Improving black tea quality through optimization of withering conditions using artificial neural network and genetic algorithm

摘要

Polyphenol oxidase and peroxidase activity and moisture loss have been used for optimization of withering parameters in black tea production. Leaves of TV25 clone were withered at different air temperature (25, 30, 35°C) and relative humidity (RH) (60, 75, 85%), with constant air flow rate. With higher air temperature, Polyphenol oxidase specific activity decreased, whereas peroxidase specific activity was enhanced. A multilayered feed forward artificial neural network, with single hidden layer, was trained and validated using experimental data based on mean square error and coefficient of determination (R~2). Optimized withering parameters for both crushtear- curl (CTC) and orthodox tea production were obtained using Genetic Algorithms to maximize enzyme activity and bring down moisture content within desired range. A penalty function based on distance from feasible region was incorporated in Genetic Algorithm. Optimized parameters are: CTC - temperature: 29.13°C, RH: 79.58%, duration: 11.41 hr; Orthodox - temperature: 33.04°C, RH: 77.27%, duration: 10.60 hr.