基于机器视觉的茶叶微波杀青中品质变化与预测研究

摘要/Abstract

摘要： 茶多酚、氨基酸、含水率是茶叶品质的重要指标,传统检测方法周期长且过程复杂。本研究利用机器视觉对微波杀青过程中茶叶的色泽和纹理特征实时监测,在线检测含水率,同时检测茶多酚和氨基酸含量。结果表明,色泽、纹理特征与含水率、茶多酚、氨基酸含量均呈规律性变化且显著相关。对色泽和纹理特征进行主成分分析,以前3个主成分为输入建立极限学习机（ELM）、遗传神经网络（GA-BP）、卷积神经网络（CNN）模型对品质成分含量进行预测。结果表明,ELM、GA-BP、CNN模型分别适用于含水率、茶多酚含量和氨基酸含量的预测,精度均在0.99以上。研究表明,通过实时监测茶叶的色泽和纹理特征来预测其在杀青过程中含水率、茶多酚和氨基酸含量是可行的。

关键词: 机器视觉, 微波杀青, 茶多酚, 氨基酸, 含水率, 预测模型

Abstract: Tea polyphenol, amino acid and moisture contents are important indicators of tea quality. Traditional detection methods have long cycles and complex processes. In this paper, machine vision was used to monitor the color and texture of tea leaves in real time during the microwave fixation process. The moisture content was detected online and the tea polyphenol and amino acid contents were also measured. The results show that the color, texture features and moisture content, tea polyphenol, amino acid contents all showed regular changes during the fixation process and had significant correlations. The principal component analysis was used to analyze the color and texture features and the first 3 principal components were taken to establish extreme learning machine (ELM), genetic neural network (GA-BP), and convolutional neural network (CNN) models to predict the quality. The results show that ELM, GA-BP and CNN models were more suitable for the prediction of moisture, tea polyphenol and amino acid contents, respectively, and their accuracies were all above 0.99. The research results show that it is feasible to predict the moisture, tea polyphenol and amino acid contents during the fixation process by monitoring the color and texture features of tea in real time.

Key words: machine vision, microwave fixation, tea polyphenols, amino acids, moisture content, prediction model

中图分类号:

S571.1

吴鑫, 宋飞虎, 裴永胜, 朱冠宇, 姜乐兵, 宁文楷, 李臻峰, 刘本英. 基于机器视觉的茶叶微波杀青中品质变化与预测研究[J]. 茶叶科学, 2021, 41(6): 854-864.

WU Xin, SONG Feihu, PEI Yongsheng, ZHU Guanyu, JIANG Lebing, NING Wenkai, LI Zhenfeng, LIU Benying. Study on the Tea Quality Changes and Predictions during the Microwave Fixation Process by Machine Vision[J]. Journal of Tea Science, 2021, 41(6): 854-864.

