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Chen Y, Wu H, Liu Y, Wang Y, Lu C, Li T, Wei Y, Ning J. Monitoring green tea fixation quality by intelligent sensors: comparison of image and spectral information. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:3093-3101. [PMID: 36418909 DOI: 10.1002/jsfa.12350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 11/12/2022] [Accepted: 11/24/2022] [Indexed: 06/16/2023]
Abstract
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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Affiliation(s)
- Yuyu Chen
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Huiting Wu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Ying Liu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Chengye Lu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Tiehan Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Yuming Wei
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
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Wu C, Qian J, Zhang J, Wang J, Li B, Wei Z. Moisture measurement of tea leaves during withering using multifrequency microwave signals optimized by ant colony optimization. J FOOD ENG 2022. [DOI: 10.1016/j.jfoodeng.2022.111174] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Chen J, Yang C, Yuan C, Li Y, An T, Dong C. Moisture content monitoring in withering leaves during black tea processing based on electronic eye and near infrared spectroscopy. Sci Rep 2022; 12:20721. [PMID: 36456868 PMCID: PMC9715558 DOI: 10.1038/s41598-022-25112-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 11/24/2022] [Indexed: 12/05/2022] Open
Abstract
Monitoring the moisture content of withering leaves in black tea manufacturing remains a difficult task because the external and internal information of withering leaves cannot be simultaneously obtained. In this study, the spectral data and the color/texture information of withering leaves were obtained using near infrared spectroscopy (NIRS) and electronic eye (E-eye), respectively, and then fused to predict the moisture content. Subsequently, the low- and middle-level fusion strategy combined with support vector regression (SVR) was applied to detect the moisture level of withering leaves. In the middle-level fusion strategy, the principal component analysis (PCA) and random frog (RF) were employed to compress the variables and select effective information, respectively. The middle-level-RF (cutoff line = 0.8) displayed the best performance because this model used fewer variables and still achieved a satisfactory result, with 0.9883 and 5.5596 for the correlation coefficient of the prediction set (Rp) and relative percent deviation (RPD), respectively. Hence, our study demonstrated that the proposed data fusion strategy could accurately predict the moisture content during the withering process.
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Affiliation(s)
- Jiayou Chen
- grid.495239.00000 0004 4657 1319Liming Vocational University, Quanzhou, 362007 China ,grid.410727.70000 0001 0526 1937Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, 310008 China
| | - Chongshan Yang
- grid.410727.70000 0001 0526 1937Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, 310008 China ,grid.263906.80000 0001 0362 4044College of Engineering and Technology, Southwest University, Chongqing, 400715 China
| | - Changbo Yuan
- grid.452757.60000 0004 0644 6150Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250033 China
| | - Yang Li
- grid.410727.70000 0001 0526 1937Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, 310008 China
| | - Ting An
- grid.410727.70000 0001 0526 1937Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, 310008 China ,grid.263906.80000 0001 0362 4044College of Engineering and Technology, Southwest University, Chongqing, 400715 China
| | - Chunwang Dong
- grid.410727.70000 0001 0526 1937Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, 310008 China ,grid.452757.60000 0004 0644 6150Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250033 China
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A Rapid Prediction Method of Moisture Content for Green Tea Fixation Based on WOA-Elman. Foods 2022; 11:foods11182928. [PMID: 36141056 PMCID: PMC9498461 DOI: 10.3390/foods11182928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/05/2022] [Accepted: 09/15/2022] [Indexed: 11/17/2022] Open
Abstract
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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Detection of adulteration in mutton using digital images in time domain combined with deep learning algorithm. Meat Sci 2022; 192:108850. [DOI: 10.1016/j.meatsci.2022.108850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 04/17/2022] [Accepted: 05/12/2022] [Indexed: 11/19/2022]
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Liu Z, Zhang R, Yang C, Hu B, Luo X, Li Y, Dong C. Research on moisture content detection method during green tea processing based on machine vision and near-infrared spectroscopy technology. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 271:120921. [PMID: 35091181 DOI: 10.1016/j.saa.2022.120921] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 06/14/2023]
