Online monitoring and control of the drying processes are necessary to maintain the final products’ quality attributes, especially when a microwave system is used to facilitate the drying process. Machine learning techniques could be a suitable and very accurate approach for modelling the drying process. Two machine learning techniques including Support Vector Regression (SVR) and Artificial Neural Network (ANN) were employed to predict lentil seeds’ temperature and moisture ratio during drying in a microwave fluidised bed dryer with inputs of microwave power (0–500 W), fluidising air temperature (50 °C and 60 °C) and drying time. Mean squared error (MSE) and the coefficient of determination ($R^2$) were used to evaluate the performance of the models. One hidden layer and 10 hidden neurons, the logistic sigmoid transfer function for the hidden layer, and a linear function for the output layer were determined to be the best structure for the ANN. Weights and biases were found by training the network with Bayesian Regularisation (trainbr) and the optimum MSE and $R^2$ of the test set were $3.8\times {10}^{-5}$ and 0.999 respectively. Optimised SVR could also provide a good model to predict MR and temperature (overall MSE = 1.96 $\times {10}^{-4}$ and$R^2$ = 0.995), although ANN provided relatively more accuracy for the temperature data. Therefore, ANN could be utilised as an accurate tool for the prediction of MR and temperature of lentil seeds during microwave fluidised bed drying.

在线监测和控制干燥过程对于维持最终产品的质量属性是必要的，尤其是在使用微波系统促进干燥过程时。机器学习技术可能是用于模拟干燥过程的合适且非常准确的方法。两种机器学习技术，包括支持向量回归（SVR）和人工神经网络（ANN），被用来预测在微波流化床干燥机中干燥过程中扁豆种子的温度和水分比，输入微波功率（0–500 W），流化空气温度（50°C和60°C）和干燥时间。均方误差（MSE）和确定系数（${\mathrm{[R}}^2$）用于评估模型的性能。确定一个隐藏层和10个隐藏神经元，用于隐藏层的逻辑S型传递函数和用于输出层的线性函数是ANN的最佳结构。通过使用贝叶斯正则化（trainbr）和最佳MSE训练网络来发现权重和偏差。${\mathrm{[R}}^2$ 测试集的 $3.8\times {10}^{--5}$和0.999。优化的SVR还可以提供一个预测MR和温度的良好模型（总体MSE = 1.96$\times {10}^{--4}$ 和${\mathrm{[R}}^2$ = 0.995），尽管人工神经网络为温度数据提供了相对更高的准确性。因此，人工神经网络可以用作预测微波流化床干燥过程中小扁豆种子的MR和温度的准确工具。