青砖茶茶汤滋味成分分析及品质评价模型建立
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摘要
以不同厂家生产的青砖茶为研究对象,在对茶汤滋味进行感官审评后,测定内含成分含量,利用主成分分析法和相关系数法筛选代表性内含成分,最后结合主成分回归法和逐步回归法建立茶汤滋味品质评价回归方程,并用未知样品检验回归方程预测效果。结果表明,筛选出5个与茶汤滋味品质密切相关的内含成分(P<0.05),按照对茶汤滋味的贡献大小,依次为表没食子儿茶素没食子酸酯、儿茶素总量、茶多酚、表儿茶素没食子酸酯和表没食子儿茶素;以逐步回归方法建立的茶汤滋味品质评价方程预测结果最佳(校正集决定系数0.973 5,交互验证均方根误差0.380 7;验证集决定系数0.968 1,预测均方根误差0.400 0);以逐步回归方程对未知样品的预测结果最佳(验证集决定系数0.974 6,预测均方根误差0.391 5)。结果表明,应用化学计量学方法建立拟合方程实现对青砖茶茶汤滋味品质的准确、可靠预测,为青砖茶茶汤滋味品质评价提供一种新的参考方法。
In the present experiment, Qingzhuan tea samples from different manufacturers were used. Taste sensory evaluation of the tea infusions was carried out, followed by chemical measurement. Then, representative chemical components were selected by principal component analysis(PCA) and the correlation coefficient method. Finally, a regression equation for taste quality evaluation of tea infusion was established by principal component regression(PCR) or stepwise regression(SR), and the predictive performance of the regression equation was tested by applying it to unknown samples. The results showed that five components were found to be closely related to the taste quality of tea infusion(P < 0.05),whose contributions to the taste of tea infusion were in the descending order of EGCG, total catechins, tea polyphenols, ECG and EGC. The SR model was better than the PCR model, and the correlation coefficient of calibration(R_c~2), root mean square error of cross-validation(RMSECV), correlation coefficient of prediction(R_p~2) and root mean square error of prediction(RMSEP) of the SR model were 0.973 5, 0.380 7, 0.968 1 and 0.400 0, respectively. The SR model showed better predictive performance for the unknown samples(R_p~2 = 0.974 6, and RMSEP = 0.391 5). The results showed that the chemometric model was able to predict the taste quality of Qingzhuan brick tea accurately and reliably and could provide a new method for evaluating the taste quality of Qingzhuan tea infusion.
In the present experiment, Qingzhuan tea samples from different manufacturers were used. Taste sensory evaluation of the tea infusions was carried out, followed by chemical measurement. Then, representative chemical components were selected by principal component analysis(PCA) and the correlation coefficient method. Finally, a regression equation for taste quality evaluation of tea infusion was established by principal component regression(PCR) or stepwise regression(SR), and the predictive performance of the regression equation was tested by applying it to unknown samples. The results showed that five components were found to be closely related to the taste quality of tea infusion(P < 0.05),whose contributions to the taste of tea infusion were in the descending order of EGCG, total catechins, tea polyphenols, ECG and EGC. The SR model was better than the PCR model, and the correlation coefficient of calibration(R_c~2), root mean square error of cross-validation(RMSECV), correlation coefficient of prediction(R_p~2) and root mean square error of prediction(RMSEP) of the SR model were 0.973 5, 0.380 7, 0.968 1 and 0.400 0, respectively. The SR model showed better predictive performance for the unknown samples(R_p~2 = 0.974 6, and RMSEP = 0.391 5). The results showed that the chemometric model was able to predict the taste quality of Qingzhuan brick tea accurately and reliably and could provide a new method for evaluating the taste quality of Qingzhuan tea infusion.
引文
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[2]李世刚,郑倩倩,何建刚,等.湖北青砖茶对IBS-D模型大鼠肠道敏感性的影响[J].茶叶科学,2016,36(3):245-249.DOI:10.13305/j.cnki.jts.2016.03.003.
[3]YANG X H,HUANG M J,QIN C Q,et al.Structural characterization and evaluation of the antioxidant activities of polysaccharides extracted from Qingzhuan brick tea[J].International Journal of Biological Macromolecules,2017,101:768-775.DOI:10.1016/j.ijbiomac.2017.03.189.
[4]陈玉琼,张伟,倪德江,等.湖北青砖茶辅助降血脂作用及其抗氧化效果[J].茶叶科学,2010,30(2):124-128.DOI:10.13305/j.cnki.jts.2010.02.013.