参考文献

[1] 尹惠玲. 茶叶对人体的保健作用[J]. 饮食科学, 2017, 10(20): 109.
Yin H L.The health effect of tea on the human body[J]. Diet Science, 2017, 10(20): 109.
[2] 祁丹丹, 戴伟东, 谭俊峰, 等. 杀青方式对夏季绿茶化学成分及滋味品质的影响[J]. 茶叶科学, 2016, 36(1): 18-26.
Qi D D, Dai W D, Tan J F, et al.Study on the effects of the fixation methods on the chemical components and taste quality of summer green tea[J]. Journal of Tea Science, 2016, 36(1): 18-26.
[3] Patel K K, Kar A, Jha S N, et al.Machine vision system: a tool for quality inspection of food and agricultural products[J]. Journal of Food Science & Technology, 2012, 49(2): 123-141.
[4] Aghbashlo M, Hosseinpour S, Ghasemi-Varnamkhasti M.Computer vision technology for real-time food quality assurance during drying process[J]. Trends in Food Science & Technology, 2014, 39(1): 76-84.
[5] Zhu L, Spachos P, Pensini E, et al.Deep learning and machine vision for food processing: a survey[J]. Current Research in Food Science, 2021, 4(3): 233-249.
[6] 李颀, 王康, 强华, 等. 基于颜色和纹理特征的异常玉米种穗分类识别方法[J]. 江苏农业学报, 2020, 36(1): 24-31.
Li Q, Wang K, Qiang H, et al.Classification and recognition method of abnormal corn ears based on color and texture features[J]. Jiangsu Journal of Agricultural Sciences, 2020, 36(1): 24-31.
[7] Golpour I, Chayjan R A, Parian J A, et al.Prediction of paddy moisture content during thin layer drying using machine vision and artificial neural networks[J]. Journal of Agricultural Science & Technology, 2018, 17(2): 287-298.
[8] Laddi A, Prakash N R, Sharma S, et al.Significant physical attributes affecting quality of Indian black (CTC) tea[J]. Journal of Food Engineering, 2012, 113(1): 69-78.
[9] 汤哲, 江才华, 张立, 等. 基于纹理分析的茶青在线分类[J]. 高技术通讯, 2014, 24(6): 651-656.
Tang Z, Jiang C H, Zhang L, et al.Online classification of tea greens based on texture analysis[J]. Chinese High Technology Letters, 2014, 24(6): 651-656.
[10] Pereira L S, Barbon S, Valous N A, et al.Predicting the ripening of papaya fruit with digital imaging and random forests[J]. Computers and Electronics in Agriculture, 2018, 145(1): 76-82.
[11] 徐海霞. 基于机器视觉和电子鼻技术的菠菜新鲜度无损检测研究[D]. 镇江: 江苏大学, 2016.
Xu H X.Study on Nondestructive detection of freshness of post-harvest spinach based on machine vision and electronic nose [D]. Zhenjiang: Jiangsu University, 2016.
[12] Zhu H, Yang Y, He H, et al.Evaluation of green tea sensory quality via process characteristics and image information[J]. Food and Bioproducts Processing, 2017, 102(4): 116-122.
[13] 刘勍, 温志贤, 杨筱平, 等. 现代数字图像噪声滤除技术及其评价[J]. 自动化与仪器仪表, 2012, 3(2): 146-148.
Liu Q, Wen Z X, Yang X P, et al.Modern digital image noise filtering technology and its evaluation[J]. Automation & Instrumentation, 2012, 3(2): 146-148.
[14] Shahabi M, Rafiee S, Mohtasebi S S, et al.Image analysis and green tea color change kinetics during thin-layer drying[J]. Food Science and Technology International, 2013, 20(6): 465-76.
[15] 叶鹏, 王永芳, 夏雨蒙, 等. 一种融合深度基于灰度共生矩阵的感知模型[J]. 计算机科学, 2019, 46(3): 92-96.
Ye P, Wang Y F, Xia Y M, et al.Perceptual model based on GLCM combined with depth[J]. Computer Science, 2019, 46(3): 92-96.
[16] 党满意, 孟庆魁, 谷芳, 等. 基于机器视觉的马铃薯晚疫病快速识别[J]. 农业工程学报, 2020, 36(2): 193-200.
Dang M Y, Meng Q K, Gu F, et al.Rapid recognition of potato late blight based on machine vision[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(2): 193-200.
