Predicting the Shelf Life of Dairy Products through Mathematical Modelling and in silico Experimentation

Authors

DOI:

https://doi.org/10.17488/RMIB.45.2.6

Keywords:

experimental data, nonlinear regression, nonlinear time-varying systems, numerical simulations, shelf life

Abstract

The preservation of foods such as milk, meat, and vegetables through fermentation results in products like yogurt, cheese, pickles, sausages, and silage with an extended shelf life compared to their natural unprocessed counterparts. This work aims to formulate a mathematical model of first-order ordinary differential equations (ODEs) that accounts for both the physicochemical and microbiological parameters affecting biomass kinetics [B(t)], acidity [A(t)], and viscosity [V(t)] as a function of temperature across different yogurt samples. In order to validate the efficacy of the model in predicting yogurt shelf life, we compared its fitting results with commonly employed systems or equations, including the Weibull model, the Reaction Order model, the Arrhenius Equation, and the Q10 Factor. Our evaluation, based on R-squared (R2) values exceeding 0.95, demonstrates the robustness of the proposed model. Furthermore, all parameters were estimated along with their corresponding 95 % confidence intervals. The mathematical model estimates the dynamic of each of the physicochemical and microbiological parameters which will help to predict the behavior over time of the shelf life of yogurt at different temperatures.

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References

N.-N. Zhi, K. Zong, K. Thakur, J. Qu, et al., “Development of a dynamic prediction model for shelf life evaluation of yogurt by using physicochemical, microbiological and sensory parameters,” CyTA - J. Food, vol. 16, no. 1, pp. 42–49, Jan. 2018, doi: https://doi.org/10.1080/19476337.2017.1336572

S. Sarkar, “Shelf‐life extension of cultured milk products,” Nutr. Food Sci., vol. 36, no. 1, pp. 24–31, Jan. 2006, doi: https://doi.org/10.1108/00346650610642160

C. Paulo Vieira, M. Pereira da Costa, B. da Silva Frasão, V. L. de Melo Silva, R. Vilela de Barros Pinto Moreira, Y. E. Chifarelli de Oliveira Nunes, C. A. Conte-Junior, “Nondestructive prediction of the overall quality of cow milk yogurt by correlating a biogenic amine index with traditional quality parameters using validated nonlinear models,” J. Food Compos. Anal., vol. 84, art. no. 103328, Dec. 2019, doi: https://doi.org/10.1016/j.jfca.2019.103328

F. Al-Rimawi, M. Alayoubi, c. Elama, M. Jazzar, and A. Çakıcı, “Use of cinnamon, wheat germ, and eucalyptus oils to improve quality and shelf life of concentrated yogurt (Labneh),” Cogent Food Agric., vol. 6, no. 1, art. no. 1807810, 2020, doi: https://doi.org/10.1080/23311932.2020.1807810

M. Mataragas, V. Dimitriou, P. N. Skandamis, and E. H. Drosinos, “Quantifying the spoilage and shelf-life of yogurt with fruits,” Food Microbiol., vol. 28, no. 3, pp. 611–616, May 2011, doi: https://doi.org/10.1016/j.fm.2010.11.009

Y. Shao, Y. He, and S. Feng, “Measurement of yogurt internal quality through using Vis/NIR spectroscopy,” Food Res. Int., vol. 40, no. 7, pp. 835–841, Aug. 2007, doi: https://doi.org/10.1016/j.foodres.2007.01.014

O. S. Papadopoulou, A. A. Argyri, V. Kounani, C. C. Tassou, and N. Chorianopoulos, “Use of Fourier Transform Infrared Spectroscopy for Monitoring the Shelf Life and Safety of Yogurts Supplemented With a Lactobacillus plantarum Strain With Probiotic Potential,” Front. Microbiol., vol. 12, art. no. 678356, Jun. 2021, doi: https://doi.org/10.3389/fmicb.2021.678356

A. G. Cruz, E. H. M. Walter, R. Silva Cadena, J. A. F. Faria, H. M. A. Bolini, H. P. Pinheiro, A. S. Sant’Ana, “Survival analysis methodology to predict the shelf-life of probiotic flavored yogurt,” Food Res. Int., vol. 43, no. 5, pp. 1444–1448, Jun. 2010, doi: https://doi.org/10.1016/j.foodres.2010.04.028

J. Stangierski, D. Weiss, and A. Kaczmarek, “Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese,” Eur. Food Res. Technol., vol. 245, no. 11, pp. 2539–2547, Nov. 2019, doi: https://doi.org/10.1007/s00217-019-03369-y

S. Goyal and G. K. Goyal, “Shelf Life Estimation of Processed Cheese by Artificial Neural Network Expert Systems,” J. Adv. Comput. Sci. Technol., vol. 1, no. 1, pp. 32-41, 2012. [Online]. Available: https://www.sciencepubco.com/index.php/JACST/article/view/10/439

