摘要
为研究猕猴桃片基于旋转托盘式微波真空干燥特性及品质优化工艺,探讨了不同功率密度(3.33、6.25、9.58 W/g)、干燥温度(40、45、50、55℃)、腔室压力(5、10、15、20 kPa)及切片厚度(3、6、9、12 mm)对猕猴桃片干燥特性的影响,比较了旋转托盘式相对于传统水平转盘式微波真空装备的优势,并研究了不同模型拟合预测猕猴桃片水分比变化的准确性与适用性。结果表明:随着功率密度的降低和切片厚度的增大,物料干燥过程中存在更为明显的恒速段;当干基含水率降至1.3 g/g左右时,干燥过程转入降速阶段。综合考虑感官评价及干燥时间可得,功率密度6.25 W/g、干燥温度45℃、腔室压力5 kPa、切片厚度6 mm干燥条件下猕猴桃片干制品品质最佳。旋转托盘式微波真空干燥可大幅提升物料装载量,干燥均匀性较传统方式提升了16%,干燥平均能耗仅为后者的71.2%。通过模型预测值与试验实测值的比较,BP神经网络模型决定系数R2可达0.996,相比Weibull模型能更好地预测猕猴桃干燥过程的水分比变化规律。
To investigate the rotating tray microwave vacuum drying characteristics and quality attributes of kiwifruit slices,the effects of different microwave power density(3.33 W/g,6.25 W/g and 9.58 W/g),drying temperature(40℃,45℃,50℃and 55℃),vacuum holding pressure(20 kPa,15 kPa,10 kPa and 5 kPa)and slice thickness(3 mm,6 mm,9 mm and 12 mm)on the drying kinetics of kiwifruit slices were analyzed.Weibull function and neural network model were also compared to choose the appropriate fitting model for kiwifruit slices drying.The results suggested that rotary tray microwave vacuum drying can greatly increase the material loading capacity.Meanwhile,the drying uniformity was increased by 16%compared with the traditional vacuum microwave drying method,and the average energy consumption was only 71.2%of the latter,which ensured excellent drying quality under the mass processing of kiwifruit slices.With the decrease of power density and the increase of slice thickness,there was a more obvious constant speed section in the process of material drying.When the moisture content of kiwifruit slices(dry base)dropped to about 1.3 g/g,the drying process changed to the speed reduction stage.The drying rate was added with the increase of microwave power density,drying temperature and vacuum holding pressure,as well as the decrease in slice thickness.Properly reducing the power density and drying temperature,increasing the vacuum degree and thickness can improve the quality attributes of dry products.The optimization parameter was confirmed at the power density of 6.25 W/g,drying temperature of 45℃,vacuum holding pressure of 5 kPa and the thickness of 6 mm.By analyzing the predicted values of two models,the determination coefficient(R2)of neural network model can reach up to 0.996 and the root mean square error(RMSE)was 0.0216,which had better simulation precision than that of Weibull model and it can predict the water transfer law of kiwifruit accurately during microwave vacuum drying.The research reuslt provided a scientific r
作者
张付杰
辛立东
代建武
李丽霞
周杰
ZHANG Fujie;XIN Lidong;DAI Jianwu;LI Lixia;ZHOU Jie(Faculty of Agriculture and Food,Kunming University of Science and Technology,Kunming 650500,China;College of Mechanical and Electrical Engineering,Sichuan Agricultural University,Ya’an 625014,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2020年第S01期501-508,共8页
Transactions of the Chinese Society for Agricultural Machinery
基金
云南省重大科技专项计划项目(2018ZF004)
云南省科技人才和平台计划项目(2019IC001)
四川省科技创新人才计划项目(2020JDRC0066)。
关键词
猕猴桃片
微波真空干燥
干燥特性
水分比预测
BP神经网络
kiwifruit slices
microwave vacuum drying
drying characteristics
moisture ratio prediction
BP neural network
