Color prediction of mushroom slices during drying using Bayesian extreme learning machine

Abstract

Color is an important appearance attribute of fruits and vegetables during drying processing, as it influences consumer's preference and acceptability. Establishing color change kinetics model is an effective way for better understanding the quality changes and optimization of drying process. However, it is difficult to quickly and accurately predict color change kinetics during drying as it is highly nonlinear, complex, dynamic, and multivariable. To alleviate this problem, a new model based on extreme learning machine integrated Bayesian methods (BELM) has been developed for the prediction of color changes of mushroom slices during drying process. The effects of drying temperature (55, 60, 65, 70, and 75 °C) and air velocity (3, 6, 9, and 12 m/s) on color change kinetics of mushroom slices during hot air impingement drying were firstly explored and the experimental results indicated that both drying temperature and air velocity significantly affected the color attributes. Then, to validate the robustness and effectiveness of BELM, the basic extreme learning machine (ELM) and traditional back-propagation neural network (BPNN) models have also been employed to predict the color quality. In terms of prediction accuracy and execution time, BELM could achieve least similar or even better performance than ELM and BPNN. It overcame the overfitting problems of ELM. The test results of optimal BELM model by two new cases revealed that the lowest R2 and highest RMSE of BELM model were 0.9725 and 0.0563, respectively. The absolute values of relative errors between the actual and predicted values were lower than 8.5%.