Physics-Informed Machine Learning: the Next Big Trend in Food Process Modelling?
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s43555-023-00012-6.pdf
Reference23 articles.
1. Madoumier M, Trystram G, Sébastian P, Collignan A. Towards a holistic approach for multi-objective optimization of food processes: a critical review. Trends Food Sci Technol. 2019;86:1–15. https://doi.org/10.1016/j.tifs.2019.02.002.
2. Purlis E, Cevoli C, Fabbri A. Modelling volume change and deformation in food products/processes: an overview. Foods. 2021;10(4):778. https://doi.org/10.3390/foods10040778.
3. Bradley W, Kim J, Kilwein Z, Blakely L, Eydenberg M, Jalvin J, et al. Perspectives on the integration between first-principles and data-driven modeling. Comput Chem Eng. 2022;166:107898. https://doi.org/10.1016/j.compchemeng.2022.107898. This review article can be taken as starting point to understand machine learning tools and hybrid modelling approaches.
4. Sansana J, Joswiak MN, Castillo I, Wang Z, Rendall R, Chiang LH, et al. Recent trends on hybrid modeling for Industry 4.0. Comput Chem Eng. 2021;151:107365. https://doi.org/10.1016/j.compchemeng.2021.107365.
5. Zhou L, Zhang C, Liu F, Qiu Z, He Y. Application of deep learning in food: a review. Compr Rev Food Sci Food Saf. 2019;18(6):1793–811. https://doi.org/10.1111/1541-4337.12492.
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