Modeling in food across the scales: towards a universal mass transfer simulator of small molecules in food

Vitrac, Olivier; Hayert, Murielle

doi:10.1007/s42452-020-03272-2

Cited by 4 publications

(6 citation statements)

References 55 publications

(61 reference statements)

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“…Among mechanical, heat, and mass transfer problems, fracture, mixing, wetting, phase separation and multiphasic flow problems with dynamic and free surfaces are not readily manageable using the numerical strategies cited above, as they involve complex descriptions at more than one length or time scale. Several extensions using particle or Lagrangian descriptions have been proposed to describe mass transfers at the food-structure scale (Vitrac & Hayert, 2020) or the coupling between oxidation reactions and anisothermal oil flow (Touffet et al, 2021). It is still an active field of research, and the available software packages are still under-intuitive and require some level of coding.…”

Section: Limitationsmentioning

confidence: 99%

A primer on predictive techniques for food and bioresources transformation processes

Sicard

Barbe²,

Boutrou

et al. 2023

J Food Process Engineering

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To meet current societal demand for more sustainable transformation processes and bioresources, these processes must be optimized and new ones developed. The evolution of various systems (raw material, food, or process attributes) can be predicted to optimize the uses of biomass for better quality, safety, economic benefit, and sustainability. Predictive modeling can guide the necessary changes and influence industrials, governmental policies and consumers decision-making. However, achieving good predictive capability requires reflection on the models and model validation, which can be difficult. This review aims to help scientists begin to predict by presenting the techniques currently used in predictive science for food and related bioproducts. First, a guideline helps readers initiate a prediction process along with final tips and a warning about the risks involved, with a particular focus on the crucial validation step. Three broad categories of techniques are then presented: empirical, mechanistic, and artificial intelligence (or "data-driven"). For each category, the advantages and limitations of current techniques for prediction are explained in light of their current domains of applications, illustrated with literature studies and a detailed example. Thus this article provides engineering researchers information about predictive modeling which is a recent relevant development in optimization of both food and nonfood bioresources processes. Practical applicationsPredictive modeling is a recent development of much relevance in the optimization of both food and nonfood bioresources processes. The goal of this article is to guide those in research or industry who would like to start predicting. Therefore, the article is intended as a primer on prediction concepts and predictive techniques for food and non-food bioresources processing. Three categories of techniques commonly used in these fields are illustrated by various examples of current applications and a more detailed example helps to understand the implementation process. An increased ability of the global scientific body to predict the outcome of various decisions, often linked or sequential, will open new avenues for designing food products with circularity in mind: maintaining value and not creating waste in the process.

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Section: Limitationsmentioning

confidence: 99%

A primer on predictive techniques for food and bioresources transformation processes

Sicard

Barbe²,

Boutrou

et al. 2023

J Food Process Engineering

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“…When rare events control macroscopic properties, Lagrangian or detailed descriptions have been devised to cross all timescales continuously. Such models have successfully calculated diffusion in emulsified systems 46 , evaluated oil uptake in French fries, and predicted thermal oxidation of frying oil in deep-fryer design 47 .…”

Section: Molecular Intermediate and Multiscale Physics-based Modelsmentioning

confidence: 99%

“…In design and optimization using CAFE, the goal is often to find trends or directionalities where empirically predicted properties often suffice. When more detailed understanding at the microscopic scale is needed-for example when changing a food formulation or combining it with a process to obtain expected properties such as texture, chemical reactions, digestibility, or bio-accessibility 61 -lower-scale, primarily theory-based models for predicting food properties can be critically important 46 .…”

Section: Physicochemical Properties Of Foods Needed For Mechanistic M...mentioning

confidence: 99%

Computer-aided food engineering

et al. 2022

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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.

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“…For any finite region divided into cells, the probability of reaching each cell on the long term is proportional to the cell volume divided by the local activity coefficient with a normalization, which depends on the distance to the neighboring regions as shown in Ref. (Vitrac and Hayert, 2020).…”

Section: An Analytical Example Of How To Treat Microscopic Mechanisms Of Diffusion With Radial Forcesmentioning

confidence: 99%

“…in 1, 2, and 3 dimensions with the following conventions based on the results of Ref (Vitrac and Hayert, 2020)…”

mentioning

confidence: 99%

In Silico Prediction of Food Properties: A Multiscale Perspective

2022

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Several open software packages have popularized modeling and simulation strategies at the food product scale. Food processing and key digestion steps can be described in 3D using the principles of continuum mechanics. However, compared to other branches of engineering, the necessary transport, mechanical, chemical, and thermodynamic properties have been insufficiently tabulated and documented. Natural variability, accented by food evolution during processing and deconstruction, requires considering composition and structure-dependent properties. This review presents practical approaches where the premises for modeling and simulation start at a so-called “microscopic” scale where constituents or phase properties are known. The concept of microscopic or ground scale is shown to be very flexible from atoms to cellular structures. Zooming in on spatial details tends to increase the overall cost of simulations and the integration over food regions or time scales. The independence of scales facilitates the reuse of calculations and makes multiscale modeling capable of meeting food manufacturing needs. On one hand, new image-modeling strategies without equations or meshes are emerging. On the other hand, complex notions such as compositional effects, multiphase organization, and non-equilibrium thermodynamics are naturally incorporated in models without linearization or simplifications. Multiscale method’s applicability to hierarchically predict food properties is discussed with comprehensive examples relevant to food science, engineering and packaging. Entropy-driven properties such as transport and sorption are emphasized to illustrate how microscopic details bring new degrees of freedom to explore food-specific concepts such as safety, bioavailability, shelf-life and food formulation. Routes for performing spatial and temporal homogenization with and without chemical details are developed. Creating a community sharing computational codes, force fields, and generic food structures is the next step and should be encouraged. This paper provides a framework for the transfer of results from other fields and the development of methods specific to the food domain.

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Modeling in food across the scales: towards a universal mass transfer simulator of small molecules in food

Cited by 4 publications

References 55 publications

A primer on predictive techniques for food and bioresources transformation processes

A primer on predictive techniques for food and bioresources transformation processes

Computer-aided food engineering

In Silico Prediction of Food Properties: A Multiscale Perspective

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