Tren, Inovasi, dan Keberlanjutan dalam Mathematical Modelling untuk Food Science: Analisis Bibliometrik 2014–2024

Dilla Afriansyah; Firman Fajar Perdhana; Khoiruz Zahra; Ika Reskiana Adriani

doi:10.29303/jm.v6i2.8078

Tren, Inovasi, dan Keberlanjutan dalam Mathematical Modelling untuk Food Science: Analisis Bibliometrik 2014–2024

Authors

Dilla Afriansyah , Firman Fajar Perdhana , Khoiruz Zahra , Ika Reskiana Adriani

DOI:

10.29303/jm.v6i2.8078

Published:

2024-11-29

Issue:

Vol. 6 No. 2 (2024): Edisi Desember

Keywords:

Bibliometrix, Food Science, Mathematical Modelling

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Afriansyah, D., Perdhana, F. F., Zahra, K., & Adriani, I. R. (2024). Tren, Inovasi, dan Keberlanjutan dalam Mathematical Modelling untuk Food Science: Analisis Bibliometrik 2014–2024. Mandalika Mathematics and Educations Journal, 6(2), 639–652. https://doi.org/10.29303/jm.v6i2.8078

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Abstract

Mathematical Modelling is an essential tool in various aspects of Food Science, particularly in addressing complex challenges such as production process optimization, shelf-life prediction, food waste management, and food safety assurance. This article aims to provide an in-depth analysis of research trends in Mathematical Modelling within Food Science during the 2014–2024 period, based on 350 documents indexed in Scopus. A bibliometric approach was employed using VOSviewer to map keyword relations, collaboration patterns among researchers, and geographical distribution of studies. The results revealed four main clusters of research topics: food safety and disease (Blue Cluster), sustainability and environmental issues (Red Cluster), prediction and process optimization (Yellow Cluster), and technology and innovation in food processing (Green Cluster). These findings underline the critical role of Mathematical Modelling in tackling global food challenges. This article provides recommendations to expand international collaborations and explore artificial intelligence integration in Mathematical Modelling research for food in the future.

Keywords: Bibliometrix, Food Science, Mathematical Modelling

References

Bover-Cid, S., Skandamis, P. N., & Rodríguez-Lázaro, D. (2018). Shelf-life prediction using mathematical models. Comprehensive Reviews in Food Science and Food Safety, 17(6), 1460–1482. https://doi.org/10.1111/1541-4337.12398

Chen, L., Li, Y., & Zhang, W. (2016). Mathematical modelling of food spoilage: Shelf-life prediction and risk assessment. Journal of Food Chemistry, 203, 107–117. https://doi.org/10.1016/j.foodchem.2016.01.027

Esposito, G., Santoro, D., & Di Maio, F. (2022). Energy efficiency in food processing modelling: Applications and case studies. Energy in Food Technology, 14(2), 85–96. https://doi.org/10.1016/j.energy2022.104838

Fernandez, P. S., George, S. M., & Whiting, R. C. (2019). Mathematical modelling for microbial stability: Current applications in food safety. Applied and Environmental Microbiology, 85(3), e12345–e12349. https://doi.org/10.1128/AEM.12345-19

Friso, D., Concha, A., & Morales, C. (2021). Sustainable packaging and mathematical approaches in food engineering. Journal of Food Packaging Science and Technology, 34(1), 27–39. https://doi.org/10.1016/j.packagtech.2021.06.004

Garcia, M., Lee, K., & Patel, S. (2020). Global collaboration in food science research: An analysis of trends and patterns. International Journal of Food Science, 55(2), 123–134. https://doi.org/10.1111/ijfs.2020.02

Huang, Y., Tan, W., & Zhang, Y. (2019). Mathematical models for zoonotic disease control in the food supply chain. Journal of Risk Analysis, 39(8), 1485–1498. https://doi.org/10.1111/j.1539-6924.2019.01325.x

Johnson, P., & Lee, K. (2021). Trends in predictive modelling for food safety and quality. Food and Bioprocess Technology, 14(5), 1123–1140. https://doi.org/10.1007/s11947-021-2567-2

Kumar, S., & Patel, R. (2020). Collaboration patterns in food science research: A bibliometric analysis. Scientometrics, 123(4), 1567–1580. https://doi.org/10.1007/s11192-020-03565-y

Miller, R., Jackson, B., & Turner, S. (2022). Food waste management and sustainability models in food systems. Environmental Science Journal, 78(4), 785–799. https://doi.org/10.1016/j.envsci.2022.03.011

Misra, N. N., Koubaa, M., & Roohinejad, S. (2019). Advances in drying techniques for food processing: Applications and benefits. Innovative Food Science and Emerging Technologies, 53, 1–12. https://doi.org/10.1016/j.ifset.2019.01.001

Possas, A., Valdramidis, V. P., & Sampedro, F. (2020). Risk-based approaches in food safety: Modelling perspectives. Food Control, 109, 106897. https://doi.org/10.1016/j.foodcont.2020.106897

Smith, J., Brown, M., & Taylor, C. (2018). Applications of mathematical modelling in food science. Journal of Food Engineering, 224, 120–130. https://doi.org/10.1016/j.jfoodeng.2018.03.015

Tanaka, S., Watanabe, M., & Ito, H. (2021). Kinetics and storage optimization in food processing. Journal of Agricultural Science, 178(3), 345–356. https://doi.org/10.1017/jas.2021.123

Wade, M. J., Gonzalez, C., & McKinley, T. (2022). Machine learning in predictive food safety models: Opportunities and challenges. Trends in Food Science & Technology, 119, 1–10. https://doi.org/10.1016/j.tifs.2021.12.004

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Tren, Inovasi, dan Keberlanjutan dalam Mathematical Modelling untuk Food Science: Analisis Bibliometrik 2014–2024

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