Rapid and Dynamic Determination Models of Amino Acids and Catechins Concentrations during the Processing Procedures of Keemun Black Tea
NING Jing-ming1, YAN Ling1, ZHANG Zheng-zhu1*, WEI Ling-dong1, LI Lu-qing1, FANG Jun-ting1, HUANG Cai-wang2
1. State Key Laboratory of Tea Plant Biology and Utilization Tea Plant, Anhui Agricultural University, Hefei 230036, China 2. JindongTea Co.Ltd of Qimen, Huangshan 245600, China
Abstract：Tea is one of the most popular beverages in the world. For the contribution to the taste and healthy functions of tea, amino acids and catechins are important components. Among different kinds of black teas in the world, Keemun black tea has the famous and specific fragrance, “Keemun aroma”. During the processing procedure of Keemun black tea, the contents of amino acids and catechins changed greatly, and the differences of these concentrations during processing varied significantly. However, a rapid and dynamic determination method during the processing procedure was not existed up to now. In order to find out a rapid determination method for the contents of amino acids and catechins during the processing procedure of Keemun black tea, the materials of fresh leaves, withered leaves, twisted leaves, fermented leaves, and crude tea (after drying) were selected to acquire their corresponding near infrared spectroscopy and obtain their contents of amino acids and catechins by chemical analysis method. The original spectra data were preprocessed by the Standard Normal Variate Transformation (SNVT) method. And the model of Near Infrared (NIR) spectroscopy with the contents of amino acids and catechins combined with Synergy Interval Partial Least squares (Si-PLS) was established in this study. The correlation coefficients and the cross validation root mean square error are treated as the efficient indexes for evaluating models. The results showed that the optimal prediction model of amino acids by Si-PLS contained 20 spectral intervals combined with 4 subintervals and 9 principal component factors. The correlation coefficient and the root mean square error of the calibration set were 0.955 8 and 1.768, respectively; the correlation coefficient and the root mean square error of the prediction set were 0.949 5 and 2.16, respectively. And the optimal prediction model of catechins by Si-PLS contained 20 spectral intervals combined with 3 subintervals and 10 principal component factors. The correlation coefficient and the root mean square error of the calibration set were 0.940 1 and 1.22, respectively; the correlation coefficient and the root mean square error of the prediction set were 0.938 5 and 1.17, respectively. The results showed that the established models had good accuracy which could provide a theoretical foundation for the online determination of tea chemical components during processing.
Key words：Keemun black tea;Quality;Near infrared spectroscopy;Models
宁井铭1，颜 玲1，张正竹1*，韦玲冬1，李露青1，方骏婷1，黄财旺2 . 祁门红茶加工中氨基酸和儿茶素快速检测模型建立 [J]. 光谱学与光谱分析, 2015, 35(12): 3422-3426.
NING Jing-ming1, YAN Ling1, ZHANG Zheng-zhu1*, WEI Ling-dong1, LI Lu-qing1, FANG Jun-ting1, HUANG Cai-wang2 . Rapid and Dynamic Determination Models of Amino Acids and Catechins Concentrations during the Processing Procedures of Keemun Black Tea . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(12): 3422-3426.
[1] YAN Yan-lu，ZHAO Long-lian，HAN Dong-hai(严衍禄，赵龙莲，韩东海). The Foundation and Application of Near Infrared Spectroscopy Analysis(近红外光谱分析基础与应用). Beijing：China Light Industry Press(北京：中国轻工业出版社)，2005. [2] SUN Xu-dong, DONG Xiao-ling(孙旭东，董小玲). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 14: 261. [3] Cecilia S, Luis R.Journal of Food Composition and Analysis, 2009, 22(6): 596. [4] DAI Fen, HONG Tian-sheng, YUE Xue-jun, et al(代 芬，洪添胜，岳学军，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2011, 42(4): 133. [5] Giovanellia N, Sinellia R, Beghib R. Postharvest Biology and Technology，2014, 87: 13. [6] GUO Wen-chuan, WANG Ming-hai, YUE Rong(郭文川，王铭海，岳 绒). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报)2013，44(2)：143. [7] Cai J, Chen Q, Wan X, et al. Food Chemistry, 2011, 126: 1354. [8] Chen Q, Zhao J, Liu M, et al. Journal of Pharmaceutical and Biomedical Analysis, 2008, 46：568. [9] Wang J, Liu H, Ren G. The Crop Journal, 2014, 2: 28. [10] YANG Fang, WANG Dan-hong, LIN Zhen-yu, et al(杨 方，王丹红，林振宇，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(3): 656. [11] LIN Xin，NIU Zhi-you(林 新，牛智有). Journal of Huazhong Agricultural University(华中农业大学学报), 2008, 27(2): 326. [12] REN Guang-xin, ZHANG Zheng-zhu, NING Jing-ming, et al(任广鑫，张正竹，宁井铭，等). Chinese Journal of Spectroscopy Laboratory(光谱实验室), 2012, 29(4): 2148.