[17] Dong C, Zhu H, Wang J, et al.Prediction of black tea fermentation quality indices using NIRS and nonlinear tools[J]. Food Science and Biotechnology, 2017, 26(4): 853-860.
[18] Gao Z J, Liu J B, Xiao X G.Purification and characterisation of polyphenol oxidase from leaves of Cleome gynandra L.[J]. Food Chemistry, 2011, 129(3): 1012-1018.
[19] 马思蕊, 康玉梅, 田晓静. 茶叶氨基酸的影响因素与检测方法研究进展[J]. 农产品加工, 2019, 489(19): 61-63.
Ma S R, Kang Y M, Tian X J.Research progress on influencing factors and detection methods of amino acids in tea[J]. Farm Products Processing, 2019, 489(19): 61-63.
[20] 莫婷, 张婉璐, 李平. 茶叶加工中品质关键组分的变化与调控机制[J]. 中国食品学报, 2011, 11(9): 176-180.
Mo T, Zhang W L, Li P.The change and regulation mechanism of key components during tea processing[J]. Journal of Chinese Institute of Food Science and Technology, 2011, 11(9): 176-180.
[21] 滑金杰, 袁海波, 王近近, 等. 微波杀青对茶在制品物理特性影响的初探[J]. 茶叶科学, 2017, 37(5): 476-482.
Hua J J, Yuan H B, Wang J J, et al.Effect of microwave fixation on the physical characteristics of tea fresh leaves[J]. Journal of Tea Science, 2017, 37(5): 476-482.
[22] 李晓斌, 郭玉明, 付丽红. 应用纹理分析方法在线监测苹果冻干含水率[J]. 农业工程学报, 2012, 28(21): 229-235.
Li X B, Guo Y M, Fu L H.On-line monitoring of moisture ratio for apple during vacuum freeze-drying based on image texture analysis[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(21): 229-235.
[23] Huang G B, Zhu Q Y, Siew C K.Extreme learning machine: theory and applications[J]. Neurocomputing, 2006, 70(1/3): 489-501.
[24] 张海东, 李贵荣, 李若诚, 等. 近红外光谱结合极限学习机和GA-PLS算法检测普洱茶茶多酚含量[J]. 激光与光电子学进展, 2013, 50(4): 180-186.
Zhang H D, Li G R, Li R C, et al.Determination of tea polyphenols content in Puerh tea using near infrared spectroscopy combined with extreme learning machine and GA-PLS algorithm[J]. Laser & Optoelectronics Progress, 2013, 50(4): 180-186.
[25] 陈远玲, 王肖, 孙英杰, 等. 基于GA-BP神经网络的甘蔗收获质量预测[J]. 农机化研究, 2022, 44(2): 187-191.
Chen Y L, Wang X, Sun Y J, et al.Sugarcane harvest quality prediction based on GA-BP neural network[J]. Journal of Agricultural Mechanization Research, 2022, 44(2): 187-191.
[26] 邱丽媛, 梁泽华, 吴鑫雨, 等. 基于模式识别和遗传神经网络算法的醋香附近红外光谱等级评价和含量预测模型研究[J]. 中草药, 2021, 52(13): 3818-3830.
Qiu L Y, Liang Z H, Wu X Y, et al.Study on near infrared spectrum grade evaluation and content prediction model of vinegar-processed Cyperi Rhizoma based on pattern recognition and GA-BPNN[J]. Chinese Traditional and Herbal Drugs, 2021, 52(13): 3818-3830.
[27] 汪建, 杜世平. 基于颜色和形状的茶叶计算机识别研究[J]. 茶叶科学, 2008, 28(6): 420-424.
Wang J, Du S P.Identification investigation of tea based on HSI color space and figure[J]. Journal of Tea Science, 2008, 28(6): 420-424.
[28] 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6): 1229-1251.
Zhou F Y, Jin L P, Dong J.Review of convolutional neural network[J]. Chinese Journal of Computers, 2017, 40(6): 1229-1251.
[29] 李英超. 基于卷积神经网络的电力市场电价预测[J]. 机械设计与制造工程, 2021, 50(1): 101-104.
Li Y C.The electricity market price forecast based on the convolutional neural network[J]. Machine Design and Manufacturing Engineering, 2021, 50(1): 101-104.
[30] 杜剑, 胡炳樑, 刘永征, 等. 基于卷积神经网络与光谱特征的夏威夷果品质鉴定研究[J]. 光谱学与光谱分析, 2018, 38(5): 1514-1519.
Du J, Hu B L, Liu Y Z, et al.Study on quality identification of macadamia nut based on convolutional neural networks and spectral features[J]. Spectroscopy and Spectral Analysis, 2018, 38(5): 1514-1519.