In this study, genetic algorithm–artificial neural network (GA‐ANN) and adaptive neuro‐fuzzy inference system (ANFIS) models were used for the prediction of drying time (DT) and moisture ratio (MR) of basil seed mucilage (BSM) in an infrared (IR) dryer. The GA‐ANN and ANFIS were fed with three inputs of IR radiation power, the distance of mucilage from lamp surface, and mucilage thickness for the prediction of average DT. Also, to predict the MR, these models were fed with four inputs of IR power, lamp distance, mucilage thickness, and treatment time. The developed GA–ANN, which included eight hidden neurons, could predict the DT of BSM with a correlation coefficient (r) of 0.97. Also, the GA–ANN model with 10 neurons in 1 hidden layer, could predict the MR with a high r value (r = 0.99). The calculated r values for the prediction of DT and MR using the ANFIS‐based subtractive clustering algorithm were 0.96 and 0.99, respectively. Sensitivity analysis results showed that mucilage thickness and treatment time were the most sensitive factor for the prediction of DT and MR of BSM drying, respectively.

Advantages of infrared radiation over convective heating include high heat transfer coefficients, short process times, and low energy costs. Dried seeds mucilage are hydrophilic molecules and they can used as functional ingredients in food products formulation for improving food viscosity and consistency, and controlling the microstructure, texture, flavor, and shelf life. Both GA‐ANN and ANFIS models predictions agreed well with testing data sets and they could be useful for understanding and controlling the factors affecting on drying kinetics of BSM in an IR dryer.