teknolojisi, Mühendislik, Ziraat
Bölüm Research Articles
Yazarlar

Fatih TARLAK (Sorumlu Yazar)
İSTANBUL GEDİK ÜNİVERSİTESİ
0000-0001-5351-1865
Türkiye

Destekleyen Kurum This work was financially supported by Istanbul Gedik University through the centre supporting Scientific Research Projects.
Teşekkür Dr. Fatih Tarlak would like to thank Asst. Prof. Dr. Emel Birol for her help during this research.
Yayımlanma Tarihi 1 Temmuz 2021
Başvuru Tarihi 7 Ocak 2021
Kabul Tarihi 12 Şubat 2021
Yayınlandığı Sayı Yıl 2021, Cilt 7, Sayı 3

Kaynak Göster

Bibtex @araştırma makalesi { jfhs856064, journal = {Food and Health}, issn = {}, eissn = {2602-2834}, address = {Vidin Caddesi No:28 D:4 Kocamustafapaşa 34107 Fatih İstanbul}, publisher = {Özkan ÖZDEN}, year = {2021}, volume = {7}, pages = {194 - 202}, doi = {10.3153/FH21021}, title = {Prediction of growth kinetics of Pseudomonas spp. in meat products under isothermal and non-isothermal storage conditions}, key = {cite}, author = {Tarlak, Fatih} }
APA Tarlak, F. (2021). Prediction of growth kinetics of Pseudomonas spp. in meat products under isothermal and non-isothermal storage conditions . Food and Health , 7 (3) , 194-202 . DOI: 10.3153/FH21021
MLA Tarlak, F. "Prediction of growth kinetics of Pseudomonas spp. in meat products under isothermal and non-isothermal storage conditions" . Food and Health 7 (2021 ): 194-202 <http://jfhs.scientificwebjournals.com/tr/pub/issue/61021/856064>
Chicago Tarlak, F. "Prediction of growth kinetics of Pseudomonas spp. in meat products under isothermal and non-isothermal storage conditions". Food and Health 7 (2021 ): 194-202
RIS TY - JOUR T1 - Prediction of growth kinetics of Pseudomonas spp. in meat products under isothermal and non-isothermal storage conditions AU - Fatih Tarlak Y1 - 2021 PY - 2021 N1 - doi: 10.3153/FH21021 DO - 10.3153/FH21021 T2 - Food and Health JF - Journal JO - JOR SP - 194 EP - 202 VL - 7 IS - 3 SN - -2602-2834 M3 - doi: 10.3153/FH21021 UR - https://doi.org/10.3153/FH21021 Y2 - 2021 ER -
EndNote %0 Food and Health Prediction of growth kinetics of Pseudomonas spp. in meat products under isothermal and non-isothermal storage conditions %A Fatih Tarlak %T Prediction of growth kinetics of Pseudomonas spp. in meat products under isothermal and non-isothermal storage conditions %D 2021 %J Food and Health %P -2602-2834 %V 7 %N 3 %R doi: 10.3153/FH21021 %U 10.3153/FH21021
ISNAD Tarlak, Fatih . "Prediction of growth kinetics of Pseudomonas spp. in meat products under isothermal and non-isothermal storage conditions". Food and Health 7 / 3 (Temmuz 2021): 194-202 . https://doi.org/10.3153/FH21021
AMA Tarlak F. Prediction of growth kinetics of Pseudomonas spp. in meat products under isothermal and non-isothermal storage conditions. Food Health. 2021; 7(3): 194-202.
Vancouver Tarlak F. Prediction of growth kinetics of Pseudomonas spp. in meat products under isothermal and non-isothermal storage conditions. Food and Health. 2021; 7(3): 194-202.
IEEE F. Tarlak , "Prediction of growth kinetics of Pseudomonas spp. in meat products under isothermal and non-isothermal storage conditions", Food and Health, c. 7, sayı. 3, ss. 194-202, Tem. 2021, doi:10.3153/FH21021

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