Construction of Quantitative Evaluation Model of Baoqing Guiding Black Tea Based on Chromatic Aberration Analysis

DU Shaolong; ZHOU Zhimei; LEI Yalan; LI Jin; TIAN Rongji; TANG Han

doi:10.13386/j.issn1002-0306.2021100164

Volume 43 Issue 14

Jul. 2022

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Science and Technology of Food Industry > 2022 > 43(14): 329-335. > DOI: 10.13386/j.issn1002-0306.2021100164

DU Shaolong, ZHOU Zhimei, LEI Yalan, et al. Construction of Quantitative Evaluation Model of Baoqing Guiding Black Tea Based on Chromatic Aberration Analysis[J]. Science and Technology of Food Industry, 2022, 43(14): 329−335. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2021100164.

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Construction of Quantitative Evaluation Model of Baoqing Guiding Black Tea Based on Chromatic Aberration Analysis

1.
Hunan Xinsheng Tea Sci-tech Co., Ltd., Shaoyang 422900, China
2.
College of Chemistry and Chemical Engineering, Hunan Normal University, Changsha 410083, China
3.
Shaoyang Academy of Agricultural Sciences, Shaoyang 422000, China

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Received Date: October 17, 2021
Available Online: May 11, 2022

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Abstract

Abstract

Objective: In order to construct a quantitative evaluation model of Baoqing Guiding black tea based on chromatic aberration. Methods: Baoqing Guiding black tea were used as materials. On the basis of sensory evaluation, the chromatic aberration value of dry tea, tea infusion and infused leaf were measured respectively. The correlation between chromatic aberration value and tea quality were analyzed, and the quality evaluation model were constructed by GA-BP neural networks. Results: The quality of Baoqing Guiding black tea was extremely significant correlated with the Lab value of tea infusion and leaves (P<0.01), and significantly correlated with the a value of dry tea (P<0.05). The introduction of genetic algorithm (GA) obviously improved the fitting accuracy of BP neural network, the coefficient of determination (R²) of GA-BP model was obviously higher than BP model. By comparing different hidden layer structures, the GA-BP neural network with structure of 9-5-1 were selected. The determination coefficients (R²) of training, verification, test and prediction of optimized GA-BP model were 0.988, 0.976, 0.933 and 0.95 respectively. Conclusion: The quantitative quality evaluation method of Baoqing Guiding black tea based on chromatic aberration and GA-BP neural network was feasible.
- chromatic aberration system,
- GA-BP neural networks,
- Baoqing Guiding black tea,
- quality evaluation model

