Multi-scale model of food drying: Current status and challenges

Analytical and numerical solutions of pore formation in elastic food materials during dehydration

In this paper, we describe a model for pore formation in food materials during drying. As a proxy for fruits and vegetables, we take a spherical hydrogel, with a stiff elastic skin, and a central cavity filled with air and water vapour. The model describes moisture transport coupled to large deformation mechanics. Both stress and chemical potential are derived from a free energy functional, following the framework developed by Suo and coworkers. We have compared Finite Volume and Finite Element implementations and analytical solutions with each other, and we show that they render similar solutions. The Finite Element solver has a larger range of numerical stability than the Finite Volume solver, and the analytical solution also has a limited range of validity. Since the Finite Element solver operates using the mathematically intricate weak form, we introduce the method in a tutorial manner for food scientists. Subsequently, we have explored the physics of the pore formation problem further with the Finite Element solver. We show that the presence of an elastic skin is a prerequisite for the growth of the central cavity. The elastic skin must have an elastic modulus of at least 10 times that of the hydrogel. An initial pore with 10% of the size of the gel can grow to 5 times its initial size. Such an increase in porosity has been reported in the literature on drying of vegetables, if a dense hard skin is formed, known as case hardening. We discuss that models as presented in this paper, where moisture transport is strongly coupled to large deformation mechanics, are required if one wants to describe pore/structure formation during drying and intensive heating (as baking and frying) of food materials from first principles.

Microstructural Classification and Reconstruction of the Computational Geometry of Steamed Bread Using Descriptor-Based Approach

Abstract

Microstructures affect the properties of food products; accurate and relatively less complex microstructural representations are thus needed for modelling of transport phenomena during food processing. Hence, the present study aimed at developing computational microstructures of steamed bread using descriptor-based approach. Relevant information was extracted from the scanning electron microscope (SEM) images of the steamed bread and evaluated using seven classifiers. For the automatic classification and using all descriptors, bagged trees ensembles (BTE) had the highest accuracy of 98.40%, while Gaussian Naïve Bayes was the least with 92.10% accuracy. In the “step forward” analysis, five descriptors had higher classification accuracy (98.80%) than all descriptors, implying that increase in descriptors might or might not increase classification accuracy. Microstructural validation revealed that the ellipse fitting method with a p value of 0.7984 for the area was found to be superior to the Voronoi method with a corresponding p value of 1.4554 × 10−5, confirming that the ellipse developed microstructure was more suitable for microscale modelling of transport phenomena in steamed bread.

Machine learning-based modeling in food processing applications: State of the art

Food processing is a complex, multifaceted problem that requires substantial human interaction to optimize the various process parameters to minimize energy consumption and ensure better-quality products. The development of a machine learning (ML)-based approach to food processing applications is an exciting and innovative idea for optimizing process parameters and process kinetics to reduce energy consumption, processing time, and ensure better-quality products; however, developing such a novel approach requires significant scientific effort. This paper presents and evaluates ML-based approaches to various food processing operations such as drying, frying, baking, canning, extrusion, encapsulation, and fermentation to predict process kinetics. A step-by-step procedure to develop an ML-based model and its practical implementation is presented. The key challenges of neural network training and testing algorithms and their limitations are discussed to assist readers in selecting algorithms for solving problems specific to food processing. In addition, this paper presents the potential and challenges of applying ML-based techniques to hybrid food processing operations. The potential of physics-informed ML modeling techniques for food processing applications and their strategies is also discussed. It is expected that the potential information of this paper will be valuable in advancing the ML-based technology for food processing applications.

Mathematical modeling of nutritional, color, texture, and microbial activity changes in fruit and vegetables during drying: A critical review

Retention of quality attributes during drying of fruit and vegetables is a prime concern since the product's acceptability depends on the overall quality; particularly on the nutritional, color, and physical attributes. However, these quality parameters deteriorate during drying. Food quality changes are strongly related to the drying conditions and researchers have attempted to develop mathematical models to understand these relationships. A better insight toward the degradation of quality attributes is crucial for making real predictions and minimizing the quality deterioration. The previous empirical quality models employed kinetic modeling approaches to describe the quality changes and therefore, lack the realistic understanding of fundamental transport mechanisms. In order to develop a physics based mathematical model for the prediction of quality changes during drying, an in-depth understanding of research progress made toward this direction is indispensable. Therefore, the main goal of this paper is to present a critical review of the mathematical models developed and applied to describe the degradation kinetics of nutritional, color, and texture attributes during drying of fruit and vegetables and microbial growth model during storage. This review also presents the advantages and drawbacks of the existing models along with their industrial relevance. Finally, future research propositions toward developing physics-based mathematical model are presented.

