Artificial neural network modeling and genetic algorithm optimization of process parameters in fluidized bed drying of green tea leaves. (7th June 2019)

• Record Type: Journal Article
• Authors: Kalathingal, Muhammed Sirajul Huda; Basak, Suradeep; Mitra, Jayeeta
• Is Part Of: Journal of food process engineering. Volume 43:Number 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-06-07
• Subjects: Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; 664.005
• DOI: 10.1111/jfpe.13128
• Languages: English
• ISSNs: 0145-8876

Abstract

Abstract: The present study involved integration of artificial neural network (ANN) with genetic algorithm (GA) for predicting the optimized process parameters required for fluidized bed drying of green tea leaves. It had a layer each for input and output with linear activation function and two hidden layers with a sigmoid function. The feed forward back propagation method was used to train the developed model. The input parameters used by ANN for generalizing the drying process were temperature (50–80°C) and air flow velocity (7–9.5 m/s), and the output parameters were drying time, total color difference (TCD), and total phenolic content (TPC). The weights and bias values of trained ANN were used by GA to estimate the fitness function, which maximizes the TPC and minimizes the drying time and TCD. The optimum process condition of independent variables (80°C and 9 m/s) obtained from the hybrid ANN/GA was validated, and agreeable relationship between actual and predicted values with relative standard deviation ( SD ) of 5.7, 0.46, and 0.22 was found for drying time, TCD and TPC of dried leaves, respectively. Hence, under this optimal drying condition, best quality green tea can be obtained within the limits defined. Practical applications: Drying is a popular unit operation being preferred to prolong shelf life of cash crops. Air drying is predominantly used at any given scale due to its economic feasibility. However, the quality parameters get compromised during the process of drying. Hence, an optimal drying condition is necessary to have dried product with intact nutritional and functional activity, alongside consumer acceptability. This was attempted using ANN and GA. Artificial neural network and genetic algorithm can estimate the optimum fluidized bed drying condition to have intact physical and chemical properties of green tea leaves. The results indicated that the developed ANN/GA drying model can efficiently estimate the values of quality parameters of dried green tea leaves, and also identify the optimal drying conditions for any new data.