Effects of Hot-Air Drying Conditions on Quality Attributes of Meat and Shell of Dried Shrimp
1
Issued Date
2025-12-01
Resource Type
eISSN
23048158
Scopus ID
2-s2.0-105024610601
Journal Title
Foods
Volume
14
Issue
23
Rights Holder(s)
SCOPUS
Bibliographic Citation
Foods Vol.14 No.23 (2025)
Suggested Citation
Lin Z., Zhang Z., Zheng Z., Hou R., Zhang Y., Zheng B., Sriboonvorakul N., Hu J. Effects of Hot-Air Drying Conditions on Quality Attributes of Meat and Shell of Dried Shrimp. Foods Vol.14 No.23 (2025). doi:10.3390/foods14234041 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/113603
Title
Effects of Hot-Air Drying Conditions on Quality Attributes of Meat and Shell of Dried Shrimp
Corresponding Author(s)
Other Contributor(s)
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 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 R<sup>2</sup> = 0.38 and a validation accuracy of R<sup>2</sup> = 0.32, whereas the optimized LM-ANN model exhibited markedly superior predictive capability (R<sup>2</sup><inf>Training</inf> = 0.99, R<sup>2</sup><inf>Validation</inf> = 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.