Optimization of the Vacuum Microwave Drying of Tilapia Fillets Using Response Surface Analysis

This study looked at how vacuum microwave drying (VMD) affects the quality of tilapia fillets (<i>Oreochromis</i> spp.). It focused on the impact of fillet thickness, microwave power, and vacuum pressure on key quality parameters, such as water activity (Aw), texture, rehydration rate, and whiteness. A series of experiments were conducted with varying fillet thickness (3-7 mm), microwave power (132-396 W), and vacuum pressure (0.03-0.07 MPa) using a Box-Behnken design to optimize drying conditions. The findings revealed that fillets with a thickness of 3 mm had the lowest Aw and the highest hardness, while 7 mm thick fillets had the best rehydration rate, elasticity, and whiteness. Additionally, increasing microwave power caused a gradual decrease in Aw and whiteness, while elasticity, hardness, and the rehydration rate initially increased and then decreased. As vacuum pressure increased, Aw decreased, and both whiteness and elasticity improved. The optimal drying conditions for tilapia fillets were identified as 7 mm thickness, 330 W microwave power, and 0.06 MPa vacuum pressure. Under these conditions, the dried fillets achieved a comprehensive quality score of 93.94. The regression model developed for optimization showed strong predictive performance, with a minimal deviation of only 1.45% from the experimental results, indicating its reliability for predicting drying effects.

Authors

Institutions

Publication Details

Journal
Foods
Published
2025-03-04
DOI
https://doi.org/10.3390/foods14050873
Citations
5
Primary Topic
Food Drying and Modeling
Type
article
Field-Weighted Citation Impact
5.81
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimization of the Vacuum Microwave Drying of Tilapia Fillets Using Response Surface Analysis

Biao Ye, Qing Xu, Jianwen Ruan, Guang Xue et al.
5 citations
Foods
Food Drying and Modeling
5.81
article

Optimization of the Vacuum Microwave Drying of Tilapia Fillets Using Response Surface Analysis

Biao Ye, Qing Xu, Jianwen Ruan, Guang Xue, Min Li, Yan Liu
article en
5 citations

Abstract

This study looked at how vacuum microwave drying (VMD) affects the quality of tilapia fillets (<i>Oreochromis</i> spp.). It focused on the impact of fillet thickness, microwave power, and vacuum pressure on key quality parameters, such as water activity (Aw), texture, rehydration rate, and whiteness. A series of experiments were conducted with varying fillet thickness (3-7 mm), microwave power (132-396 W), and vacuum pressure (0.03-0.07 MPa) using a Box-Behnken design to optimize drying conditions. The findings revealed that fillets with a thickness of 3 mm had the lowest Aw and the highest hardness, while 7 mm thick fillets had the best rehydration rate, elasticity, and whiteness. Additionally, increasing microwave power caused a gradual decrease in Aw and whiteness, while elasticity, hardness, and the rehydration rate initially increased and then decreased. As vacuum pressure increased, Aw decreased, and both whiteness and elasticity improved. The optimal drying conditions for tilapia fillets were identified as 7 mm thickness, 330 W microwave power, and 0.06 MPa vacuum pressure. Under these conditions, the dried fillets achieved a comprehensive quality score of 93.94. The regression model developed for optimization showed strong predictive performance, with a minimal deviation of only 1.45% from the experimental results, indicating its reliability for predicting drying effects.

FoodsVol. 14(5)
Guangdong Ocean University (CN)
Openalex Percentile: Top 5%
Food Drying and Modeling
5.81
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.