8种名优绿茶香气品质与其成分间协同定量组效关系研究
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摘要
本文首先利用电子鼻(E-nose)结合偏最小二乘判别分析(PLSDA)成功判别了8种不同种类和等级的名优绿茶的香气品质.为进一步解释香气质量差异,借助局部极小值背景漂移校正、多尺度高斯平滑以及色谱保留时间校正法对名优绿茶的气相色谱-质谱联用(GC-MS)指纹图谱进行预处理,再利用移动窗口偏最小二乘回归(MWPLSR)将预处理后的指纹图谱与香气品质得分构建谱效关系模型,筛选出21种潜在特征香气物质,最后利用变量加权最小二乘支持向量机(PSO-VWLS-SVM)将特征香气物质的含量与不同绿茶香气质量得分相关联,根据各特征香气物质的贡献率,成功揭示了名优绿茶香气物质与香气品质之间的协同量-组效关系.本文提出的方法为绿茶特征香气品质标志物的筛查和其量效-组效关系研究提供了一种新的策略方法.
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.
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.
引文
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3 Miyazawa T,Gallagher M,Preti G,Wise PM.Chem Percept,2008,1:163-167
4 Pignitter M,Stolze K,Jirsa F,Gille L,Goodman BA,Somoza V.J Agric Food Chem,2015,63:8519-8526
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7 Cheng Y,Huynh-Ba T,Blank I,Robert F.J Agric Food Chem,2008,56:2160-2169
8 Wang J,Wei ZB.RSC Adv,2015,5:106959-106970
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11 Wang C,Lv S,Wu Y,Lian M,Gao X,Meng Q.J Sci Food Agric,2016,96:4492-4498
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13 Chen Q,Zhao J,Chen Z,Lin H,Zhao DA.Sens Actuat B-Chem,2011,159:294-300
14 Wu N,Wang XC,Tao NP,Ni YQ.Fisheries Sci,2016,82:1-11
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17 Song W,Wang H,Maguire P,Nibouche O.Anal Chim Acta,2018,1009:27-38
18 Li HD,Xu QS,Liang YZ.Chemom Intell Lab Syst,2018,176:34-43
19 Bevilacqua M,Marini F.Anal Chim Acta,2014,838:20-30
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
21 Fu HY,Li HD,Yu YJ,Wang B,Lu P,Cui HP,Liu PP,She YB.J Chromatogr A,2016,1449:89-99
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
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
24 Jiang JH,Berry RJ,Siesler HW,Ozaki Y.Anal Chem,2002,74:3555-3565
25 Liu J,Chen DS,Shen JF.Ind Eng Chem Res,2016,49:11530-11546
26 Zou HY,Wu HL,Fu HY,Tang LJ,Xu L,Nie JF,Yu RQ.Talanta,2010,80:1698-1701
27 van Gemert LJ.Compilations of Odour Threshold Values in Air,Water and Other Media.Zeist:Oliemans Punter&Partners BV,2003.28
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
29 Javidnia K,Miri R,Jamalian A.Flavour Fragr J,2005,20:542-543