Feasibility Study on Rapid Determination of Tea Polyphenols in Black Tea by Near Infrared Spectroscopy

JIN Jiarui; SUN Xiaorong; LIU Cuiling; WU Jingzhu; ZHENG Dongyu; CHEN Bingwen

doi:10.13386/j.issn1002-0306.2022060205

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May. 2023

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JOURNAL OF MECHANICAL ENGINEERING > 2023 > 44(10): 256-263.

JIN Jiarui, SUN Xiaorong, LIU Cuiling, et al. Feasibility Study on Rapid Determination of Tea Polyphenols in Black Tea by Near Infrared Spectroscopy[J]. Science and Technology of Food Industry, 2023, 44(10): 256−263. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2022060205

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Feasibility Study on Rapid Determination of Tea Polyphenols in Black Tea by Near Infrared Spectroscopy

doi: 10.13386/j.issn1002-0306.2022060205

JIN Jiarui^{1, 2
,},
SUN Xiaorong^{1, 2,*
,
,},
LIU Cuiling^{1, 2},
WU Jingzhu^{1, 2},
ZHENG Dongyu^{1, 2},
CHEN Bingwen²

1.
Beijing key Laboratory of Food Safety Big Data Technology, Artificial intelligence Academy, Beijing Technology and Business University, Beijing 100048, China
2.
Beijing Technology and Business University, Beijing 100048, China

Received Date: 23 Jun 2022
Issue Publish Date: 15 May 2023

Abstract

Abstract

Tea polyphenol, a vital indicator used for the detection of tea quality, is of great significance to quickly detect the tea polyphenol content via near infrared spectroscopy. In this paper, the near infrared spectroscopy in combination with partial least squares (PLS) was adopted to establish the rapid analysis models by near infrared for tea polyphenol content of powdered and complete tea samples respectively, using 144 black tea samples as the study objects, revealing that the prediction model established by SNV+first derivative+Savitzky-Golay smoothing combined with PLS had the optimal effect in the results. The correlation coefficient (r) was 0.9990 and the root mean square error of calibration (RMSEC) was 0.165% of the training set, while the r was 0.9243 and the root mean square error of prediction (RMSEP) was 0.972% of the prediction set in powered tea samples. At the same time, the r was 0.9967 and the RMSEC was 0.310% of the training set, while the r was 0.9541 and the RMSEP was 0.870% of the prediction set in complete tea samples. The results showed that the PLS model for complete tea samples was better than that for powdered tea. Therefore, rapid and nondestructive detection of tea polyphenols in black tea can be achieved by near infrared spectroscopy.
- black tea,
- tea polyphenols,
- near infrared spectroscopy,
- partial least squares(PLS)

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References(28)

