Multiscale modeling for food drying: A homogenized diffusion approach

An Empirical Model for Predicting the Fresh Food Quality Changes during Storage

It is widely recognized that the quality of fruits and vegetables can be altered during transportation and storage. Firmness and loss of weight are the crucial attributes used to evaluate the quality of various fruits, as many other quality attributes are related to these two attributes. These properties are influenced by the surrounding environment and preservation conditions. Limited research has been conducted to accurately predict the quality attributes during transport and storage as a function of storage conditions. In this research, extensive experimental investigations have been conducted on the changes in quality attributes of four fresh apple cultivars (Granny Smith, Royal Gala, Pink Lady, and Red Delicious) during transportation and storage. The study evaluated the weight loss and change in firmness of these apples varieties at different cooling temperatures ranging from 2 °C to 8 °C to assess the impact of storing at these temperatures on the quality attributes. The results indicate that the firmness of each cultivar continuously decreased over time, with the R² values ranging from 0.9489-0.8691 for red delicious, 0.9871-0.9129 for royal gala, 0.9972-0.9647 for pink lady, and 0.9964-0.9484 for granny smith. The rate of weight loss followed an increasing trend with time, and the high R² values indicate a strong correlation. The degradation of quality was evident in all four cultivars, with temperature having a significant impact on firmness. The decline in firmness was found to be minimal at 2 °C, but increased as the storage temperature increased. The loss of firmness also varied among the four cultivars. For instance, when stored at 2 °C, the firmness of pink lady decreased from an initial value of 8.69 kg·cm² to 7.89 kg·cm² in 48 h, while the firmness of the same cultivar decreased from 7.86 kg·cm² to 6.81 kg·cm² after the same duration of storage. Based on the experimental results, a multiple regression quality prediction model was developed as a function of temperature and time. The proposed models were validated using a new set of experimental data. The correlation between the predicted and experimental values was found to be excellent. The linear regression equation yielded an R² value of 0.9544, indicating a high degree of accuracy. The model can assist stakeholders in the fruit and fresh produce industry in anticipating quality changes at different storage stages based on the storage conditions.

Variables related to microwave heating-toasting time and water migration assessment with kernel size approaches of specialty maize types

BACKGROUND

Three main maize types with specialty kernels are used to make ready-to-eat maize by traditional toasting, and microwave toasting may be an innovative application. However, little is known of the toasting process of these Andean maize types. Therefore, the present study aimed to explore the behavior of a broad scope of variables in these maize types. The kernels were packed in sealed paper envelopes and subjected to six microwave heating-toasting times from 0 to 390 s. Subsequently, with actual kernel size approaches, water content (WC), water ratio (WR), and water loss (WL) were analyzed.

RESULTS

In addition to WC, WR, and WL, the surface area (S), volume (V), and geometric mean diameter (GMD) behaved like time-related variables with a high correlation depending on the maize types and kernel dimensions. Thus, the WC, WR, and WL third-order polynomial regression curves computed with the spatial (S/V)² and distance (GMD/2)² approaches indicated the water variation at each microwave heating-toasting time with a clear difference among maize types a0, a1, and a2. Regarding their exchange profiles without and with the spatial (S/V)² approach, the maximum rates showed significant differences between maize types and WC and WL. Likewise, the maximum rates displayed significant differences between the spatial (S/V)² and distance (GMD/2)² approaches, revealing a notable lack of consistency with the distance (GMD/2)² approach.

CONCLUSION

The kernel size approaches revealed that water migration rates depended on differences in maize types. Such basic information represents the first insight into more physical-based models of water diffusion during raw microwave maize heating-toasting. © 2022 Society of Chemical Industry.

Experimental and numerical evaluation of the effect of micro-aeration on the thermal properties of chocolate

Thermal properties, such as thermal conductivity, specific heat capacity and latent heat, influence the melting and solidification of chocolate. The accurate prediction of these properties for micro-aerated chocolate products with varying levels of porosity ranging from 0% to 15% is beneficial for understanding and control of heat transfer mechanisms during chocolate manufacturing and food oral processing. The former process is important for the final quality of chocolate and the latter is associated with sensorial attributes, such as grittiness, melting time and flavour. This study proposes a novel multiscale finite element model to accurately predict the temporal and spatial evolution of temperature across chocolate samples. The model is evaluated via heat transfer experiments at temperatures varying from 16 °C to 45 °C. Both experimental and numerical results suggest that the rate of heat transfer within the micro-aerated chocolate is reduced by 7% when the 15% micro-aerated chocolate is compared to its solid counterpart. More specifically, on average, the thermal conductivity decreased by 20% and specific heat capacity increased by 10% for 15% micro-aeration, suggesting that micro-pores act as thermal barriers to heat flow. The latter trend is unexpected for porous materials and thus the presence of a third phase at the pore's interface is proposed which might store thermal energy leading to a delayed release to the chocolate system. The developed multiscale numerical model provides a design tool to create pore structures in chocolate with optimum melting or solidifying response.

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.