Study on Optimization of Hot Air Drying Process and Nutrient Quality Model of Rice Driven by Accumulated Temperature
Year: 2024
Type: article
Abstract: The study undertook a thin-layer hot air-drying experiment on rice, and an orthogonal testing approach was used to explore the drying characteristics under diverse conditions of hot air temperature, humidity, initial moisture content, airflow velocity, and tempering ratio. It compared the applicability of nine different accumulated temperature-nutritional index (protein, fat, starch) mathematical models in the hot air drying process of rice. The results showed that the experimental scheme was designed using Central- Composite design, and the parameter combination was optimized through a regression model to hot air temperature hot air temperature (T) at 38.5 ℃, humidity (RH) at 48.086%, initial moisture content (MC) at 19.82%, airflow velocity (V) at 0.70 m/s, and tempering ratio (TR) at 1.45. The relative error between the experimental results and the optimized results was 3.65%, and the relative error of the accumulated temperature-nutritional quality model was 5.92%. Through mathematical model fitting, it was found that the accumulated temperature-protein content was best fitted by the modified Page II model, while the accumulated temperature-fat content was best fitted by the Thompson equation. The accumulated temperature-amylose content was best fitted by the Midilli equation, and the accumulated temperature- amylopectin content was best fitted by the Weibull II equation. All models were highly significant. The optimized drying process maintained nutritional quality and improved drying efficiency. This study could provide a new approach for in-depth exploration of the mechanism of rice quality changes and lay a foundation for the subsequent development of intelligent control systems.
Authors: LI Jin-quan, YIN Jun, JIN Yi, YI Xiao-kang, ZHANG Zhong-jie
Language: English
Topic: Food Drying and Modeling
Subfield: Food Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W6962783246
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