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SCIENTIA SINICA Chimica, Volume 49, Issue 4: 625-636(2019) https://doi.org/10.1360/N032018-00249

Synergetic quantitative composition-activity relationship between the aroma components and aroma quality of 8 famous green teas

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  • ReceivedNov 15, 2018
  • AcceptedJan 9, 2019
  • PublishedMar 4, 2019

Abstract

This article identifies the aroma quality of eight different types and grades of famous green teas by E-nose combined with partial least squares discriminant analysis (PLSDA). Subsequently, in order to further explain the reasons for the differences in aroma quality, the background drift correction, multi-scale Gaussian smoothing and chromatographic retention time correction methods were employed to preprocess the chromatographic fingerprint of the famous green tea collected from GC-MS technology. Then, the pre-processed fingerprint and the famous green tea aroma score were analyzed to construct spectrum-activity relationship model by moving window partial least squares regression (MWPLSR). As a result, 21 kinds of latent characteristic aroma substances were screened out. Finally, the variable weighted least squares support vector (PSO-VWLS-SVM) was used to correlate the content of 21 kinds of latent characteristic aroma substances with the score for the aroma quality of different green tea. According to the contribution rate of each characteristic aroma substance to the score, a synergistic quantitative composition-activity relationship between aroma substances and the aroma quality of the famous green tea was successfully revealed. This method provided a new strategy for screening and quantitative analysis of characteristic aroma substances , as well their synergistic relationship in aroma quality of green tea.


Funded by

国家自然科学基金(21776321,21576297,21776259)


References

[1] Yang Z, Baldermann S, Watanabe N. Food Res Int, 2013, 53: 585-599 CrossRef Google Scholar

[2] Lee J, Chambers DH, Chambers Iv E. J Sci Food Agric, 2014, 94: 1315-1324 CrossRef PubMed Google Scholar

[3] Miyazawa T, Gallagher M, Preti G, Wise PM. Chem Percept, 2008, 1: 163-167 CrossRef Google Scholar

[4] Pignitter M, Stolze K, Jirsa F, Gille L, Goodman BA, Somoza V. J Agric Food Chem, 2015, 63: 8519-8526 CrossRef PubMed Google Scholar

[5] Bartle KD, Myers P. TrAC Trends Anal Chem, 2002, 21: 547-557 CrossRef Google Scholar

[6] Mizukami Y, Kohata K, Yamaguchi Y, Hayashi N, Sawai Y, Chuda Y, Ono H, Yada H, Yoshida M. J Agric Food Chem, 2006, 54: 7370-7377 CrossRef PubMed Google Scholar

[7] Cheng Y, Huynh-Ba T, Blank I, Robert F. J Agric Food Chem, 2008, 56: 2160-2169 CrossRef PubMed Google Scholar

[8] Wang J, Wei ZB. RSC Adv, 2015, 5: 106959-106970 CrossRef Google Scholar

[9] Zhu JC, Chen F, Wang LY, Niu YW, Xiao ZB. Food Chem, 2017, 221: 1484-1490 CrossRef PubMed Google Scholar

[10] Ni H, Hao S, Zheng FP, Zhang LZ, Lee B, Wang YQ, Chen F. Food Sci Biotechnol, 2017, 26: 1–12. Google Scholar

[11] Wang C, Lv S, Wu Y, Lian M, Gao X, Meng Q. J Sci Food Agric, 2016, 96: 4492-4498 CrossRef PubMed Google Scholar

[12] Zhu Y, Lv HP, Shao CY, Kang S, Zhang Y, Guo L, Dai WD, Tan JF, Peng QH, Lin Z. Food Res Int, 2018, 108: 74-82 CrossRef PubMed Google Scholar

[13] Chen Q, Zhao J, Chen Z, Lin H, Zhao DA. Sens Actuat B-Chem, 2011, 159: 294-300 CrossRef Google Scholar

