Editorial: Advances in Drying Kinetics and Quality Control in Food Processing, 2nd Edition

1. Introduction

The second edition of the Special Issue, “Drying Kinetics and Quality Control in Food Processing”, serves as a comprehensive platform for discussing the latest innovations in dehydration technologies. Drying is not merely a method of preservation but a sophisticated thermal process that significantly influences the structural, nutritional, and sensorial attributes of food products. As the global food industry moves toward sustainability and precision manufacturing, understanding the interplay between drying kinetics and final product quality has never been more critical [1,2]. This collection of 10 papers explores diverse matrices—from fruits and root vegetables to aquatic feed and industrial systems—utilizing advanced modeling, non-thermal pre-treatments, and artificial intelligence.

2. Smart Optimization and Digital Transformation

A standout theme in this edition is the shift toward “Food Industry 4.0,” characterized by the integration of advanced computational tools [3,4]. Jia et al. [5] demonstrated the power of deep learning by employing Long Short-Term Memory (LSTM) networks to predict the moisture and color kinetics of apple slices, offering a robust alternative to traditional empirical models. This approach aligns with the rapidly growing body of literature demonstrating LSTM’s effectiveness for time-series prediction in dynamic food-processing environments: Zhang et al. [6] showed that an LSTM-based deep learning architecture for complex grain drying processes successfully achieved real-time prediction of moisture content with high accuracy. In a parallel study on process monitoring, Guo et al. [7] integrated convolutional neural networks with attention-based LSTM models for predicting state parameters in industrial thermal treatments, obtaining reliable R² values and underscoring the potential of hybrid deep-learning frameworks.

Similarly, Oyinloye et al. [8] utilized K-means clustering and morphological grading to optimize steaming conditions for Platycodon grandiflorus, showcasing how machine vision can standardize quality in traditional herbal processing. Furthermore, the work of Metzner and Dacanal [9] on monitoring fluidized beds via acoustic emissions and neural networks proves that real-time, non-invasive sensing is becoming essential for industrial drying consistency. These computational strategies exemplify the broader trajectory of Food Industry 4.0, in which AI-driven monitoring and IoT-connected sensing enable precision drying [3,4].

3. Innovative Pre-Treatments and Intensification Technologies

Enhancing mass transfer while preserving bioactive compounds remains a central challenge. Lin et al. [10] investigated the effects of stabilized sound pressure in multiple-frequency ultrasonic-assisted osmotic dehydration (UAOD) for pineapple slices, finding that precise acoustic control significantly improves efficiency. In a similar vein, Yilmaz et al. [11] explored the synergy between ultrasound pre-treatment and various drying methods for blood oranges, highlighting how non-thermal interventions can drastically reduce drying time and energy consumption.

These findings are consistent with recent evidence: Ahmad et al. [12] reviewed how ultrasound enhances mass transfer rates and product color across various food matrices during dehydration. Complementarily, Wang et al. [13] reported that ultrasonic pre-treatments effectively disrupted internal resistance and modified microstructure, shortening total freeze-drying times for delicate horticultural products. Collectively, these studies reinforce the consensus that non-thermal intensification technologies can reduce processing time by 20–55% while significantly improving bioactive retention [14], making them essential tools for sustainable food drying.

4. Mathematical Modeling and Kinetic Insights

Predictive accuracy is fundamental to scaling drying processes. Kheredine et al. [15] provided a sophisticated mechanistic view using Lattice Boltzmann simulations coupled with Weibull-based kinetics to describe carrot drying. These theoretical frameworks are complemented by the detailed experimental kinetic studies of Uribe et al. [16] on vacuum-dried cauliflower and Rudy et al. [17] on Jerusalem artichoke, both of which emphasize the critical balance between temperature, vacuum pressure, and the retention of antioxidant properties.

The shift toward physics-based modeling is well supported: de Oliveira et al. [18] demonstrated that physics-informed neural networks (PINN) and advanced machine learning models can accurately predict complex moisture diffusion and drying behavior in plant-based biological materials. In a more applied direction, Rojas et al. [19] evaluated the mathematical modeling of vacuum drying for functional vegetables like red cabbage, finding that thin-layer kinetic equations best described the drying curves and that specific bioactive compounds were optimally retained under controlled low-temperature regimes. Together, these contributions highlight the importance of coupling kinetic model selection with targeted quality outcomes.

5. Quality Control and Energy Efficiency in Industrial Applications

Sustainability in food engineering is inextricably linked to energy management and product functionality. Jiménez-Rodríguez et al. [20] analyzed the temperature effects on cocoa drying, specifically focusing on energy consumption and bioactive stability. This finding is reinforced by Pita-Garcia et al. [21], who showed that combining hybrid solar-convective drying setups can drastically reduce conventional energy consumption while preserving premium product quality. For the feed industry, Graff et al. [22] explored how drying parameters influence the physical durability and energy efficiency of extruded aquatic feed. This industrial-scale concern is echoed by Cheng et al. [23], who successfully applied rheological and viscosity modeling to predict extruded aquafeed physical quality and structural stability—a significant step toward quality-assured industrial feed manufacturing.

6. Conclusions and Future Perspectives

The 10 contributions in this Special Issue underline that the future of food drying lies in the convergence of AI-driven prediction, innovative non-thermal intensification, and mechanistic modeling. These studies collectively move the field closer to achieving high-efficiency drying systems that do not compromise the nutritional or sensorial integrity of the food. Looking ahead, the integration of digital twin frameworks and physics-informed machine learning holds particular promise for bridging laboratory-scale insights with industrial-scale process control [3,4,18].