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Computer-aided food engineering.
Datta, Ashim; Nicolaï, Bart; Vitrac, Olivier; Verboven, Pieter; Erdogdu, Ferruh; Marra, Francesco; Sarghini, Fabrizio; Koh, Chris.
Afiliación
  • Datta A; Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA. akd1@cornell.edu.
  • Nicolaï B; Biosystems Department - MeBioS Division, Katholieke Universiteit Leuven, Leuven, Belgium.
  • Vitrac O; Université Paris-Saclay, INRAE, AgroParisTech, UMR 0782 SayFood, Massy, France.
  • Verboven P; Biosystems Department - MeBioS Division, Katholieke Universiteit Leuven, Leuven, Belgium.
  • Erdogdu F; Department of Food Engineering, Ankara University, Golbasi-Ankara, Turkey.
  • Marra F; Department of Industrial Engineering, University of Salerno, Fisciano, Italy.
  • Sarghini F; Department of Agricultural Sciences, Agricultural and Biosystems Engineering, University of Naples Federico II, Portici, Italy.
  • Koh C; PepsiCo R&D, PepsiCo, Plano, TX, USA.
Nat Food ; 3(11): 894-904, 2022 11.
Article en En | MEDLINE | ID: mdl-37118206
Computer-aided food engineering (CAFE) can reduce resource use in product, process and equipment development, improve time-to-market performance, and drive high-level innovation in food safety and quality. Yet, CAFE is challenged by the complexity and variability of food composition and structure, by the transformations food undergoes during processing and the limited availability of comprehensive mechanistic frameworks describing those transformations. Here we introduce frameworks to model food processes and predict physiochemical properties that will accelerate CAFE. We review how investments in open access, such as code sharing, and capacity-building through specialized courses could facilitate the use of CAFE in the transformation already underway in digital food systems.

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Nat Food Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Nat Food Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos