Food modelling strategies and approaches for knowledge transfer

Autor: Kamal Kansou, Wim Laurier, Maria N. Charalambides, Guy Della-Valle, Ilija Djekic, Aberham Hailu Feyissa, Francesco Marra, Rallou Thomopoulos, Bert Bredeweg
Přispěvatelé: Faculteit Onderwijs en Opvoeding
Rok vydání: 2022
Předmět:
Zdroj: Trends in Food Science & Technology, 120, 363-373. Elsevier
Kansou, K, Laurier, W, Charalambides, M N, Della-Valle, G, Djekic, I, Feyissa, A H, Marra, F, Thomopoulos, R & Bredeweg, B 2022, ' Food modelling strategies and approaches for knowledge transfer ', Trends in Food Science and Technology, vol. 120, pp. 363-373 . https://doi.org/10.1016/j.tifs.2022.01.021
ISSN: 0924-2244
DOI: 10.1016/j.tifs.2022.01.021
Popis: Background: Scientific software incorporates models that capture fundamental domain knowledge. This software is becoming increasingly more relevant as an instrument for food research. However, scientific software is currently hardly shared among and (re-)used by stakeholders in the food domain, which hampers effective dissemination of knowledge, i.e. knowledge transfer. Scope and approach: This paper reviews selected approaches, best practices, hurdles and limitations regarding knowledge transfer via software and the mathematical models embedded in it to provide points of reference for the food community. Key findings and conclusions: The paper focusses on three aspects. Firstly, the publication of digital objects on the web, which offers valorisation software as a scientific asset. Secondly, building transferrable software as way to share knowledge through collaboration with experts and stakeholders. Thirdly, developing food engineers' modelling skills through the use of food models and software in education and training.
Databáze: OpenAIRE
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