The low-field NMR studies the change in cellular water in tilapia fillet tissue during different drying conditions

The muscle is a highly organized tissue, where there are three different moistures including free water, entrapped water, and bound water. These moistures were distributed in intercellular spaces, intracellular spaces, and other solute environments, respectively. Understanding the moisture migration in different environments is crucial to enhance energy efficiency and improve the quality of processed food. Therefore, the tilapia fillets were used to experiment, and the low-field nuclear magnetic resonance technique is used to measure the change in different moistures during the drying process. The study found that free water is the highest when cell membranes started to rupture. In addition, it also observed that the cell membrane ruptures at different stages of drying. The result of this study provides critical information that could be used to guide the study of the dynamic mechanisms underlying drying and the development of drying technology for tilapia fillets and similar aquatic products.

Sustainable food drying technologies based on renewable energy sources

Waste in the food supply chain is estimated to be about 30-40% of the total food production, which aggravates the world hunger and increases waste management burden and environmental impact. Despite the dire food scarcity, majority of this food waste takes place in developing countries because of the lack of appropriate and affordable preservation techniques. Traditional open sun drying is the most popular food-reservation technique to the local farmers due to near-zero capital cost and cheap labor cost. However, this method is highly energy intensive, unhygienic, and time demanding. The high energy consumption resulting from uncontrolled simultaneous heat, mass, and momentum transfer processes in traditional drying systems highlights the necessity of pursuing sustainability in drying process targeting reduced energy consumption, environmental and social impacts. This paper presents a comprehensive review on the sustainable food drying technologies based on renewable energy sources, with emphasis on the developing countries. It was observed that the integration of thermal energy storage with heat pump makes the integrated drying system more efficient, and dries food with better quality. Likewise, advanced integrated drying systems, such as, solar with microwave, and heat pump with microwave make the drying process more cost and quality competent. Finally, impact of resource distribution and governmental incentives for renewable energy use in sustainable drying is discussed.

Utilization of Cumbeba (Tacinga inamoena) Residue: Drying Kinetics and Effect of Process Conditions on Antioxidant Bioactive Compounds

The residue generated from the processing of Tacinga inamoena (cumbeba) fruit pulp represents a large amount of material that is discarded without proper application. Despite that, it is a raw material that is source of ascorbic acid, carotenoids and phenolic compounds, which are valued in nutraceutical diets for allegedly combating free radicals generated in metabolism. This research paper presents a study focused on the mathematical modeling of drying kinetics and the effect of the process on the level of bioactive of cumbeba residue. The experiments of cumbeba residue drying (untreated or whole residue (WR), crushed residue (CR) and residue in the form of foam (FR)) were carried out in a fixed-bed dryer at four air temperatures (50, 60, 70 and 80 °C). Effective water diffusivity (Deff) was determined by the inverse method and its dependence on temperature was described by an Arrhenius-type equation. It was observed that, regardless of the type of pretreatment, the increase in air temperature resulted in higher rate of water removal. The Midilli model showed better simulation of cumbeba residue drying kinetics than the other models tested within the experimental temperature range studied. Effective water diffusivity (Deff) ranged from 6.4890 to 11.1900 × 10-6 m2/s, 2.9285 to 12.754 × 10-9 m2/s and 1.5393 × 10-8 to 12.4270 × 10-6 m2/s with activation energy of 22.3078, 46.7115 and 58.0736 kJ/mol within the temperature range of 50-80 °C obtained for the whole cumbeba, crushed cumbeba and cumbeba residue in the form of foam, respectively. In relation to bioactive compounds, it was observed that for a fixed temperature the whole residue had higher retention of bioactive compounds, especially phenolic compounds, whereas the crushed residue and the residue in the form of foam had intermediate and lower levels, respectively. This study provides evidence that cumbeba residue in its whole form can be used for the recovery of natural antioxidant bioactive compounds, mainly phenolic compounds, with the possibility of application in the food and pharmaceutical industries.

