Novel Drying Technologies in Food Processing: Maximizing Food Quality and Safety Preservation

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Engineering and Technology".

Deadline for manuscript submissions: 10 April 2026 | Viewed by 989

Special Issue Editors


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Guest Editor
Department of Sustainable Technologies, Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague, Prague, Czech Republic
Interests: food processing technology; solar drying; food security
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
Department of Sustainable Technologies, Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague, Suchdol, Prague, Czech Republic
Interests: coffee and grain post-harvesting technologies; machine design and chemical processes; organic waste management; computational fluid dynamics; drying; mechanical design

Special Issue Information

Dear Colleagues, 

Drying is a crucial aspect of food processing; however, conventional methods often compromise the essential qualities of the food, such as its nutritional content, sensory attributes, and microbial stability. This Special Issue aims to showcase innovative research and technological improvements that address these challenges, providing solutions that meet both consumer expectations and industry needs. We will focus on new drying methods that enhance efficiency, environmental friendliness, and food quality. Additionally, contributions should explore smart monitoring systems, modeling, and process optimization, illustrating how digital tools and intelligent control strategies can enhance product quality and ensure safety. By connecting fundamental science with practical applications, this Special Issue will provide a platform for innovative drying technologies that not only preserve food quality and safety, but also promote sustainability, energy efficiency, and environmental responsibility across the food sector.

Prof. Dr. Jan Banout
Guest Editor

Dr. Eduardo Duque Dussán
Guest Editor Assistant

Manuscript Submission Information

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Keywords

  • innovative drying technologies
  • process optimization
  • food quality
  • smart monitoring
  • energy efficiency
  • microbial safety
  • energy efficiency

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Published Papers (1 paper)

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Research

26 pages, 7060 KB  
Article
Effects of Hot-Air Drying Conditions on Quality Attributes of Meat and Shell of Dried Shrimp
by Zhongjing Lin, Zhaorong Zhang, Zhipeng Zheng, Ruoting Hou, Yi Zhang, Baodong Zheng, Natthida Sriboonvorakul and Jiamiao Hu
Foods 2025, 14(23), 4041; https://doi.org/10.3390/foods14234041 - 25 Nov 2025
Viewed by 488
Abstract
Maintaining desirable texture, color, and flavor during hot-air drying is crucial for improving the commercial value of dried shrimp. This study aims to address the limitations of previous research on hot-air drying of shrimp, which focused solely on the meat. The objective is [...] Read more.
Maintaining desirable texture, color, and flavor during hot-air drying is crucial for improving the commercial value of dried shrimp. This study aims to address the limitations of previous research on hot-air drying of shrimp, which focused solely on the meat. The objective is to simultaneously investigate the dual effects of hot-air drying conditions on the textural and physicochemical properties of both the shrimp shell and meat. This provides a theoretical foundation for preserving the optimal texture, color, and flavor of dried shrimp snack products. After drying and separation, the textural and physicochemical properties of the two components were comprehensively evaluated, including hardness, crispness, chewiness, springiness, color (L*, a*, b*), rehydration rate, sensory attributes, and odor characteristics. Furthermore, to elucidate the complex interrelationships among these variables, two predictive models were established: a Partial Least Squares Regression (PLSR) model and an Artificial Neural Network (ANN) model optimized using the Levenberg–Marquardt algorithm. The PLSR model achieved a calibration accuracy of R2 = 0.38 and a validation accuracy of R2 = 0.32, whereas the optimized LM-ANN model exhibited markedly superior predictive capability (R2Training = 0.99, R2Validation = 0.98), effectively capturing nonlinear associations between drying parameters and quality attributes of both meat and shell. Finally, a user-oriented prediction module was established based on the optimized ANN model, allowing flexible input of variables and prediction of quality outcomes. This integrated framework may provide a novel approach for modeling and optimizing the hot-air drying process of shrimp, offering practical guidance for quality control and texture customization of dried shrimp products. Full article
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