[14] Wu N, Wang XC, Tao NP, Ni YQ. Fisheries Sci, 2016, 82: 1–11. Google Scholar

[15] Zhu JC, Chen F, Wang LY, Niu YW, Yu D, Shu C, Chen HX, Wang HL, Xiao ZB. J Agric Food Chem, 2015, 63: 7499-7510 CrossRef PubMed Google Scholar

[16] Yang YQ, Yin HX, Yuan HB, Jiang YW, Dong CW, Deng YL. PLoS ONE, 2018, 13: e0193393 CrossRef PubMed ADS Google Scholar

[17] Song W, Wang H, Maguire P, Nibouche O. Anal Chim Acta, 2018, 1009: 27-38 CrossRef PubMed Google Scholar

[18] Li HD, Xu QS, Liang YZ. Chemom Intell Lab Syst, 2018, 176: 34-43 CrossRef Google Scholar

[19] Bevilacqua M, Marini F. Anal Chim Acta, 2014, 838: 20-30 CrossRef PubMed Google Scholar

[20] Fu HY, Li HD, Xu L, Yin QB, Yang TM, Ni C, Cai CB, Yang J, She YB. Food Chem, 2017, 227: 322-328 CrossRef PubMed Google Scholar

[21] Fu HY, Li HD, Yu YJ, Wang B, Lu P, Cui HP, Liu PP, She YB. J Chromatogr A, 2016, 1449: 89-99 CrossRef PubMed Google Scholar

[22] Fu HY, Guo JW, Yu YJ, Li HD, Cui HP, Liu PP, Wang B, Wang S, Lu P. J Chromatogr A, 2016, 1452: 1-9 CrossRef PubMed Google Scholar

[23] Zheng QX, Fu HY, Li HD, Wang B, Peng CH, Wang S, Cai JL, Liu SF, Zhang XB, Yu YJ. Sci Rep, 2017, 7: 256 CrossRef PubMed ADS Google Scholar

[24] Jiang JH, Berry RJ, Siesler HW, Ozaki Y. Anal Chem, 2002, 74: 3555-3565 CrossRef Google Scholar

[25] Liu J, Chen DS, Shen JF. Ind Eng Chem Res, 2016, 49: 11530-11546 CrossRef Google Scholar

[26] Zou HY, Wu HL, Fu HY, Tang LJ, Xu L, Nie JF, Yu RQ. Talanta, 2010, 80: 1698-1701 CrossRef PubMed Google Scholar

[27] van Gemert LJ. Compilations of Odour Threshold Values in Air, Water and Other Media. Zeist: Oliemans Punter & Partners BV, 2003. 28. Google Scholar

[28] Ntlhokwe G, Tredoux AGJ, Górecki T, Edwards M, Vestner J, Muller M, Erasmus L, Joubert E, Christel Cronje J, de Villiers A. Anal Bioanal Chem, 2017, 409: 4127-4138 CrossRef PubMed Google Scholar

[29] Javidnia K, Miri R, Jamalian A. Flavour Fragr J, 2005, 20: 542-543 CrossRef Google Scholar

  • Figure 1

    The experimental method for investigating the relationship between the aroma components and the aroma quality of the famous green teas (color online).

  • Figure 2

    Radar diagram of E-nose sensor response of the famous green teas (color online).

  • Figure 3

    PLSDA virtual coded sample class attribution map based on the E-nose sensor data set of the famous green teas. (a) 58 training set samples of the E-nose-PLSDA model. (b) 22 prediction set samples of the E-nose-PLSDA model (color online).

  • Figure 4

    (a) Original TIC map of GC-MS for 8 famous green teas. (b) TIC map of GC-MS for 8 famous green teas corrected by local minimum value combined with robust statistical analysis (LVM-RSA) (color online).

  • Figure 5

    (a) GC-MS multi-scale Gaussian smoothing peak extraction map of Premium Yunwu Maojian tea corrected by background drift; GC-MS chromatogram of the famous green teas before (b) and after (c) time drift correction (color online).