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References

[1]	王璟, 高静, 刘思彤, 等. 基于色差系统的黄茶外观色泽评价模型构建及其关键物质基础分析[J]. 食品科学,2017,38(17):145−150. [WANG Jin, GAO Jing, LIU Sitong, et al. Establishment of an evaluation model for color of yellow tea based on color difference and analysis of key pigments[J]. Food Chemistry,2017,38(17):145−150. doi: 10.7506/spkx1002-6630-201717024 WANG Jin, GAO Jing, LIU Sitong, et al. Establishment of an evaluation model for color of yellow tea based on color difference and analysis of key pigments[J]. Food Chemistry, 2017, 38(17): 145-150. doi: 10.7506/spkx1002-6630-201717024
[2]	OUYANG Qin, LIU Yan, CHEN Quansheng, et al. Intelligent evaluation of color sensory quality of black tea by visible-near infrared spectroscopy technology: A comparison of spectra and color data information[J]. Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy,2017,180:91−96. doi: 10.1016/j.saa.2017.03.009
[3]	LIANG Yuerong, LU Jiangliang, ZHANG Lingyun, et al. Estimation of black tea quality by analysis of chemical composition and colour difference of tea infusions[J]. Food Chemistry,2003,80:283−290. doi: 10.1016/S0308-8146(02)00415-6
[4]	LI Luqing, XIE Shimeng, ZHU Fengyuan, et al. Colorimetric sensor array-based artificial olfactory system for sensing Chinese green tea’s quality: A method of fabrication[J], International Journal of Food Properties, 2017, 20(20): 1762−1773.
[5]	关洁婷, 谢郁超, 周巧仪, 等. 英红九号红茶茶汤色泽与感官评价之间的相关性研究[J]. 广东茶叶,2020(3):22−25. [GUAN Jieting, XIE Yuchao, ZHOU Qiaoyi, et al. Study on correlation between tea infusion color and sensory evaluation of Yinghong No. 9 black tea[J]. Guangdong Tea,2020(3):22−25. GUAN Jieting, XIE Yuchao, ZHOU Qiaoyi, et al. Study on correlation between tea infusion color and sensory evaluation of Yinghong No. 9 black tea[J]. Guangdong Tea, 2020(3): 22-25.
[6]	苏静, 李长文, 梁慧珍, 等. 色差技术在普洱茶品质控制中的应用研究[J]. 食品研究与开发,2017,38(20):148−151. [SU Jing, LI Changwen, LIANG Huizhen, et al. Application and research of chromaticity technology in quality control of Pu’erh tea[J]. Food Research and Development,2017,38(20):148−151. doi: 10.3969/j.issn.1005-6521.2017.20.031 SU Jing, LI Changwen, LIANG Huizhen, et al. Application and research of chromaticity technology in quality control of Pu’erh tea[J]. Food Research and Development, 2017, 38(20): 148-151. doi: 10.3969/j.issn.1005-6521.2017.20.031
[7]	余书平, 尹军峰, 袁海波, 等. 小叶种红茶发酵外观色差及其主要品质成分相关性[J]. 食品安全质量检测学报,2015,6(6):2201−2208. [YU Shuping, YIN Junfeng, YUAN Haibo, et al. Appearance chromatic aberration of fermented leaves of small leaf black tea and correlation analysis of the main quality components[J]. Journal of Food Safety and Quality,2015,6(6):2201−2208. YU Shuping, YIN Junfeng, YUAN Haibo, et al. Appearance chromatic aberration of fermented leaves of small leaf black tea and correlation analysis of the main quality components[J]. Journal of Food Safety and Quality, 2015, 6(6): 2201-2208.
[8]	杨娟, 袁林颖, 钟应富, 等. 工夫红茶色泽与品质相关性研究[J]. 西南农业学报,2014,27(6):2605−2610. [YANG Juan, YUAN Linying, ZHONG Yingfu, et al. Study on correlation between color and quality of congou made of different varieties of tea[J]. Southwest China Journal of Agricultural Sciences,2014,27(6):2605−2610. YANG Juan, YUAN Linying, ZHONG Yingfu, et al. Study on correlation between color and quality of congou made of different varieties of tea[J]. Southwest China Journal of Agricultural Sciences, 2014, 27(6): 2605-2610.
