Abstract
Objective:This study aimed to investigate the drying characteristic of Chaenomeles sinensis by using vacuum pulsed drying technology and establish BP neural network model.Methods:The single factor experiment of drying temperature (50, 60, 70 ℃), constant atmosphere time (2, 4, 8 min) and vacuum time (5, 10, 15, 20 min) on drying time, rehydration ratio, VC and general flavone content as well as microstructure of Chaenomeles sinensis during vacuum pulsed drying technology were investigated.Results:All the drying temperature, constant atmosphere time and vacuum time had significant influence on drying time(P＜0.05). The moisture effective diffusion coefficient (Deff) ranged from 6.044 8×10-10 to 12.008 6×10-10 m2/s in different drying conditions and increased with drying temperature increasing. BP neural network mode consisted of input layer, hidden layer and output layer. The input layer included four neurons named drying time, drying temperature, constant atmosphere time and vacuum time. The hidden layer included seven neurons and the output layer included one neuron named moisture content. The maximum error between simulated and experimental values was 4.77%. Rehydration ratio decreased as drying temperature increased and increased first and then decreased with the extension of atmospheric pressure time and vacuum time. VC and general flavone content increased first and then decreased with the increasing of drying temperature, atmospheric pressure time and vacuum time. The microstructure indicated that when drying temperature was70 ℃, the material surface crusted due to a large amount of water loss. In this case, the water migration channel collapsed and blocked. When drying temperature was 50 ℃, the surface of the material appeared a cellular porous structure, which was conducive to water diffusion and migration.Conclusion:The optimal drying process was drying at temperature 60 ℃, with atmospheric pressure for 5 min and vacuum for 15 min. In this circumstance, the drying time, rehydration ratio, VC and general flavone content were 12.1 h,6.28±0.05, (71.26±0.74)×10-2 mg/g and (19.27±0.33) mg/g, respectively. BP neural network model can describe the vacuum pulsed drying process of Chaenomeles sinensis.
Publication Date
7-7-2022
First Page
147
Last Page
153
Recommended Citation
Hao-yu, JU; Shi-hao, ZHAO; Hai-yan, ZHAO; Wei-peng, ZHANG; and Hong-wei, XIAO
(2022)
"Drying characteristics of Chaenomeles sinensis with vacuum pulsed drying technology based on BP neural network mode,"
Food and Machinery: Vol. 38:
Iss.
3, Article 24.
Available at:
https://www.ifoodmm.cn/journal/vol38/iss3/24
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