  • Figure 6

    GC-MS variable screening residual error map simulated by MWPLSR for predicting the aroma quality of the famous green teas in training set (color online).

  • Figure 7

    The variable weight map of potential aroma substances optimized by PSO-VWLS-SVM in famous green tea samples (color online).

  • Table 1   The details and sensory evaluation results of 8 famous green teas, and the training set and prediction set table partitioned by E-nose

    样品种类

    编号

    等级品种

    香气评语

    平均得分±

    标准差

    训练集

    预测集

    样品数

    样品编号

    样品数

    样品编号

    GT01

    特级二等

    碧螺春

    清高持久

    花果香

    92±0.6

    9

    1st~9th

    1

    1st

    GT02

    特级

    碧螺春

    陈味

    尚纯(带异气)

    80±0.5

    6

    10th~15th

    4

    2nd~5th

    GT03

    论道级

    竹叶青

    嫩香清幽

    95±0.2

    8

    16th~23rd

    2

    6th~7th

    GT04

    青宇心级

    竹叶青

    清香

    (稍带青气)

    91±0.5

    8

    24th~31st

    2

    8th~9th

    GT05

    品味级

    竹叶青

    清嫩香

    (稍带青气)

    90±0.5

    6

    32nd~37th

    4

    10th~13th

    GT06

    二级

    都匀毛尖

    尚浓纯

    (火工香)

    86±0.6

    8

    38th~45th

    2

    14th~15th

    GT07

    特级

    都匀毛尖

    香浓

    栗香明显

    93±0.3

    5

    46th~50th

    5

    16th~20th

    GT08

    二级

    云雾毛尖

    陈味

    尚纯(带烟气)

    80±0.5

    8

    51st~58th

    2

    21st~22nd

  • Table 2   Contents, related aroma parameters and potential aroma ingredients contribution of 21 kinds of components in the famous green teas

    香气

    物质

    香型

    描述[9]

    OAV[27]阈值(μg/L)

    保留指数(实测值/理论值[28,29])

    GT01

    (μg/L)

    GT02

    (μg/L)

    GT03

    (μg/L)

    GT04

    (μg/L)

    GT05

    (μg/L)

    GT06

    (μg/L)

    GT07

    (μg/L)

    GT08

    (μg/L)

