Modeling the rehydration kinetics of dried rice noodles based on Peleg and Arrhenius equations and qualities maintenance

Rehydration of dried rice noodles (DRN) is a time-consuming process and has a significant impact on the quality of the rice noodles. In this study, the relationship between rehydration conditions and moisture content was explored through the rehydration kinetics analysis, and the effect of rehydration temperature on the quality of DRN was investigated, and preservation methods were optimized for preservative to improve storage quality on rehydrated rice noodles (RRN). The results demonstrated that the rehydration process of DRN could be described using mathematical equation, and the fitted and predictable equations were obtained. RRN remained better in terms of cooking quality, texture, and the rate of rupture after optimizing rehydration conditions. When the concentrations of lactic acid, ε-polylysine, and sodium dehydroacetate were 25 ppm, 63 ppm, and 200 ppm, respectively, the initial aerobic plate count of RRN was the lowest, and the shelf-life was extended to 3 days. Though the E-nose and Loadings analysis, nitrogen oxides, and aromatic hydrocarbons, played an important role in differentiating the odor of RRN. Understanding the rehydration process helped to maintain the quality of RRN, and the optimization of the preservative is expected to be applied to increase the economic benefits in rice products-related enterprises. • Peleg equation used to model the rehydration process of dried rice noodles. • Optimization of rehydration process of dried rice noodles. • Optimized preservation combination inhibits microbial growth. • Quality maintenance and shelf-life extension of rehydrated rice noodles.

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Publication Details

Journal
LWT
Published
2025-04-14
DOI
https://doi.org/10.1016/j.lwt.2025.117783
Primary Topic
Freezing and Crystallization Processes
Type
article
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article

Modeling the rehydration kinetics of dried rice noodles based on Peleg and Arrhenius equations and qualities maintenance

Sijie Zhang, Cuiping Yi, Nannan Qiang, Zuyin Li
LWT
Freezing and Crystallization Processes
article

Modeling the rehydration kinetics of dried rice noodles based on Peleg and Arrhenius equations and qualities maintenance

Sijie Zhang, Cuiping Yi, Nannan Qiang, Zuyin Li
article en

Abstract

Rehydration of dried rice noodles (DRN) is a time-consuming process and has a significant impact on the quality of the rice noodles. In this study, the relationship between rehydration conditions and moisture content was explored through the rehydration kinetics analysis, and the effect of rehydration temperature on the quality of DRN was investigated, and preservation methods were optimized for preservative to improve storage quality on rehydrated rice noodles (RRN). The results demonstrated that the rehydration process of DRN could be described using mathematical equation, and the fitted and predictable equations were obtained. RRN remained better in terms of cooking quality, texture, and the rate of rupture after optimizing rehydration conditions. When the concentrations of lactic acid, ε-polylysine, and sodium dehydroacetate were 25 ppm, 63 ppm, and 200 ppm, respectively, the initial aerobic plate count of RRN was the lowest, and the shelf-life was extended to 3 days. Though the E-nose and Loadings analysis, nitrogen oxides, and aromatic hydrocarbons, played an important role in differentiating the odor of RRN. Understanding the rehydration process helped to maintain the quality of RRN, and the optimization of the preservative is expected to be applied to increase the economic benefits in rice products-related enterprises. • Peleg equation used to model the rehydration process of dried rice noodles. • Optimization of rehydration process of dried rice noodles. • Optimized preservation combination inhibits microbial growth. • Quality maintenance and shelf-life extension of rehydrated rice noodles.

LWTVol. 223
Changsha University of Science and Technology (CN), Hunan Women'S University (CN)
Zero hunger
Openalex Percentile: Top 92%
Freezing and Crystallization Processes
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