An adaptive fuzzy logic control of green tea fixation process based on image processing technology. (March 2022)

• Record Type: Journal Article
• Authors: Chen, Cheng; Liu, Benying; Song, Feihu; Jiang, Jianjun; Li, Zhenfeng; Song, Chunfang; Li, Jing; Jin, Guangyuan; Wu, Jincheng
• Is Part Of: Biosystems engineering. Volume 215(2022)
• Page Start: 1
• Page End: 20
• Publication Date: 2022-03
• Subjects: Green tea -- Machine vision -- Prediction model -- Adaptive fuzzy logic control; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2021.12.023
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : In postharvest processing of green tea, fixing is the first and the most critical step for forming green tea's specific appearance and fragrance. In this study, green tea sample was firstly fixed at constant pot temperatures. The image information was captured to reflect colour changes. By observing the disappearance of the enzymatic activity, the fixation process was divided into the first and the last stage, where different concerns were emphasised on. After fixation, chemical components including enzymatic activity, tea polyphenols, amino acids and chlorophyll were analysed. The MLP, Grid-Search-SVR and GA-SVR models were constructed based on image information to predict chemical compositions. Results demonstrated GA-SVR could achieve superior performance than Grid-Search-SVR and MLP (1.9742>0.9167>0.7797 for average MSE, 0.3829< 0.5969<0.6557 for average R-Square, and 15.7132>8.8603> 8.5081 for average MAPE at first fixation stage; 0.3343>0.2552>0.2460 for MSE, 0.7393<0.8317<0.8658 for R-Square, and 5.2898 > 3.9612>3.8259 for MAPE at last fixation stage). An adaptive fuzzy logic control algorithm was designed to adjust the pot temperature during the fixation process. The pre-processed image information was fed into the controller to control the pot temperature continuously. With the intelligently controlled high temperature in the first stage, the enzymatic reaction could be suppressed and more polyphenols and chlorophyll were retained. In the last fixation stage, the temperature was automatically adjusted to low values to minimise oxidation, and more amino acids were formed. Consequently, the final product quality was significantly improved in terms of appearance, liquor colour, fragrance, taste, and residue colour, with the adaptive fuzzy logic controller. Highlights: Images were captured during fixation process. Chemical components were predicted based on image information. Intelligent control was applied to adjust the fixation parameters. Quality was improved with the developed adaptive fuzzy logic control.