References

[1]	黄玉梅. 谈谈茶叶中茶多酚的药理保健功效[J]. 农业考古,2010(5):255−258. [HUANG Yumei. Talk about the health care effect of tea polyphenols in tea[J]. Agricultural Archaeology,2010(5):255−258.
[2]	陈宗懋. 20世纪茶与健康研究的主要进展[J]. 中国茶叶,2001(4):8−10. [CHEN Zongmao. Major advances in tea and health research in the 20th century[J]. Chinese Tea,2001(4):8−10. doi: 10.3969/j.issn.1000-3150.2001.04.002
[3]	陈淑珍, 甄永苏. 茶多酚的分子作用靶点及其在抗肿瘤药物实验治疗中的作用[J]. 药学学报,2013,48(1):1−7. [CHEN Shuzhen, ZHEN Yongsu. Molecular targets of tea polyphenols and their role in experimental treatment of antitumor drugs[J]. Acta Pharmaceutica Sinica,2013,48(1):1−7.
[4]	陈磊. 不同种类茶叶中茶多酚含量的测定和分析[J]. 食品安全导刊,2021(25):71,73. [CHEN Lei. Determination and analysis of tea polyphenols in different kinds of tea[J]. China Food Safety Magazine,2021(25):71,73.
[5]	于然, 李晔, 金丽华, 等. 高效液相色谱法同时测定茶叶中6种茶多酚含量[J]. 中国食品添加剂,2021,32(5):74−82. [YU Ran, LI Ye, JIN Lihua, et al. Simultaneous determination of 6 kinds of tea polyphenols in tea by HPLC[J]. China Food Additives,2021,32(5):74−82.
[6]	朱虹. 茶叶中茶多酚的检测方法研究[J]. 福建茶叶,2019,41(8):4. [ZHU Hong. Study on the detection method of tea polyphenols in tea[J]. Tea In Fujian,2019,41(8):4. doi: 10.3969/j.issn.1005-2291.2019.08.003
[7]	SIRIPAT S, DARANEE P. Flow injection analysis-spectrophotometry for rapid determination of total polyphenols in tea extracts[J]. Journal of Flow Injection Analysis,2018,25(1):61−64.
[8]	傅海军, 周树斌, 武小红, 等. 茶叶傅里叶近红外光谱的混合模糊极大熵聚类分析[J]. 光谱学与光谱分析,2019,39(11):3465−3469. [FU Haijun, ZHOU Shubin, WU Xiaohong, et al. Mixed fuzzy maximum entropy clustering analysis of Fourier near infrared spectra of tea leaves[J]. Spectroscopy and Spectral Analysis,2019,39(11):3465−3469.
[9]	圣阳, 焦俊, 滕燕, 等. 基于卷积神经网络和近红外光谱的茶叶品种和等级鉴别[J]. 分析科学学报,2022,38(5):552−560. [SHENG Yang, JIAO Jun, TENG Yan, et al. Identification of tea varieties and grades based on convolutional neural network and near infrared spectroscopy[J]. Journal of Analytical Science,2022,38(5):552−560. doi: 10.13526/j.issn.1006-6144.2022.05.004
[10]	王胜鹏, 高士伟, 滕靖, 等. 近红外光谱技术在茶叶中的研究进展[J]. 华中农业大学学报,2021,40(5):226−232. [WANG Shengpeng, GAO Shiwei, TENG Jing, et al. Research progress of near infrared spectroscopy in tea[J]. Journal of Huazhong Agricultural University,2021,40(5):226−232. doi: 10.13300/j.cnki.hnlkxb.2021.05.027
[11]	LUO Yu, LI Wenlong, QU Haibin. Research progress on method validation of near infrared spectroscopy in quantitative analysis[J]. China Journal of Chinese Material Medica,2017,41(19):3515−3519.
[12]	罗一帆, 郭振飞, 朱振宇, 等. 近红外光谱测定茶叶中茶多酚和茶多糖的人工神经网络模型研究[J]. 光谱学与光谱分析,2005(8):1230−1233. [LUO Yifan, GUO Zhenfei, ZHU Zhenfei, et al. Study on Artificial neural network model for determination of tea polyphenols and tea polysaccharides by near infrared spectroscopy[J]. Spectroscopy and Spectral Analysis,2005(8):1230−1233. doi: 10.3321/j.issn:1000-0593.2005.08.017
[13]	夏贤明, 丁宁. 用近红外光谱法检测绿茶中品质成分的研究[J]. 分析化学,1991(8):945−948. [XIA Xianming, DING Ning. Study on the determination of quality components in green tea by near infrared spectroscopy[J]. Chinese Journal of Analytical Chemistry,1991(8):945−948. doi: 10.3321/j.issn:0253-3820.1991.08.001
[14]	WANG Shengpeng, LIU Panpan, FENG Lin, et al. Rapid determination of tea polyphenols content in Qingzhuan tea based on near infrared spectroscopy in conjunction with three different PLS algorithms[J]. Food Science and Technology,2022,42:94322. doi: 10.1590/fst.94322
[15]	李尚科, 杜国荣, 李跑, 等. 基于近红外光谱技术和优化预处理方法的豆浆粉无损鉴别研究[J]. 食品研究与开发,2020,41(17):144−150. [LI Shangke, DU Guorong, LI Pao, et al. Nondestructive identification of soybean milk powder based on near infrared spectroscopy and optimized pretreatment method[J]. Food Research and Development,2020,41(17):144−150. doi: 10.12161/j.issn.1005-6521.2020.17.023