    OAV法计算香气物质贡献值/含量

    异丁醛

    鲜嫩花、苹果香

    1.2

    723/

    722

    217.72/261.3

    314.86/377.8

    176.86/212.2

    226.24/271.5

    175.58/210.7

    451.84/542.2

    124.72/149.7

    乙酸乙酯

    水果、甜花香

    5.0

    887/

    889

    112.35/561.7

    32.32/

    161.6

    85.10/

    425.5

    61.82/

    309.1

    147.02/735.1

    1.56/

    7.8

    82.87/

    414.4

    2-甲基丁醛

    靑香

    4.4

    908/

    911

    14.04/

    61.8

    49.21/

    216.5

    42.04/

    185.0

    62.90/

    276.8

    37.69/

    165.8

    112.44/494.7

    17.65/

    77.6

    12.57/

    55.3

    戊醛

    杏仁香、刺激性

    12

    805/–

    0.93/

    11.2

    1.00/

    12.0

    乙酸丁酯

    果香

    100

    1059/

    1057

    0.03/

    2.8

    0.09/

    8.7

    0.11/

    10.7

    正己醛

    青草、

    靑香

    4.5

    1052/

    1054

    0.85/

    3.8

    4.25/

    19.1

    0.47/

    2.1

    乙酸异戊酯

    鲜嫩、香蕉味

    30

    1167/

    1168

    0.55/

    16.6

    0.66/

    19.8

    1.02/

    30.5

    0.64/

    19.2

    1-戊烯-3-醇

    果甜香

    400

    1132/

    1133

    1.85/

    738.3

    1.25/

    499.8

    3.01/

    1203.2

    3.52/

    1408.8

    1.99/

    795.2

    0.88/

    351.0

    2.75/

    1098.1

    1.85/

    741.4

    桉叶油素

    樟脑香

    1023/

    1022

    –/48.3

    –/8.5

    –/32.1

    –/15.1

    –/7.6

    异戊醇

    青草香

    4000

    1212/

    1208

    0.01/

    26.7

    0.09/

    342.7

    0.02/

    92.0

    0.07/

    282.4

    0.06/

    222.8

    0.08/

    335.1

    0.04/

    162.1

    正己醇

    水果香、香蕉味

    500

    1353/

    1352

    0.08/

    40.5

    0.05/

    25.7

    0.15/

    76.8

    0.06/

    30.2

    0.08/

    31.2

    0.04/

    17.4

    –/2.2

    顺-3-己烯-1-醇

    青香

    70

    859/

    858

    2.11/

    148.0

    3.00/

    209.9

    5.22/

    365.7

    26.43/

    1849.8

    2.62/

    183.2

    2.09/

    146.2

    1.47/

    102.9

    0.15/

    10.8

    苯甲醛

    焦甜、

    微苦

    350

    1512/

    1515

    0.02/

    7.9

    0.10/

    35.0

    0.18/

    62.8

    芳樟醇

    花、靑香

    6.0

    1104/

    1101

    31.64/

    189.9

    22.39/

    134.3

    16.84/

    101.0

    7.30/

    43.8

    15.42/

    92.5

    糠醇

    焦糖香

    4500

    1664/

    1662

    –/

    17.4

    0.01/

    25.8

    0.00/

    10.0

    0.01/

    24.8

    –/18.0

    0.01/

    33.2

    乙酸苄酯

    花、果香

    270

    1474/

    1470

    0.13/

    34.7

    0.15/

    40.2

    柳酸甲酯

    药草香

    40

    1022/–

    0.08/

    3.2

    0.14/

    5.7

    0.03/

    1.0

    苯甲醇

    花、甜香

    0.1

    1885/

    1889

    985.08/98.5

    834.54/

    83.5

    504.90/50.5

    1115.29/111.5

    451.47/45.1

    137.90/13.8

    468.32/46.8

    215.67/21.6

    苯乙醇

    花香

    750

    1310/

    1314

    0.11/

    82.1

    0.07/

    49.8

    0.04/

    28.3

    0.06/

    48.3

    0.09/

    68.4

    0.05/

    34.0

    0.04/

    27.8

    0.03/

    25.6

    2,4-二叔丁基酚

    1511/

    1508

    –/175.4

    –/459.5

    –/257.7

    –/834.8

    –/323.0

    –/722.6

    –/261.3

    –/637.9

    吲哚

    花果香

    0.04

    1297/

    1295

    673.72/26.9

    338.89/13.6

  • Table 3   The aroma quality scores of the famous green teas predicted by the constructed PSO-VWLS-SVM model

    预测样品

    真实值

    预测值

    相对误差 (%)

    准确率 (%)

    1

    90

    90.02

    0.03

    100.03

    2

    86

    85.97

    0.04

    99.96

    3

    93

    92.95

    0.05

    99.95

    4

    80

    80.02

    0.03

    100.03

    5

    80

    79.96

    0.05

    99.95

    6

    95

    94.99

    0.01

    99.99

    7

    80

    80.21

    0.26

    100.26

    8

    92

    91.87

    0.14

    99.86

    9

    80

    82.22

    2.77

    102.77

    10

    95

    94.88

    0.13

    99.87

    11

    90

    89.86

    0.16

    99.84

    12

    80

    80.25

    0.32

    100.32

    平均准确率(%)

    100.23±0.81

     

    校正均方根误差

    0.0920

     

    预测均方根误差

    0.6513

     

    T(t-test)t0.0511=2.201

    1.0007<2.201

     

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