Research on the Vegetable Shrinkage During Drying and Characterization and Control Based on LF-NMR

收缩率 含水量 化学 水分 体积热力学 食品科学 材料科学 复合材料 热力学 物理 工程类 岩土工程
作者
Qing Sun,Min Zhang,Arun S. Mujumdar,Dongxing Yu
出处
期刊:Food and Bioprocess Technology [Springer Nature]
卷期号:15 (12): 2776-2788 被引量:5
标识
DOI:10.1007/s11947-022-02917-x
摘要

Shrinkage is a common phenomenon during the drying of fruits and vegetables. The research aimed to study the mechanism of drying shrinkage and investigate the potential use of low-field nuclear magnetic resonance (LF-NMR) for online monitoring changes in shrinkage. The effects of drying parameters (temperature, power, and vacuum) on shrinkage of three types of materials banana (fruit), carrot (vegetable), and Pleurotus eryngii (an edible fungus) were studied in the different drying processes of hot air drying (HAD), microwave vacuum drying (MVD), infrared drying (IRD), and infrared freeze-drying (IFD). During drying, material shrinkage mainly occurred in the early and middle drying stages with different characteristics of retention volume and shrinkage equilibrium point of moisture content. The drying shrinkage was significantly related to the change of MC in vacuolar compartment (p < 0.05). Reducing the drying time from drying beginning to the LF-NMR A23/A22(1), i.e., when the water content between vacuolar compartment and cytoplasm was equal, was beneficial for reducing shrinkage, and the volume retention rate increased by 39.13%. The shrinkage model of BP-ANN based on LF-NMR had a high prediction accuracy of shrinkage more than 95% and was excellent with the R2 of 0.9989 and RMSE of 0.0087. The shrinkage control strategy based on LF-NMR provided a reference for the development of artificial intelligence drying equipment.
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