Modelling Volume Change and Deformation in Food Products/Processes: An Overview

Volume change and large deformation occur in different solid and semi-solid foods during processing, e.g., shrinkage of fruits and vegetables during drying and of meat during cooking, swelling of grains during hydration, and expansion of dough during baking and of snacks during extrusion and puffing. In addition, food is broken down during oral processing. Such phenomena are the result of complex and dynamic relationships between composition and structure of foods, and driving forces established by processes and operating conditions. In particular, water plays a key role as plasticizer, strongly influencing the state of amorphous materials via the glass transition and, thus, their mechanical properties. Therefore, it is important to improve the understanding about these complex phenomena and to develop useful prediction tools. For this aim, different modelling approaches have been applied in the food engineering field. The objective of this article is to provide a general (non-systematic) review of recent (2005-2021) and relevant works regarding the modelling and simulation of volume change and large deformation in various food products/processes. Empirical- and physics-based models are considered, as well as different driving forces for deformation, in order to identify common bottlenecks and challenges in food engineering applications.

Identifying in silico how microstructural changes in cellular fruit affect the drying kinetics

Convective drying of fruits leads to microstructural changes within the material as a result of moisture removal. In this study, an upscaling approach is developed to understand and identify the relation between the drying kinetics and the resulting microstructural changes of apple fruit, including shrinkage of cells without membrane breakage (free shrinkage) and with membrane breakage (lysis). First, the effective permeability is computed from a microscale model as a function of the water potential. Both temperature dependency and microstructural changes during drying are modeled. The microscale simulation shows that lysis, which can be induced using various pretreatment processes, enhances the tissue permeability up to four times compared to the free shrinkage of the cells. Second, via upscaling, macroscale modeling is used to quantify the impact of these microstructural changes in the fruit drying kinetics. We identify the formation of a barrier layer for water transport during drying, with much lower permeability, at the tissue surface. The permeability of this layer strongly depends on the dehydration mechanism. We also quantified how inducing lysis or modifying the drying conditions, such as airspeed and relative humidity, can accelerate the drying rate. We found that inducing lysis is more effective in increasing the drying rate (up to 26%) than increasing the airspeed from 1 to 5 m s^-1 or decreasing the relative humidity from 30% to 10%. This study quantified the need for including cellular dehydration mechanisms in understanding fruit drying processes and provided insight at a spatial resolution that experiments almost cannot reach.

A mathematical model for predicting the transport process and quality changes during intermittent microwave convective drying

Intermittent microwave convective drying (IMCD) is an advanced drying method where volumetric heating of samples drives the drying process. Understanding of the physical effects of IMCD on simultaneous heating and mass transfer as well as quality changes during IMCD is essential to predict accurately drying processes and quality attributes of end products. However, there is a lack of studies in this particular interest area. The aim of this research was to develop an IMCD model coupled with quality degradation kinetics by integrating a simultaneous heat and mass transfer model with Maxwell's equations for microwave heating and the chemical reaction kinetics model. The simulated results were compared with experimental results and a good agreement was observed. As it was found that power ratio (PR) had a vital role in altering quality attributes, different PR and drying conditions were considered to investigate the effects of IMCD on the drying kinetics. The simulated results showed that the model was capable of predicting accurately moisture and temperature distributions along with heath beneficial compounds, such as total phenolic content (TPC) and ascorbic acid (AA) as well as colour changes during IMCD processing. About 70% of AA was degraded during IMCD drying using PR of 1/3. However, losses were reduced when PR was reduced to 1/4 or 1/5. Likewise, TPC degraded significantly during the early stages (first 60 min) of IMCD processing but stabilised at later stages.

Multiscale Modeling for Food Drying: State of the Art

Plant-based food materials are mostly porous in nature and heterogeneous in structure with huge diversity in cellular orientation. Different cellular environments of plant-based food materials, such as intercellular, intracellular, and cell wall environments, hold different proportions of water with different characteristics. Due to this structural heterogeneity, it is very difficult to understand the drying process and associated morphological changes during drying. Transport processes and morphological changes that take place during drying are mainly governed by the characteristics of and the changes in the cells. Therefore, to predict the actual heat and mass transfer process that occurs in the drying process and associated morphological changes, development of multiscale modeling is crucial. Multiscale modeling is a powerful approach with the ability to incorporate this cellular structural heterogeneity with microscale heat and mass transfer during drying. However, due to the huge complexity involved in developing such a model for plant-based food materials, the studies regarding this issue are very limited. Therefore, we aim in this article to develop a critical conceptual understanding of multiscale modeling frameworks for heterogeneous food materials through an extensive literature review. We present a critical review on the multiscale model formulation and solution techniques with their spatial and temporal coupling options. Food structure, scale definition, and the current status of multiscale modeling are also presented, along with other key factors that are critical to understanding and developing an accurate multiscale framework. We conclude by presenting the main challenges for developing an accurate multiscale modeling framework for food drying.