[16]	左旭丽, 潘秀珍, 耿泽宇, 等. 经19种光谱预处理筛选建立3种连翘叶茶近红外定性定量模型[J]. 食品安全质量检测学报,2022,13(19):6431−6440. [ZUO Xuli, PAN Xiuzhen, GENG Zeyu, et al. The near-infrared qualitative and quantitative models of 3 kinds of forsythia leaf tea were established by 19 kinds of spectral pretreatment and screening[J]. Journal of Food Safety & Quality,2022,13(19):6431−6440. doi: 10.19812/j.cnki.jfsq11-5956/ts.2022.19.032
[17]	DU Zhenjiao, TIAN Wenfei, Tilley Michael, et al. Quantitative assessment of wheat quality using near-infrared spectroscopy: A comprehensive review[J]. Comprehensive Reviews in Food Science and Food safety,2022,21(3):2956−3009. doi: 10.1111/1541-4337.12958
[18]	张津源, 张德贤, 张苗. 基于连续投影算法的小麦蛋白质含量近红外光谱预测分析[J]. 江苏农业学报,2019,35(4):960−964. [ZHANg Jinyuan, ZHANG Dexian, ZHANG Miao. Prediction and analysis of wheat protein content by near infrared spectroscopy based on continuous projection algorithm[J]. Jiangsu Journal of Agricultural Sciences,2019,35(4):960−964. doi: 10.3969/j.issn.1000-4440.2019.04.030
[19]	ZHUANG Xingang, WANG Lili, CHEN Qi, et al. Identification of green tea origins by near-infrared(NIR) spectroscopy and different regression tools[J]. Science China(Technological Sciences),2017,60(1):84−90. doi: 10.1007/s11431-016-0464-0
[20]	余代鑫, 郭盛, 张霞, 等. 近红外光谱技术结合化学计量学和机器学习算法的干姜产地溯源研究[J]. 中国中药杂志,2022,47(17):4583−4592. [YU Daixin, GUO Sheng, ZHANG Xia, et al. Origin tracing of dried ginger by near infrared spectroscopy combined with stoichiometry and machine learning algorithm[J]. China Journal of Chinese Materia Medica,2022,47(17):4583−4592. doi: 10.19540/j.cnki.cjcmm.20220514.103
[21]	丁海泉, 温江北, 高洪智, 等. 最小二乘支持向量机用于人参皂苷近红外光谱分析研究[J]. 现代农业装备,2021,42(5):17−22. [DING Haiquan, WEN Jiangbei, GAO Hongzhi, et al. The least square support vector machine is used for near infrared spectroscopy analysis of ginsenosides[J]. Modern Agricultural Equipment,2021,42(5):17−22. doi: 10.3969/j.issn.1673-2154.2021.05.003
[22]	崔蕴涵, 许金钗, 方智毅, 等. 莲子含水率近红外光谱检测模型构建[J]. 食品研究与开发,2021,42(18):130−135. [CUI Yunhan, XU Jinchai, FANG Zhiyi, et al. Construction of near-infrared spectroscopy detection Model for water content of lotus seed[J]. Food Research and Development, Food Research and Development,2021,42(18):130−135. doi: 10.12161/j.issn.1005-6521.2021.18.019
[23]	刘淑文, 游静萍, 李云冰, 等. 影响茶叶中茶多酚含量的因素分析[J]. 福建茶叶,2022,44(2):18−21. [LIU Shuwen, YOU Jingping, LI Yunbing, et al. Analysis on influencing factors of tea polyphenol content in tea[J]. Tea in Fujian,2022,44(2):18−21. doi: 10.3969/j.issn.1005-2291.2022.02.007
[24]	LI Shiming, ZHANG Liang, WAN Xiaochun, et al. Focusing on the recent progress of tea polyphenol chemistry and perspectives[J]. Food Science and Human Wellness,2022,11(3):437−444. doi: 10.1016/j.fshw.2021.12.033
[25]	第五鹏瑶, 卞希慧, 王姿方, 等. 光谱预处理方法选择研究[J]. 光谱学与光谱分析,2019,39(9):2800−2806. [[DI Wupengyao, BIAN Xihui, WANG Zifang, et al. Study on the selection of spectral preprocessing methods[J]. Spectroscopy and Spectral Analysis,2019,39(9):2800−2806.
[26]	苗雪雪, 苗莹, 龚浩如, 等. 不同偏最小二乘法的近红外光谱技术测定大米中水分的研究[J]. 分析科学学报,2019,35(5):643−649. [MIAO Xuexue, MIAO Ying, GONG Haoru, et al. Determination of moisture in rice by near infrared spectroscopy with different partial least squares method[J]. Journal of Analytical Science,2019,35(5):643−649.
[27]	王胜鹏, 龚自明, 何远军, 等. 背景和光程对茶多酚含量近红外预测模型的影响[J]. 华中农业大学学报,2015,34(2):120−124. [WANG Shengpeng, GONG Ziming, HE Yuanjun, et al. Effect of background and optical path on near-infrared prediction model of tea polyphenol content[J]. Journal of Huazhong Agricultural University,2015,34(2):120−124.
[28]	周昌海, 周海军. 铁观音中茶多酚含量的近红外光谱检测模型分析[J]. 宜宾学院学报,2019,19(12):121−124. [ZHOU Changhai, ZHOU Haijun. Analysis of tea polyphenols content in Tieguanyin by near infrared spectroscopy model[J]. Journal of Yibin University,2019,19(12):121−124.

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