J. F. Oblitas-Cruz and J. A. Sánchez-González, “Application of Weibull analysis and artificial neural networks to predict the useful life of the vacuum packed soft cheese,” Rev. Fac. Ing. Univ. Antioquia, no. 82, pp. 53–59, Mar. 2017, doi: https://doi.org/10.17533/udea.redin.n82a07

R. R. B. Singh, A. P. Ruhil, D. K. Jain, A. A. Patel, and G. R. Patil, “Prediction of sensory quality of UHT milk – A comparison of kinetic and neural network approaches,” J. Food Eng., vol. 92, no. 2, pp. 146–151, May 2009, doi: https://doi.org/10.1016/j.jfoodeng.2008.10.032

X. Dong, Q. Li, D. Sun, X. Chen, and X. Yu, “Direct FTIR Analysis of Free Fatty Acids in Edible Oils Using Disposable Polyethylene Films,” Food Anal. Methods, vol. 8, no. 4, pp. 857–863, 2015, doi: https://doi.org/10.1007/s12161-014-9963-y

I. Ahmad, M. Hao, Y. Li, J. Zhang, Y. Ding, and F. Lyu, “Fortification of yogurt with bioactive functional foods and ingredients and associated challenges - A review,” Trends Food Sci. Technol., vol. 129, pp. 558–580, Nov. 2022, doi: https://doi.org/10.1016/j.tifs.2022.11.003

W. F. Castro, A. G. Cruz, M. S. Bisinotto, L. M. R. Guerreiro, et al., “Development of probiotic dairy beverages: Rheological properties and application of mathematical models in sensory evaluation,” J. Dairy Sci., vol. 96, no. 1, pp. 16–25, 2013, doi: https://doi.org/10.3168/jds.2012-5590

E. Al-Kadamany, M. Khattar, T. Haddad, and I. Toufeili, “Estimation of shelf-life of concentrated yogurt by monitoring selected microbiological and physicochemical changes during storage,” LWT – Food, Sci. Techol., vol. 36, no. 4, pp. 407–414, 2003, doi: https://doi.org/10.1016/S0023-6438(03)00018-5

E. Al-Kadamany, I. Toufeili, M. Khattar, Y. Abou-Jawdeh, S. Harakeh, and T. Haddad, “Determination of shelf life of concentrated yogurt (labneh) produced by in-bag straining of set yogurt using hazard analysis,” J. Dairy Sci., vol. 85, no. 5, pp. 1023–1030, 2002, doi: https://doi.org/10.3168/jds.s0022-0302(02)74162-3

R. Arboretti, E. Barzizza, L. Salmaso, R. Ceccato, et al., “Shelf-life prediction: A comparison of methods,” Electron. J. Appl. Stat. Anal., vol. 15, no. 3, pp. 527–552, 2022, doi: https://doi.org/10.1285/i20705948v15n3p527

M. Mahendradattal, F. Bastianl, Kasmiati, and N. Amaliah, “Shelf-life prediction of seasoning powder made from whole fermented fish (peda) by using Arrhenius method,” in Procc. of International Seminar Current Issues and Challenges in Food Safety, 2007, pp. 222–232.

R. Sánchez, F. Cerrón, J. Canchuricra, and M. Aquino, “Vida util del yogur bionatural usando el metodo del valor Q10 y analisis de supervivencia,” Tecnol. Alimentos, pp. 1-9, 2013.

I. Saguy and M. Karel, “Modelling of quality deterioration during food processing and storage,” Food Technol., vol. 34, no. 2, pp. 78-85, 1980.

P. S. Taoukis, T. P. Labuza, and I. S. Saguy, “Kinetics of Food Deterioration and Shelf-Life Prediction,” in Handbook of food engineering practice, United State of America: CRC Press, 1997, pp. 367–407.

R. M. Salinas-Hernández, G. A. González-Aguilar, and M. E. Tiznado-Hernández, “Utilization of physicochemical variables developed from changes in sensory attributes and consumer acceptability to predict the shelf life of fresh-cut mango fruit,” J. Food Sci. Technol., vol. 52, no. 1, pp. 63–77, 2015, doi: https://doi.org/10.1007/s13197-013-0992-0

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Published

2024-07-11

How to Cite

Salazar-Muñoz, Y., Valle, P. A., Rodríguez, E., & Alvarado Ontíveros, M. F. (2024). Predicting the Shelf Life of Dairy Products through Mathematical Modelling and in silico Experimentation. Revista Mexicana De Ingenieria Biomedica, 45(2), 100–113. https://doi.org/10.17488/RMIB.45.2.6

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