Enabling computer-aided food process engineering: Property estimation equations for transport phenomena-based models

Computer-aided food engineering

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.

In Silico Prediction of Food Properties: A Multiscale Perspective

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.

Dynamic Thermal Properties Estimation Using Sensitivity Coefficients for Rapid Heating Process

Thermal conductivity determination of food at temperatures > 100 °C still remains a challenge. The objective of this study was to determine the temperature-dependent thermal conductivity of food using rapid heating (TPCell). The experiments were designed based on scaled sensitivity coefficient (SSC), and the estimated thermal conductivity of potato puree was compared between the constant temperature heating at 121.10 °C (R12B10T1) and the rapid heating (R22B10T1). Temperature-dependent thermal conductivity models along with a constant conductivity were used for estimation. R22B10T1 experiment using the k model provided reliable measurements as compared to R12B10T1 with thermal conductivity values from 0.463 ± 0.011 W m⁻¹ K⁻¹ to 0.450 ± 0.016 W m⁻¹ K⁻¹ for 25–140 °C and root mean squares error (RMSE) of 1.441. In the R12B10T1 experiment, the analysis showed the correlation of residuals, which made the estimation less reliable. The thermal conductivity values were in the range of 0.444 ± 0.012 W m⁻¹ K⁻¹ to 0.510 ± 0.034 W m⁻¹ K⁻¹ for 20–120 °C estimated using the k model. Temperature-dependent models (linear and k models) provided a better estimate than the single parameter thermal conductivity determination with low RMSE for both types of experiments. SSC can provide insight in designing dynamic experiments for the determination of thermal conductivity coefficient.

Modeling time-temperature history and sterilization value of mango puree under conventional and microwave assisted pasteurization

Continuous pasteurization of liquid foods has to provide the desired lethality level to guarantee food safety with minimum degradation of quality attributes (sensorial and nutritional characteristics) and high energy efficiency. To optimize quality and cost, a thermal process should be modeled considering flow, heat transfer and mass dispersion principles; however, flow through helical tubes and microwave heating require a complex 3D multiphysics approach. Herein a simplified 2D approach is presented to model a hybrid pasteurization unit with conventional and microwave heating under laminar flow to predict axial and radial distributions of temperature and residual activity of a microorganism or enzyme. A study case of 20 °Brix mango puree (power law fluid) processing is used to test the model based on an existing pilot plant unit. Results were useful to compare conventional and microwave heating regarding the process sterilization value and model can be used for process analysis, design and optimization.

Modeling Food Particle Systems: A Review of Current Progress and Challenges

For many years, food engineers have attempted to describe physical phenomena such as heat and mass transfer in food via mathematical models. Still, the impact and benefits of computer-aided engineering are less established in food than in most other industries today. Complexity in the structure and composition of food matrices are largely responsible for this gap. During processing of food, its temperature, moisture, and structure can change continuously, along with its physical properties. We summarize the knowledge foundation, recent progress, and remaining limitations in modeling food particle systems in four relevant areas: flowability, size reduction, drying, and granulation and agglomeration. Our goal is to enable researchers in academia and industry dealing with food powders to identify approaches to address their challenges with adequate model systems or through structural and compositional simplifications. With advances in computer simulation capacity, detailed particle-scale models are now available for many applications. Here, we discuss aspects that require further attention, especially related to physics-based contact models for discrete-element models of food particle systems.

Modeling and simulation of heat and mass transfer in an Ethiopian fresh injera drying process

In this paper, we developed a mathematical model to simulate the heat and mass transfer during the convective drying of injera. The coupled set of heat and moisture partial differential equations (PDEs) were numerically solved by the finite element method (FEM) using COMSOL Multi-physics, 5.5. To validate the simulated results, drying experiments were performed using a tunnel dryer at two air temperatures (313.15 and 333.15 K) and velocities (0.25 and 0.5 ms⁻¹). The predicted versus the experimental results showed a very good agreement with a coefficient of determination, R2>0.95 for both temperature and moisture ratio and a Root Mean Square Error, RMSE < 0.05 for moisture ratio and <3.5 K for temperature. The predicted temperature and moisture ratio distributions of the injera at different times and positions (thickness and diameter) clearly showed the uniformity of drying. The time required to reduce the moisture ratio of injera from 1 (-) to 0.03 (-) at a temperature of 333.15 K, relative humidity of 11% and air velocity of 0.5 ms⁻¹ was 125 min. Both temperature and velocity have a significant effect on moisture reduction when drying was conducted (p < 0.05). The interaction effect between them also indicates a significant difference (p < 0.05) in the moisture removal rate of injera.

