高气压光电离-化学电离-飞行时间质谱在绿茶香型快速鉴别中的应用
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摘要
茶香是决定茶叶品质的重要因素之一。本研究基于动态顶空进样-高气压光电离-化学电离-飞行时间质谱仪(HPPI-CI-TOFMS),结合偏最小二乘法判别分析(PLS-DA)和层聚类分析(HCA)等多元统计分析技术,建立了一种快速鉴别绿茶香型的方法,并通过受试者工作特征曲线(ROC)以及置换检验对分类方法进行评估。采用本方法对收集自四川、贵州、浙江、安徽等不同产地的4种香型(嫩香型、栗香型、嫩栗香型、熟栗香型),共31个绿茶样品进行茶香挥发性有机物(VOCs)指纹谱分析。在所得茶香VOCs的HPPI/CI质谱图中,不同香型绿茶特征谱峰的种类和相对强度差异明显;多元统计分析结果表明,同种香型的绿茶样品挥发性茶香成分的自身差异较小,但不同香型之间存在较大差异,可实现准确的分类鉴别,方法模型对未知样品的预测能力达到85.1%,并通过样本验证组测试了该模型的准确度。本方法为绿茶香型的有效鉴别提供了新的思路和途径,在食品的品质评价和质量安全等领域具有潜在的应用价值和良好的发展前景。
As the basic element of tea, aroma is one of the most important factors determining the quality of tea. In this paper, a novel and objective method for rapid discrimination and classification of green tea aromas was developed by coupling of dynamic headspace sampling high-pressure photoionization/chemical ionization-time-of-flight mass spectrometer(HPPI-TOFMS) and multivariate statistical analysis techniques, such as partial least squares discrimination analysis(PLS-DA) and layer cluster analysis(HCA). The classification efficiency was evaluated by analyzing the receiver operating characteristic(ROC) curve. The observations and the robustness were assessed using 200 permutation tests. The volatile fingerprint mass spectra of 31 green tea samples of 4 sub-types(tender chestnut-like, tender-like, chestnut-like and roasted chestnut-like aroma) collected from different production areas in Sichuan, Guizhou, Zhejiang, Anhui, etc, were obtained based on this method. In the HPPI/CI mass spectra of the aroma VOCs from different sub-types of green teas, the types and relative intensities of the characteristic mass peaks were obviously different. The results of multivariate statistical analysis showed that there were little differences among the aroma VOCs of the same sub-type, but significant differences were exhibited among different sub-types, which could achieve accurate classification and identification. The prediction ability of the model for unknown samples reaches 85.1%. This method provided a new idea and approach for the effective identification of green tea aromas, indicating that it had potential application value and broad development prospects in the fields of food quality and safety evaluation.
As the basic element of tea, aroma is one of the most important factors determining the quality of tea. In this paper, a novel and objective method for rapid discrimination and classification of green tea aromas was developed by coupling of dynamic headspace sampling high-pressure photoionization/chemical ionization-time-of-flight mass spectrometer(HPPI-TOFMS) and multivariate statistical analysis techniques, such as partial least squares discrimination analysis(PLS-DA) and layer cluster analysis(HCA). The classification efficiency was evaluated by analyzing the receiver operating characteristic(ROC) curve. The observations and the robustness were assessed using 200 permutation tests. The volatile fingerprint mass spectra of 31 green tea samples of 4 sub-types(tender chestnut-like, tender-like, chestnut-like and roasted chestnut-like aroma) collected from different production areas in Sichuan, Guizhou, Zhejiang, Anhui, etc, were obtained based on this method. In the HPPI/CI mass spectra of the aroma VOCs from different sub-types of green teas, the types and relative intensities of the characteristic mass peaks were obviously different. The results of multivariate statistical analysis showed that there were little differences among the aroma VOCs of the same sub-type, but significant differences were exhibited among different sub-types, which could achieve accurate classification and identification. The prediction ability of the model for unknown samples reaches 85.1%. This method provided a new idea and approach for the effective identification of green tea aromas, indicating that it had potential application value and broad development prospects in the fields of food quality and safety evaluation.