[9]	王家勤, 姚月凤, 袁海波, 等. 基于色差系统的工夫红茶茶汤亮度的量化评价方法研究[J]. 茶叶科学,2020,40(2):259−268. [WANG Jiaqin, YAO Yuefeng, YUAN Haibo, et al. A quantitative method for brightness evaluation of congou black tea infusion based on color difference analysis[J]. Journal of Tea Science,2020,40(2):259−268. doi: 10.3969/j.issn.1000-369X.2020.02.012 WANG Jiaqin, YAO Yuefeng, YUAN Haibo, et al. A quantitative method for brightness evaluation of congou black tea infusion based on color difference analysis[J]. Journal of Tea Science, 2020, 40(2): 259-268. doi: 10.3969/j.issn.1000-369X.2020.02.012
[10]	陈星, 赵雯俊. 8种工夫红茶的色素含量、茶汤色差值与感官品质间的相关性探讨[J]. 江西农业学报,2014,26(5):47−50. [CHEN Xing, ZHAO Wenjun. Studies on correlation between pigment contents, tea soup chromatism parameters and sensory quality of eight congou black tea[J]. Acta Agriculturae Jiangxi,2014,26(5):47−50. doi: 10.3969/j.issn.1001-8581.2014.05.012 CHEN Xing, ZHAO Wenjun. Studies on correlation between pigment contents, tea soup chromatism parameters and sensory quality of eight congou black tea[J]. Acta Agriculturae Jiangxi, 2014, 26(5): 47-50. doi: 10.3969/j.issn.1001-8581.2014.05.012
[11]	马婉君, 马士成, 施江, 等. 六堡茶茶汤色泽量化分析3 种方法的比较[J]. 食品科学,2021,42(16):155−161. [MA Wanjun, MA Shicheng, SHI Jiang, et al. Comparative study on three quantitative analysis methods for the color Liupao tea infusion[J]. Food Chemistry,2021,42(16):155−161. doi: 10.7506/spkx1002-6630-20200315-237 MA Wanjun, MA Shicheng, SHI Jiang, et al. Comparative study on three quantitative analysis methods for the color Liupao tea infusion[J]. Food Chemistry, 2021, 42(16): 155-161. doi: 10.7506/spkx1002-6630-20200315-237
[12]	韩力群. 人工神经网络教程[M]. 北京: 北京邮电大学出版社, 2006: 16. HAN Liqun. Artifical neural networks tutorial[M]. Beijing: Beijing University of Posts and Telecommunications Press, 2006: 16.
[13]	黄凤. 基于计算机视觉技术的武夷岩茶识别研究[J]. 哈尔滨师范大学自然科学学报,2014,30(6):60−64. [HUANG Feng. The study of Wuyi rock tea recognition based on computer vision technology[J]. Natural Sciences Journal of Harbin Normal University,2014,30(6):60−64. doi: 10.3969/j.issn.1000-5617.2014.06.017 HUANG Feng. The study of Wuyi rock tea recognition base on computer vision technology[J]. Natural Sciences Journal of Harbin Normal University, 2014, 30(6): 60-64. doi: 10.3969/j.issn.1000-5617.2014.06.017
[14]	XU Min, WANG Jun, ZHU Luyi. The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye combined with chemometrics[J]. Food Chemistry,2019,289:482−489. doi: 10.1016/j.foodchem.2019.03.080
[15]	薛大为, 孔慧芳, 杨春兰. 主成分分析与神经网络结合的黄山毛峰茶品质检测[J]. 计算机与应用化学,2014,31(5):578−582. [XUE Dawei, KONG Huifang, YANG Chunlan. Huangshanmaofeng tea quality detection based on principal component analysis and neural network[J]. Computers and Applied Chemistry,2014,31(5):578−582. XUE Dawei, KONG Huifang, YANG Chunlan. Huangshanmaofeng tea quality detection based on principal component analysis and neural network[J]. Computers and Applied Chemistry, 2014, 31(5): 578-582.
[16]	SONG Yan, WANG Xiaozhong, XIE Hanlei, et al. Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors[J]. Spectrochimica Acta Part A:Molecular and biomolecular Spectroscopy,2021,252:119522. doi: 10.1016/j.saa.2021.119522
[17]	YU Peigen, LOW Meiyin, ZHOU Weibiao. Development of a partial least suqares-artifical neural network (PLS-ANN) hybrid model for the prediction of consumer liking scores of ready-to-drink green rea beverages[J]. Food Research International,2018,103:68−75. doi: 10.1016/j.foodres.2017.10.015
[18]	SHI Bolin, ZHAO Lei, ZHI Ruicong, et al. Optimization of electronic nose sensor array by genetic algorithms in Xihu-Longjing tea quality analysis[J]. Mathematical and Computer Modelling,2013(58):752−758.
[19]	YU Huichun, WANG Jun, YAO Cong, et al. Quality grade identidication of green tea using E-nose by CA and ANN[J]. LWT-Food Science and Technology,2008,41(7):1268−1273. doi: 10.1016/j.lwt.2007.08.018