Multiphase porous media modelling: A novel approach to predicting food processing performance

The development of a physics-based model of food processing is essential to improve the quality of processed food and optimize energy consumption. Food materials, particularly plant-based food materials, are complex in nature as they are porous and have hygroscopic properties. A multiphase porous media model for simultaneous heat and mass transfer can provide a realistic understanding of transport processes and thus can help to optimize energy consumption and improve food quality. Although the development of a multiphase porous media model for food processing is a challenging task because of its complexity, many researchers have attempted it. The primary aim of this paper is to present a comprehensive review of the multiphase models available in the literature for different methods of food processing, such as drying, frying, cooking, baking, heating, and roasting. A critical review of the parameters that should be considered for multiphase modelling is presented which includes input parameters, material properties, simulation techniques and the hypotheses. A discussion on the general trends in outcomes, such as moisture saturation, temperature profile, pressure variation, and evaporation patterns, is also presented. The paper concludes by considering key issues in the existing multiphase models and future directions for development of multiphase models.

Multiphysics Modeling of Microwave Heating of a Frozen Heterogeneous Meal Rotating on a Turntable

A 3-dimensional (3-D) multiphysics model was developed to understand the microwave heating process of a real heterogeneous food, multilayered frozen lasagna. Near-perfect 3-D geometries of food package and microwave oven were used. A multiphase porous media model combining the electromagnetic heat source with heat and mass transfer, and incorporating phase change of melting and evaporation was included in finite element model. Discrete rotation of food on the turntable was incorporated. The model simulated for 6 min of microwave cooking of a 450 g frozen lasagna kept at the center of the rotating turntable in a 1200 W domestic oven. Temperature-dependent dielectric and thermal properties of lasagna ingredients were measured and provided as inputs to the model. Simulated temperature profiles were compared with experimental temperature profiles obtained using a thermal imaging camera and fiber-optic sensors. The total moisture loss in lasagna was predicted and compared with the experimental moisture loss during cooking. The simulated spatial temperature patterns predicted at the top layer was in good agreement with the corresponding patterns observed in thermal images. Predicted point temperature profiles at 6 different locations within the meal were compared with experimental temperature profiles and root mean square error (RMSE) values ranged from 6.6 to 20.0 °C. The predicted total moisture loss matched well with an RMSE value of 0.54 g. Different layers of food components showed considerably different heating performance. Food product developers can use this model for designing food products by understanding the effect of thickness and order of each layer, and material properties of each layer, and packaging shape on cooking performance.

Modeling moisture migration in a multi-domain food system: Application to storage of a sandwich system

Moisture transport in a food system involving two different materials of unequal moisture content was modeled with water activity as the driving force using a porous media framework. This model was applied to a bread-barbecue chicken pocket sandwich stored in isothermal conditions. The model successfully predicted the equilibrium condition, where the two materials, bread and chicken, reached the same water activity, but not the same water content. The transient changes in the moisture content in the bread and chicken were predicted and shown to be significantly affected by air gap between the bread and chicken. The prediction process was also sensitive to the Darcy permeability values for the materials. The modeling framework presented for a sandwich system is very general and can easily be extended to other multicomponent food systems.

Multiscale analysis of structure development in expanded starch snacks

In this paper we perform a multiscale analysis of the food structuring process of the expansion of starchy snack foods like keropok, which obtains a solid foam structure. In particular, we want to investigate the validity of the hypothesis of Kokini and coworkers, that expansion is optimal at the moisture content, where the glass transition and the boiling line intersect. In our analysis we make use of several tools, (1) time scale analysis from the field of physical transport phenomena, (2) the scale separation map (SSM) developed within a multiscale simulation framework of complex automata, (3) the supplemented state diagram (SSD), depicting phase transition and glass transition lines, and (4) a multiscale simulation model for the bubble expansion. Results of the time scale analysis are plotted in the SSD, and give insight into the dominant physical processes involved in expansion. Furthermore, the results of the time scale analysis are used to construct the SSM, which has aided us in the construction of the multiscale simulation model. Simulation results are plotted in the SSD. This clearly shows that the hypothesis of Kokini is qualitatively true, but has to be refined. Our results show that bubble expansion is optimal for moisture content, where the boiling line for gas pressure of 4 bars intersects the isoviscosity line of the critical viscosity 10(6) Pa.s, which runs parallel to the glass transition line.