引文
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26 Xie Y Y, Hua L, Hou K Y, Chen P, Zhao W D, Chen W D, Ju B Y, Li H Y. Anal. Chem., 2014, 86(15): 7681-7687
27 Cui X J, Li H B, Wang Y, Hu Y L, Hua L, Li H Y, Han X W, Liu Q F, Yang F, He L M, Chen X Q, Li Q Y, Xiao J P, Deng D H, Bao X H. Chem, 2018, 4(8): 1902-1910
28 Hou K Y, Wang J D, Li H Y. Rapid Commun. Mass Spectrom., 2007, 21(22): 3554-3560
29 Jiang J C, Wang Y, Hou K Y, Hua L, Chen P, Liu W, Xie Y Y, Li H Y. Anal. Chem., 2016, 88(10): 5028-5032
30 Sun W Q, Liang M, Li Z, Shu J N, Yang B, Xu C, Zou Y. Talanta, 2016, 156-157: 191-195
31 Wang Y, Hua L, Jiang J C, Xie Y Y, Hou K Y, Li Q Y, Wu C X, Li H Y. Anal. Chim. Acta, 2018, 1008: 74-81
32 Dorfner R, Ferge T, Yeretzian C, Kettrup A, Zimmermann R. Anal. Chem., 2004, 76(5): 1386-1402
33 Hua L, Wu Q H, Hou K Y, Cui H P, Chen P, Wang W G, Li J H, Li H Y. Anal. Chem., 2011, 83(13): 5309-5316
34 NIST National Institute of Standards and Technology (NIST). http://webbook.nist.gov/chemistry/, (accessed Dec 10, 2018)
35 Wang Y, Hua L, Li Q Y, Jiang J C, Hou K Y, Wu C X, Li H Y. Anal. Chem., 2018, 90(8): 5398-5404
36 Flamen I. Food Rev. Inter., 1989, 5(3): 317-414
2 LI Xiao-Li, HE Yong, QIU Zheng-Jun. Spectroscopy Spectral Analysis, 2007, 27(2): 279-282李晓丽, 何勇, 裘正军. 光谱学与光谱分析, 2007, 27(2): 279-282
3 ZHANG Lai, YU Zheng-Wen, YANG Zhan-Nan, ZHANG Hong-Xia. Chinese Journal of Spectroscopy Laboratory, 2012, 29(2): 672-676张来, 余正文, 杨占南, 张红霞. 光谱实验室, 2012, 29(2): 672-676
4 Yang Z Y, Baldermann S, Watanabe N. Food Res. Int., 2013, 53(2): 585-599
5 YU Shao-Juan, LI Xin-Lei, WANG Ting-Ting, JIN Xin-Yi. J. Tea Communication, 2015, 42(4): 14-18俞少娟, 李鑫磊, 王婷婷, 金心怡. 茶叶通讯, 2015, 42(4): 14-18
6 Yu H C, Wang J, Zhang H M, Yu Y, Yao C. Sens. Actuators B, 2008, 128(2): 455-461
7 Yang Y Q, Zhang M M, Yin H X, Deng Y L, Jiang Y W, Yuan H B, Dong C W, Li J, Hua J J, Wang J J. LWT-Food Sci. Technol., 2018, 96: 42-50
8 Sheibani E, Duncan S E, Kuhn D D, Dietrich A M, O'Keefe S F. J. Food Sci., 2016, 81(2): C348-C358
9 Wu F, Lu W P, Chen J H, Liu W, Zhang L. Talanta, 2010, 82(3): 1038-1043
10 LU Yong-Jun, CHEN Hua-Cai, LV Jin, CHEN Xing-Dan. Chinese J. Anal. Chem., 2005, 33(6): 835-837芦永军, 陈华才, 吕进, 陈星旦. 分析化学, 2005, 33(6): 835-837
11 Zhang M H, Luypaert J, Fernández Pierna J A, Xu Q S, Massart D L. Talanta, 2004, 62(1): 25-35
12 Luypaert J, Zhang M H, Massart D L. Anal. Chim. Acta, 2003, 478(2): 303-312
13 CAO Yu-Hua, ZHANG Xin, DING Xiang-Huan, FANG Yu-Zhi, YE Jian-Nong. Chinese J. Anal. Chem., 2001, 29(9): 1072-1075曹玉华, 张欣, 丁祥欢, 方禹之, 叶建农. 分析化学, 2001, 29(9): 1072-1075
