A Comprehensive Review of Mathematical Modeling for Drying Processes of Fruits and Vegetables

Achieving sustainability in heat drying processing: Leveraging artificial intelligence to maintain food quality and minimize carbon footprint

The food industry is a significant contributor to carbon emissions, impacting carbon footprint (CF), specifically during the heat drying process. Conventional heat drying processes need high energy and diminish the nutritional value and sensory quality of food. Therefore, this study aimed to investigate the integration of artificial intelligence (AI) in food processing to enhance quality and reduce CF, with a focus on heat drying, a high energy-consuming method, and offer a promising avenue for the industry to be consistent with sustainable development goals. Our finding shows that AI can maintain food quality, including nutritional and sensory properties of dried products. It determines the optimal drying temperature for improving energy efficiency, yield, and life cycle cost. In addition, dataset training is one of the key challenges in AI applications for food drying. AI needs a vast and high-quality dataset that directly impacts the performance and capabilities of AI models to optimize and automate food drying.

Integrated drying model of lychee as a function of temperature and relative humidity

Drying is a universal method applied for food preservation. To date, several models have been developed to evaluate drying kinetics. In this study, lychee was dried employing a hot air dryer, and the drying kinetics was evaluated by comparing the Newtonian model, Henderson and Pabis model, Page model, and Logarithmic model. However, temperature and relative humidity, the key driving forces for drying kinetics, are not considered by these models. Thus, an integrated drying model, as a function of temperature and relative humidity, was developed to predict the hot air-drying kinetics and mass transfer phenomena of lychee followed by the calibration and validation of the model with independent experimental datasets. The model validation consisted of Nash- Sutcliffe model coefficient (E ), coefficient of determination ( R 2 ) and index of agreement ( d ) and all of them were found close to 1 indicating perfect model fit. Besides, the developed model was applied for process optimization and scenario analysis. The drying rate constant was found as a function of temperature and relative humidity that was high at high temperature and low relative humidity. Interestingly, temperature showed a higher effect on the drying rate constant compared to relative humidity. Overall, the present study will open a new window to developing further drying model of lychee to optimize quality its quality parameters.

Mathematical Modeling Approach and Simulation in Food Drying Applications

Recent developments in the branch of food drying involve advancements in the development of mathematical models [...].

Effective moisture diffusivity prediction in two Portuguese fruit cultivars (Bravo de Esmolfe apple and Madeira banana) using drying kinetics data

Air convective dehydration was performed at various temperatures (35 °C, 40 °C, 45 °C and 50 °C) using two types of fruits cultivars produced in different regions of Portugal: the Bravo de Esmolfe apple, from the Beiras province, and the Cavendish banana, from Madeira Island. The data collected were used to predict the effective moisture diffusion, which is a crucial input parameter in drying modeling and design. As expected, the values obtained in both falling drying rate periods detected for apples increased with an increase in drying temperature. The effective moisture diffusion in apples varied from 1.968 × 10^-10 m² s^-1 at 35 °C to 4.013 × 10^-10 m² s^-1 at 50 °C, for the first falling drying rate period, and from 0.9567 × 10^-10 m² s^-1 at 35 °C to 3.328 × 10^-10 m² s^-1 at 50 °C, for the second period. The dependence of effective moisture diffusion on temperature for bananas is similar, ranging from 1.572 × 10^-10 to 2.627 × 10^-10 m² s^-1 as the drying temperature changed from 35 to 50 °C.

Effect of vacuum freeze drying and hot air drying on dried mulberry fruit quality

Two different drying methods (vacuum freeze-drying and hot-air drying) were used to dry mulberry of three varieties 'Baiyuwang'(D1), 'Longsang'(D2) and 'Zhongshen.1'(D3), and the fresh fruit of each variety was used as the control. The effects of different processing conditions on the physical characteristics, nutrients, functional components and antioxidant activity of mulberry fruit were analyzed. The results show that after different drying methods, after vacuum freeze-drying, the physical properties of dried mulberry fruit such as wettability, hygroscopic property and water retention, soluble protein, ascorbic acid and other nutrients, functional components such as polyphenols, resveratrol, chlorogenic acid and anthocyanin, and antioxidant activities such as DPPH free radical scavenging ability and ABTS free radical scavenging ability were superior to hot air drying (P < 0.01). It was concluded that vacuum freeze drying was more beneficial for retaining the original quality of mulberry than hot air drying. This study can provide a retaining theoretical basis for mulberry deep processing and comprehensive development and utilization.

Effect of Different Drying Techniques on Total Bioactive Compounds and Individual Phenolic Composition in Goji Berries

In recent years, interest in the consumption of dried goji berries has increased due to its high bioactive properties. Alternative drying methods that provide faster drying and better preservation of bioactive properties should be developed. This study aims to investigate the effect of different drying methods on the drying time and quality characteristics of the goji berry; namely, hot-air drying (HAD), ultrasound-assisted vacuum drying (USVD), vacuum drying (VD), freeze-drying (FD), and ultrasound-pretreated freeze-drying (USFD). The drying kinetic, total phenolic content, antioxidant capacity, phenolic profile, carotenoid profile, and color change of the goji berry fruit were determined. The drying times for VD, USVD, and HAD varied between 275–1330 min. USVD treatment showed faster drying behavior than the other two drying methods. The total phenolic content (TPC) value of dry samples varied between 1002.53–1238.59 mg GAE/g DM. USVD treatments exhibited a higher total phenolic content (TPC) value than all other drying methods. DPPH and CUPRAC values varied between 15.70–29.90 mg TE/g DM and 40.98–226.09 mg TE/g DM, respectively. The total color change (ΔE) value ranged between 4.59 and 23.93 and HAD dried samples showed the highest ΔE of all samples. The results of the phenolic profile were consistent with TPC analysis. HAD caused higher phenolic compound degradation than VD, USVD, and FD. The results of this study showed that different drying techniques significantly affected the drying rate and retention of bioactive components of the goji berry fruit, and the USVD and VD methods could be used as an alternative to the HAD method. This study concluded that USVD and FD could be considered as suitable drying methods and could be used as alternatives to HAD in the drying of goji berries.

Combined Heat and Mass Transfer Associated with Kinetics Models for Analyzing Convective Stepwise Drying of Carrot Cubes

Stepwise drying is an effective technique that promotes energy saving without additional capital cost. The stepwise drying mode was investigated for energy consumption and dried product qualities using a coupled heat and mass transfer model associated with kinetics equations of volume shrinkage and degradation of β-carotene in carrot cubes. Simulations were performed using a finite element method with extension of a chemical species transport. Validation experiments were carried out under constant drying modes at 60 °C, 70 °C and 80 °C using a lab-scale convective hot air dryer. The verified models were subsequently employed to investigate the effects of two step-up drying modes (60 to 70 °C and 60 to -80 °C). The optimal drying condition was determined using the synthetic evaluation index (SI) with criteria of high specific moisture evaporation rate (SMER), low shrinkage ratio and β-carotene degradation. Simulated results showed comparable agreement with experimental data of moisture content, shrinkage ratio and β-carotene ratio. Step-up drying of 60 to 70 °C gave the highest SMER of 0.50 × 10^-3 kg of water evaporated per kWh, while the operation at constant temperature of 80 °C gave the lowest value of 0.19 × 10^-3 kg of water evaporated per kWh. Model-predicted results showed less shrinkage of carrot cubes, but higher degradation of β-carotene under step-up drying compared to single-stage drying under temperature of 60 °C. Based on the highest SI value (0.36), carrot cubes were optimally dried under step-up mode of 60 to 70 °C.