Modeling of Furfural and 5-Hydroxymethylfurfural Content of Fermented Lotus Root: Artificial Neural Networks and a Genetic Algorithm Approach

Abstract

Abstract The aim of this study was to investigate the effect of different pretreatment and reducing sugar content on furfural (F) and 5-hydroxymethylfurfural (HMF) contents of fermented lotus root by vinegar. The lotus root samples were fermented using vinegar for 15 days, at different solution concentrations and temperatures. The processing conditions were considered as inputs of neural network to predict the F and HMF contents of lotus root. Genetic algorithm was applied to optimize the structure and learning parameters of ANN. The developed genetic algorithm-artificial neural network (GA-ANN) which included 23 and 17 neurons in the first and second hidden layers, respectively, gives the lowest mean squared error (MSE). The correlation coefficient of ANN was compared with multiple linear regression-based models. The GA-ANN model was found to be a more accurate prediction method for the F and HMF contents of fermented lotus root than linear regression-based models. In addition, sensitivity analysis and Pearson’s correlation coefficient were also analyzed to find out the relation between input and output variables.