14 Yu H C, Wang Y W, Wang J. Sensors, 2009, 9(10): 8073-8082
15 Tudu B, Jana A, Metla A, Ghosh D, Bhattacharyya N, Bandyopadhyay R. Sens. Actuators B, 2009, 138(1): 90-95
16 Togari N, Kobayashi A, Aishima T. Food Res. Int., 1995, 28(5): 495-502
17 Dutta R, Hines E L, Gardner J W, Kashwan K R, Bhuyan M. Sens. Actuators B, 2003, 94(2): 228-237
18 HUANG Hai-Tao, CHEN Zhang-Yu, SHI Hong-Lin, MIAO En-Ming, LIU Wei, YANG Guang-Yu, ZHANG Cheng-Ming, KONG Wei-Song. Chinese J. Anal. Chem., 2005, 33(8): 1185-1188黄海涛, 陈章玉, 施红林, 缪恩铭, 刘巍, 杨光宇, 张承明, 孔维松. 分析化学, 2005, 33(8): 1185-1188
19 Zhang L, Zeng Z D, Zhao C X, Kong H W, Lu X, Xu G W. J. Chromatogr. A, 2013, 1313: 245-252
20 Li J, Yuan H B, Yao Y F, Hua J J, Yang Y Q, Dong C W, Deng Y L, Wang J J, Li H Y, Jiang Y W, Zhou Q H. Talanta, 2019, 191: 39-45
21 Zhou L J, Jiang J C, Zhao K, Li J X, Wu C X, Li H Y, Tian D, Hou K Y. Chin. Chem. Lett., 2018, 29(5): 707-710
22 LIANG Hua-Zheng, CHEN Huan-Wen. Chinese J. Appl. Chem., 2008, 25(5): 519-523梁华正, 陈焕文. 应用化学, 2008, 25(5): 519-523
23 ZHANG Jia-Ling, ZHANG Wei, ZHOU Zhi-Gui, BAI Yu, LIU Hu-Wei. Chinese Journal of Chromatography, 2011, 29(7): 681-686张佳玲, 张伟, 周志贵, 白玉, 刘虎威. 色谱, 2011, 29(7): 681-686
24 Yener S, Sanchez-Lopez J A, Granitto P M, Cappellin L, Mark T D, Zimmermann R, Bonn G K, Yeretzian C, Biasioli F. Talanta, 2016, 152: 45-53
25 ZHANG Dan-Dan, WEI Hang, QIU Xiao-Hong, ZHENG De-Yong, CAO Jie, YE Nai-Xing. Chinese J. Anal. Chem., 2017, 45(6): 914-921张丹丹, 韦航, 邱晓红, 郑德勇, 曹洁, 叶乃兴. 分析化学, 2017, 45(6): 914-921
26 Xie Y Y, Hua L, Hou K Y, Chen P, Zhao W D, Chen W D, Ju B Y, Li H Y. Anal. Chem., 2014, 86(15): 7681-7687
27 Cui X J, Li H B, Wang Y, Hu Y L, Hua L, Li H Y, Han X W, Liu Q F, Yang F, He L M, Chen X Q, Li Q Y, Xiao J P, Deng D H, Bao X H. Chem, 2018, 4(8): 1902-1910
28 Hou K Y, Wang J D, Li H Y. Rapid Commun. Mass Spectrom., 2007, 21(22): 3554-3560
29 Jiang J C, Wang Y, Hou K Y, Hua L, Chen P, Liu W, Xie Y Y, Li H Y. Anal. Chem., 2016, 88(10): 5028-5032
30 Sun W Q, Liang M, Li Z, Shu J N, Yang B, Xu C, Zou Y. Talanta, 2016, 156-157: 191-195
31 Wang Y, Hua L, Jiang J C, Xie Y Y, Hou K Y, Li Q Y, Wu C X, Li H Y. Anal. Chim. Acta, 2018, 1008: 74-81
32 Dorfner R, Ferge T, Yeretzian C, Kettrup A, Zimmermann R. Anal. Chem., 2004, 76(5): 1386-1402
33 Hua L, Wu Q H, Hou K Y, Cui H P, Chen P, Wang W G, Li J H, Li H Y. Anal. Chem., 2011, 83(13): 5309-5316
34 NIST National Institute of Standards and Technology (NIST). http://webbook.nist.gov/chemistry/, (accessed Dec 10, 2018)
35 Wang Y, Hua L, Li Q Y, Jiang J C, Hou K Y, Wu C X, Li H Y. Anal. Chem., 2018, 90(8): 5398-5404
36 Flamen I. Food Rev. Inter., 1989, 5(3): 317-414