Parameter Optimization of Black Tea Fermentation Machine Based on RSM and BP-AdaBoost-GA

Research output: Contribution to journalJournal articleResearchpeer-review

  • Chunwang Dong
  • Jiewen Zhao
  • Zhu, Hongkai
  • Haibo Yuan
  • Yang Ye
  • Quansheng Chen

Fermentation is the key procedure in processing of congou black tea, which directly decides the quality and flavor of tea products. Fermentation experiments were conducted on a novel drum-type fermentation machine as the platform, the performance parameters of fermentation machine were clarified. Methodologically, with dimensionless comprehensive scores as a measure of fermentation quality, response surface methodology (RSM) and back-propagation adaptive boosting based genetic algorithm (BP-AdaBoost-GA) were used separately to optimize three parameters (fermentation temperature x1, fermentation time x2, rotational interval x3) that affect fermentation quality. Also the optimizing effects of RSM and BP-AdaBoost-GA were compared. Results showed that the importance degrees of the three parameters ranked as x1>x3>x2. With RSM at x1=25℃, x2=150 min and x3=20 min, the predicted and actual values of comprehensive scores were 0.863 and 0.856, respectively, showing relative error of 0.8%. With BP-AdaBoost-GA at x1=27℃, x2=170 min and x3=25 min, the predicted and actual values of comprehensive scores were 0.871 and 0.868, respectively, showing relative error of 0.3%. When the BP-AdaBoost had seven nodes in the hidden layer and a prediction error threshold of 0.25, its determination coefficient was greater than that of RSM (0.994 vs 0.988), and it had lower root mean square error of prediction (RMSEP) of 0.017 and residual predictive deviation (RPD) equaled to 18.456. Both RSM and BP-AdaBoost-GA were feasible for optimization of fermentation parameters. However, the fitting ability of RSM was limited because it was based on quadratic polynomial regression, while the fitting ability over experimental data was limited. The algorithm combining improved neural network and GA had higher global extremum prediction ability and higher accuracy. Thus, it can be concluded that even though RSM was the most widely used method for fermentation parameter optimization, BP-AdaBoost-GA methodology may present a better alternative. In the meantime, the rotation function had both advantages and disadvantages on the fermentation quality of black tea, moderate rotation and mixing material can enhance the quality of black tea and shorten the fermentation time.

Original languageEnglish
JournalNongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Issue number5
Pages (from-to)335-342
Number of pages8
Publication statusPublished - 2017

    Research areas

  • AdaBoost algorithm, Black tea fermentation, Genetic algorithm, Parameter optimization

ID: 197096845