Tavuk Eti Bozulmasının Tespiti İçin Birincil Modellerin Tahmin Kabiliyetinin Karşılaştırılması

Year 2021, , 1 - 9, 26.04.2021

Fatih Tarlak

https://doi.org/10.24323/akademik-gida.927400

Abstract

Bu çalışmanın temel amacı, depolama sıcaklığının aerobik olarak depolanan çiğ ve marine edilmiş tavuk eti bozulmasına etkisini tek adımlı modelleme yaklaşımı kullanarak simüle etmek için modifiye Gompertz, modifiye lojistik ve Baranyi modelleri olarak bilinen farklı birincil modellerin tahmin kabiliyetini karşılaştırmaktır. Bu amaç doğrultusunda, toplam canlı popülasyonu (TVC) çoğalma verileri, aerobik olarak depolanmış çiğ ve marine edilmiş tavuk eti için yayımlanmış çalışmadan elde edilmiştir. Global modellerin uydurma kabiliyeti, kök ortalama kare hatası (RMSE) ve düzeltilmiş belirleme katsayısı (düzeltilmiş-R2) dikkate alınarak karşılaştırıldı. İstatistiksel indeksler, RMSE ve düzeltilmiş-R2 değerleri, birincil modellerin her biri ve her iki tavuk ürünü için sırasıyla maksimum 0.299 ve minimum 0.970 olarak bulundu. Global modellerin tahmin performansı, aerobik olarak depolanan çiğ tavuk eti için farklı çalışmadan elde edilen rmax değerleri ile değerlendirildi ve 5.11 × 10-2'den küçük RMSE değerleri, aerobik olarak depolanan çiğ tavuk etindeki toplam canlı popülasyonunu tahmin etmek için tek adımlı modelleme yaklaşımının güvenilir bir şekilde kullanılabileceğini ortaya koydu.

Keywords

Global model, Mikrobiyolojik kalite, Çoğalma kinetiği, Global model, Mikrobiyolojik kalite, Çoğalma kinetiği,

Comparison of Prediction Capability of Primary Models for Detection of Chicken Meat Spoilage

Abstract

The main objective of the present work is to compare the prediction capability of different primary models known as the modified Gompertz, modified logistic and Baranyi models to simulate the effect of temperature on aerobically-stored raw and marinated chicken meat spoilage using one-step modelling approach. For this purpose, total viable count (TVC) growth data were extracted from the published work for aerobically-stored raw and marinated chicken meat. The fitting capability of the global models was compared by taking into account root mean square error (RMSE) and adjusted coefficient of determination (adjusted-R2). Statistical indices, RMSE and adjusted-R2 values were found to be maximum 0.299 and minimum 0.970, respectively for each of the primary models and both of the chicken products. The prediction performance of the global models were evaluated with the rmax values that were independently published for aerobically-stored raw chicken meat, and RMSE values with lower than 5.11 × 10-2 revealed that one-step modelling approach can be reliably employed to predict TVC in aerobically-stored raw chicken meat.

Keywords

Global model, Microbiological quality, Growth kinetic, Chicken meat spoilage, Predictive microbiology

References

• [1] Belitz, H.D., Grosch, W., Schieberle. P. (2009). Springer food chemistry 4th revised and extended edition. Annual Review of Biochemistry, 81, 79–655.
• [2] Grau R., Sánchez A.J., Girón J., Iborra E., Fuentes., A., Barat J.M. (2011). Nondestructive assessment of freshness in packaged sliced chicken breasts using SW-NIR spectroscopy. Food Research International, 44, 331–337.
• [3] 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 of Food Safety, 37, 12331.
• [4] Falkovskaya A., Gowen A. (2020). Literature review: spectral imaging applied to poultry products. Poultry Science, 99, 3709–3722.
• [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.
• [6] 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 marinat ed chicken breast fillets under isothermal and dynamic temperature conditions. Food Microbiology, 55, 25–31.
• [7] Valero A., Pérez-Rodríguez F. (2013). Predictive Microbiology in Foods. Springer, New York.
• [8] Whiting R.C. (1995). Microbial modeling in foods. Critical Reviews in Food Science and Nutrition, 35, 467–494.
• [9] Ratkowsky D.A., Olley J., McMeekin T.A., Ball A. (1982). Relationship between temperature and growth rate of bacterial cultures. Journal of Bacteriology, 149, 1–5.
• [10] 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.
• [11] Huang L. (2017). IPMP Global Fit–A one-step direct data analysis tool for predictive microbiology. International Journal of Food Microbiology, 262, 38–48.
• [12] Swinnen I.A.M., Bernaerts K., Dens E.J., Geeraerd A.H., Van Impe J.F. (2004). Predictive modelling of the microbial lag phase: a review. International Journal of Food Microbiology, 94, 137–159.
• [13] 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.
• [14] Jewell K. (2012). Comparison of 1-step and 2-step methods of fitting microbiological models. International Journal of Food Microbiology, 160, 145–161.
• [15] Hereu A., Dalgaard P., Garriga M., Aymerich T., Bover-Cid S. (2014). Analysing and modelling the growth behaviour of Listeria monocytogenes on RTE cooked meat products after a high pressure treatment at 400 MPa. International Journal of Food Microbiology, 186, 84–94.
• [16] Manthou E., Tarlak F., Lianou A., Ozdemir M., Zervakis G.I., Panagou E.Z., Nychas G.J.E. (2019). Prediction of indigenous pseudomonas spp. growth on oyster mushrooms (Pleurotus ostreatus) as a function of storage temperature. LWT- Food Science and Technology, 111, 506–512.
• [17] Huang L. (2015). Direct construction of predictive models for describing growth of Salmonella Enteritidis in liquid eggs–A one-step approach. Food Control, 57, 76–81.
• [18] Huang L. (2016). Mathematical modeling and validation of growth of Salmonella Enteritidis and background microorganisms in potato salad–One-step kinetic analysis and model development. Food Control, 68, 69–76.
• [19] Zwietering M.H., Jongenburger I., Rombouts F.M., van’t Riet K. (1990). Modeling of the bacterial growth curve. Applied and Environmental Microbiology, 56, 1875–1881.
• [20] Baranyi J., Roberts T.A. (1994). A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology, 23, 277–294.
• [21] Juneja V.K., Melendres M.V., Huang L., Subbiah J., Thippareddi H. (2009). Mathematical modeling of growth of Salmonella in raw ground beef under isothermal conditions from 10 to 45 C. International Journal of Food Microbiology, 131, 106–111.
• [22] Lianou A., Moschonas G., Nychas G.J.E., Panagou E.Z. (2018). Growth of Listeria monocytogenes in pasteurized vanilla cream pudding as affected by storage temperature and the presence of cinnamon extract. Food Research International, 106, 1114–11.