An adaptive fuzzy logic control of green tea fixation process based on image processing technology

Cheng, Chen; Liu, Benying; Song, Feihu; Jiang, Jianjun; Li, Zhenfeng; Song, Cuihua; Li, Jing; Wu, Jincheng

doi:10.1016/j.biosystemseng.2021.12.023

Cited by 7 publications

(8 citation statements)

References 33 publications

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“…Previous studies used aroma [ 6 ] and color [ 30 ] as factors to evaluate fixation; however, the former is susceptible to the environment and the latter lacks comprehensive information. Moisture content is simple to measure and has a positive correlation with green tea quality [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

“…The fixation is a craft that requires experience and systematic training. However, young people today are unwilling to take the time and patience to learn, and these skills are being lost [ 30 ]. Therefore, it is necessary to realize the automation of fixation.…”

Section: Discussionmentioning

confidence: 99%

“…In previous studies, aroma and color were used as indicators to online evaluate green tea fixation degree [ 6 , 30 ]. However, in this study, we use moisture as the online monitoring object of the fixation and first combine machine vision and miniature near-infrared spectroscopy to quantitatively predict moisture.…”

Section: Introductionmentioning

confidence: 99%

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A Rapid Prediction Method of Moisture Content for Green Tea Fixation Based on WOA-Elman

Lan

Shen

Yuan

et al. 2022

Foods

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Fixation is the most critical step in the green tea process. Hence, this study developed a rapid and accurate moisture content detection for the green tea fixation process based on near-infrared spectroscopy and computer vision. Specifically, we created a quantitative moisture content prediction model appropriate for the processing of green tea fixation. First, we collected spectrum and image information of green tea fixation leaves, utilizing near-infrared spectroscopy and computer vision. Then, we applied the partial least squares regression (PLSR), support vector regression (SVR), Elman neural network (ENN), and Elman neural network based on whale optimization algorithm (WOA-ENN) methods to build the prediction models for single data (data from a single sensor) and mid-level data fusion, respectively. The results revealed that the mid-level data fusion strategy combined with the WOA-ENN model attained the best effect. Namely, the prediction set correlation coefficient (Rp) was 0.9984, the root mean square error of prediction (RMSEP) was 0.0090, and the relative percent deviation (RPD) was 17.9294, highlighting the model’s excellent predictive performance. Thus, this study identified the feasibility of predicting the moisture content in the process of green tea fixation by miniaturized near-infrared spectroscopy. Moreover, in establishing the model, the whale optimization algorithm was used to overcome the defect whereby the Elman neural network falls into the local optimum. In general, this study provides technical support for rapid and accurate moisture content detection in green tea fixation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A Rapid Prediction Method of Moisture Content for Green Tea Fixation Based on WOA-Elman

Lan

Shen

Yuan

et al. 2022

Foods

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show abstract

“…predicted the chemical compositions of green tea before and after fixation based on color information and further improved green tea quality through an adaptive fuzzy logic control system. Regrettably, the fixation process for this study was performed offline 11 . In addition, rapid evaluation of green tea fixation quality utilizing CVS has rarely been reported.…”

Section: Introductionmentioning

confidence: 99%

“…The same team also established a non-destructive detection method for the moisture of black tea during withering by CVS. 10 Chen et al 11 predicted the chemical compositions of green tea before and after fixation based on color information and further improved green tea quality through an adaptive fuzzy logic control system. Regrettably, the fixation process for this study was performed offline.…”

Section: Introductionmentioning

confidence: 99%

Monitoring green tea fixation quality by intelligent sensors: comparison of image and spectral information

Chen

Liu

et al. 2022

J Sci Food Agric

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BACKGROUND Intelligent monitoring of fixation quality is a prerequisite for automated green tea processing. To meet the requirements of intelligent monitoring of fixation quality in large‐scale production, fast and non‐destructive detection means are urgently needed. Here, smartphone‐coupled micro near‐infrared spectroscopy and a self‐built computer vision system were used to perform rapid detection of the fixation quality in green tea processing lines. RESULTS Spectral and image information from green tea samples with different fixation degrees were collected at‐line by two intelligent monitoring sensors. Competitive adaptive reweighted sampling and correlation analysis were employed to select feature variables from spectral and color information as the target data for modeling, respectively. The developed least squares support vector machine (LS‐SVM) model by spectral information and the LS‐SVM model by image information achieved the best discriminations of sample fixation degree, with both prediction set accuracies of 100%. Compared to the spectral information, the image information‐based support vector regression model performed better in moisture prediction, with a correlation coefficient of prediction of 0.9884 and residual predictive deviation of 6.46. CONCLUSION The present study provided a rapid and low‐cost means of monitoring fixation quality, and also provided theoretical support and technical guidance for the automation of the green tea fixation process. © 2022 Society of Chemical Industry.

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Analysis of volatile metabolite variations in strip green tea during processing and effect of rubbing degree using untargeted and targeted metabolomics

Ouyang

Wang

et al. 2022

Food Research International

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An adaptive fuzzy logic control of green tea fixation process based on image processing technology

Cited by 7 publications

References 33 publications

A Rapid Prediction Method of Moisture Content for Green Tea Fixation Based on WOA-Elman

A Rapid Prediction Method of Moisture Content for Green Tea Fixation Based on WOA-Elman

Monitoring green tea fixation quality by intelligent sensors: comparison of image and spectral information

Analysis of volatile metabolite variations in strip green tea during processing and effect of rubbing degree using untargeted and targeted metabolomics

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