[20]	雷亚兰, 周志梅, 李瑾, 等. 基于主成分分析和聚类分析方法评价宝庆桂丁绿茶品质特性[J]. 食品工业科技, 2022,43(06):269-277. LEI Yalan, ZHOU Zhimei, LI Jin, et al. Quality analysis of Baoqing Guiding Green Tea based on principal component and cluster analysis[J]. Science and Technology of Food Industry,2022,43(06):269-277.
[21]	国家标准化管理委员会. 茶叶感官审评方法: GB/T 23776-2018[S]. 北京: 中国标准出版社, 2018: 1−28. Standardization Administration of the People’s Republic of China. Methodology for sensory evaluation of tea: GB/T 23776-2018 [S]. Beijing: China Standard Press, 2018: 1−28.
[22]	陆建良, 梁月荣, 龚淑英, 等. 茶汤色差与茶叶感官品质相关性研究[J]. 茶叶科学,2002,22(1):57−61. [LU Jianliang, LIANG Yuerong, GONG Shuying, et al. Studies on relationship between liquor chromaticity and organoleptic quality of tea[J]. Journal of Tea Science,2002,22(1):57−61. doi: 10.3969/j.issn.1000-369X.2002.01.012 LU Jianliang, LIANG Yuerong, GONG Shuying, et al. Studies on relationship between liquor chromaticity and organoleptic quality of tea[J]. Journal of Tea Science, 2002, 22(1): 57-61. doi: 10.3969/j.issn.1000-369X.2002.01.012
[23]	严俊, 林刚, 叶付刚, 等. 测色技术在工夫红茶品质评价中的应用研究[J]. 中国农学通报,1997,13(6):24−26. [YAN Jun, LIN Gang, YE Fugang, et al. The study on the application of the measuring color technique to Congo (Qimen black tea) quality evaluation[J]. Chinese Agricultural Science Bulltin,1997,13(6):24−26. YAN Jun, LIN Gang, YE Fugang, et al. The study on the application of the measuring color technique to Congo (Qimen black tea) quality evaluation[J]. Chinese Agricultural Science Bulltin, 1997. 13(6): 24-26.
[24]	钟应富, 李中林, 周正科, 等. 利用色差计分析针形绿名茶色泽在加工过程中的变化[J]. 南方农业,2008,7(2):12−13. [ZHONG Yingfu, LI Zhonglin, ZHOU Zhengke, et al. Analysis of the change of needle green tea color during the manufacture processing using color difference meter[J]. South China Agriculture,2008,7(2):12−13. ZHONG Yingfu, LI Zhonglin, ZHOU Zhengke, et al. Analysis of the change of needle green tea color during the manufacture processing using color difference meter[J]. South China Agriculture, 2008, 7(2): 12-13.
[25]	严俊, 林刚. 测色技术在工夫红茶品质评价中的应用研究(二)茶叶色泽的测定[J]. 茶业通报,1995,17(2):1−3. [YAN Jun, LIN Gang. The study on the application of the measuring color technique to Congo (Qimen black tea) quality evaluation (2) determination of tea color[J]. Journal of Tea Business,1995,17(2):1−3. YAN Jun, LIN Gang. The study on the application of the measuring color technique to Congo (Qimen black tea) quality evaluation (2) determination of tea color[J]. Journal of Tea Business, 1995, 17(2): 1-3.
[26]	陈鹏. 基于 GA-BP 神经网络的运营高速公路隧道健康评价[D]. 石家庄: 石家庄铁道大学, 2019. CHEN Peng. Health assessment of operating expressway tunnel based on GA-BP neural network[D]. Shijiazhuang: Shijiazhuang Tiedao University, 2019.
[27]	欧伊伶, 张娅楠, 覃丽, 等. 茶叶色香味品质评价方法研究进展[J], 食品工业科技, 2019, 40(6): 342−360. OU Yiling, ZHANG Yaan, QIN Li, et al. Research advances on quality evaluation methods of tea color, aroma and taste[J]. Science and Technology of Food Industry, 2019, 40(6): 342−360.
[28]	周敬. 基于多特征融合的茶叶外观品质分级检测技术研究[D]. 成都: 成都大学, 2021. ZHOU Jing. Research on tea appearance quality classification detection technology based on multi-feature fusion[D]. Chengdu: Chengdu University, 2021.
[29]	GUO Z M, BARIMAH A O, SHUJAT A, et al. Simultaneous quantification of active constituents and antioxidant capability of green tea using NIR spectroscopy coupled with swarm intelligence algorithm[J]. LWT-Food Science and Technology,2020,129:109510. doi: 10.1016/j.lwt.2020.109510
[30]	BHATTACHARYYA N, SETH S, TUDU B, et al. Detection of optimum fermentation time for black tea manufacturing using electronic nose[J]. Sensors and Actuators B,2007,122:627−634. doi: 10.1016/j.snb.2006.07.013

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