关键词: 果蔬干燥, 神经网络, 干燥动力学模型, 误差反向传播算法, 含水率预测

Abstract:  Fruit and vegetable drying is an important part in the processing of agricultural products, and the construction of accurate drying kinetics models has become a key direction in the drying field. In this paper, the application status of artificial neural network in fruit and vegetable drying was reviewed, the existing problems were analyzed and the prospects was made. The scenes of the artificial neural network in the drying process were classified into four parts such as water content prediction, quality detection, process optimization and control system, the application types and development innovations of each part were summarized. Further comparison was made between traditional drying models and artificial neural network models. Finally, the application scenarios of hybrid neural networks were introduced.  It is found that the artificial neural networks  is more accurate than the traditional drying models, and the hybrid neural networks combined with expert systems, fuzzy logic and other theories can provide accurate predictions. As a novel and efficient modeling technology, it can be widely applied in the optimization, control, automation and other fields of fruit and vegetable processing. The most widely used among them is the GA-BP neural network combined with genetic algorithms, where BP is responsible for prediction and GA is responsible for optimization. In such algorithms, not only can the results be accurately predicted but also the process can be optimized. This model is more suitable for fruit and vegetable drying and has broader development space in the future, with the hope that these discussions and analyses have reference significance for the field of fruit and vegetable drying.

Key words:  , fruit and vegetable drying, neural network, drying kinetics model, error back propagation algorithm, moisture content prediction