Abstract
Moisture content is an important indicator that affects green tea processing. In this study, taking Chuyeqi tea as the research object, a quantitative prediction model of the changes in moisture content during the processing of green tea was constructed based on machine vision and near-infrared spectroscopy technology. First, collect the spectrum and image information in the process of spreading, fixation, first-drying, carding, and second-drying. The competitive adaptive reweighted sampling (CARS) method is then used to extract the characteristic wavelengths in the spectrum, and the image's 9 color features and 6 texture features are combined to establish linear PLSR and nonlinear SVR prediction models by fusing the data information from the two sensors. The results show that, when compared to single data, the PLSR and SVR models based on low-level data fusion do not effectively improve the model's prediction accuracy, but rather produce poor prediction results. In contrast, the PLSR and SVR models established by middle-level data fusion have improved the prediction accuracy of moisture content in green tea processing. Among them, the established SVR model has the best effect. The correlation coefficient of the calibration set (Rc) and the root mean square error of calibration (RMSEC) are 0.9804 and 0.0425, respectively, the correlation coefficient of the prediction set (Rp) and the root mean square error of prediction (RMSEP) are 0.9777 and 0.0490 respectively, and the relative percent deviation is 4.5002. The results show that the middle data fusion based on machine vision and near-infrared spectroscopy technology can effectively predict the moisture content in the processing of green tea, which has important guiding significance for overcoming the low prediction accuracy of a single sensor.
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Affiliation(s)
- Zhongyuan Liu
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang 310008, China; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Rentian Zhang
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang 310008, China; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Chongshan Yang
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang 310008, China
| | - Bin Hu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Xin Luo
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Yang Li
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang 310008, China.
| | - Chunwang Dong
- Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, Zhejiang 310008, China.
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Chen C, Zhang W, Shan Z, Zhang C, Dong T, Feng Z, Wang C. Moisture contents and product quality prediction of Pu-erh tea in sun-drying process with image information and environmental parameters. Food Sci Nutr 2022; 10:1021-1038. [PMID: 35432968 PMCID: PMC9007301 DOI: 10.1002/fsn3.2699] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 10/31/2021] [Accepted: 12/02/2021] [Indexed: 11/07/2022] Open
Abstract
In this study, moisture contents and product quality of Pu-erh tea were predicted with deep learning-based methods. Images were captured continuously in the sun-drying process. Environmental parameters (EP) of air humidity, air temperature, global radiation, wind speed, and ultraviolet radiation were collected with a portable meteorological station. Sensory scores of aroma, flavor, liquor color, residue, and total scores were given by a trained panel. Convolutional neural network (CNN) and gated recurrent unit (GRU) models were constructed based on image information and EP, which were selected in advance using the neighborhood component analysis (NCA) algorithm. The evolved models based on deep-learning methods achieved satisfactory results, with RMSE of 0.4332, 0.2669, 0.7508 (also with R 2 of .9997, .9882, .9986, with RPD of 53.5894, 13.1646, 26.3513) for moisture contents prediction in each batch of tea, tea at different sampling periods, the overall samples, respectively; and with RMSE of 0.291, 0.2815, 0.162, 0.1574, 0.3931 (also with R 2 of .9688, .9772, .9752, .9741, .8906, with RPD of 5.6073, 6.5912, 6.352, 6.1428, 4.0045) for final quality prediction of aroma, flavor, liquor color, residue, total score, respectively. By analyzing and comparing the RMSE values, the most significant environmental parameters (EP) were selected. The proposed combinations of different EP can also provide a valuable reference in the development of a new sun-drying system.
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Affiliation(s)
- Cheng Chen
- Faculty of Management and Economics Kunming University of Science and Technology Kunming China
| | - Wuyi Zhang
- Faculty of Management and Economics Kunming University of Science and Technology Kunming China
| | - Zhiguo Shan
- College of Agriculture and Forestry Pu'er University Pu'er China
| | - Chunhua Zhang
- College of Agriculture and Forestry Pu'er University Pu'er China
| | - Tianwu Dong
- Pu'er Gaoshan Zuxiang Tea Garden Co., Ltd. Pu'er China
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