[5]郑鹏程,龚自明,王胜鹏,等.青砖茶加工工艺及展望[J].中国茶叶加工,2017(2):46-49.DOI:10.15905/j.cnki.33-1157/ts.2017.02.008.
[6]刘盼盼,郑鹏程,龚自明,等.青砖茶的香气成分分析[J].食品科学,2017,38(8):164-170.DOI:10.7506/spkx1002-6630-201708026.
[7]郑鹏程,谭荣荣,刘盼盼,等.青砖茶渥堆过程中真菌种类及品质变化研究[J].食品科技,2017,42(11):22-26.DOI:10.13684/j.cnki.spkj.2017.11.005.
[8]唐飞,艾于杰,张善明,等.不同年份青砖茶改善小鼠胃肠道功能的研究[J].华中农业大学学报,2018,38(1):82-88.DOI:10.13300/j.cnki.hnlkxb.2018.01.013.
[9]国家标准化管理委员会.茶叶感官审评方法:GB/T 23776-2009[S].北京:中国标准出版社,2009.
[10]KUCEK L K,DYCK E,RUSSELL J,et al.Evaluation of wheat and emmer varieties for artisanal baking,pasta making,and sensory quality[J].Journal of Cereal Science,2017,74:19-27.DOI:10.1016/j.jcs.2016.12.010.
[11]TIPARAT T,ROBERT J,HEATHER S.Evaluating the sensory properties of unpolished Australian wild rice[J].Food Research International,2018,103:406-414.DOI:10.1016/j.foodres.2017.10.037.
[12]MAARIA K,SIRLI R,OSKAR L,et al.Sensory and chemical profiles of finnish honeys of different botanical origins and consumer preferences[J].Food Chemistry,2018,246:351-359.DOI:10.1016/j.foodchem.2017.10.069.
[13]HAN X,JIANG H,HAN L,et al.A novel quantified bitterness evaluation model for traditional Chinese herbs based on an animal ethology principle[J].Acta Pharmaceutica Sinica B,2018,8(2):209-217.DOI:10.1016/j.apsb.2017.08.001.
[14]ZHU H K,YE Y,HE H F,et al.Evaluation of green tea sensory quality via process characteristics and image information[J].Food and Bioproducts Processing,2017,102:116-122.DOI:10.1016/j.fbp.2016.12.004.
[15]WANG C,ZHANG C X,KONG Y W,et al.A comparative study of volatile components in Dianhong teas from fresh leaves of four tea cultivars by using chromatography-mass spectrometry,multivariate data analysis,and descriptive sensory analysis[J].Food Research International,2017,100(Part1):267-275.DOI:10.1016/j.foodres.2017.07.013.
[16]ZHU J C,CHEN F,WANG L Y,et al.Evaluation of the synergism among volatile compounds in Oolong tea infusion by odour threshold withsensory analysis and E-nose[J].Food Chemistry,2017,221:1484-1490.DOI:10.1016/j.foodchem.2016.11.002.
[17]CHEN Y S,LIU B L,CHANG Y N.Bioactivities and sensory evaluation of Pu-erh teas made from three tea leaves in an improved pile fermentation process[J].Journal of Bioscience and Bioengineering,2010,109(6):557-563.DOI:10.1016/j.jbiosc.2009.11.004.
[18]RUDNITSKAYA A,POLSHIN E,KIRSANOV D,et al.Instrmental measurement of beer taste attributes using an electronic tongue[J].Analytica Chimica Acta,2009,646(1/2):111-118.DOI:10.1016/j.aca.2009.05.008.
[19]LIANG Y R,YE Q,JIN J,et al.Chemical and instrmental assessment of green tea sensory preference[J].International Journal of Food Properties,2008,11:258-272.DOI:10.1080/10942910701299430.
[20]国家标准化管理委员会.茶水浸出物测定:GB/T 8305-2013[S].北京:中国标准出版社,2013.
[21]国家标准化管理委员会.茶叶中茶多酚和儿茶素类含量的检测方法:GB/T 8313-2008[S].北京:中国标准出版社,2008.
[22]国家标准化管理委员会.茶游离氨基酸总量的测定:GB/T 8314-2013[S].北京:中国标准出版社,2013.
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[24]钟萝.茶叶品质理化分析[M].上海:上海科学技术出版社,1989:250-350.
[25]ROBERTS E A H,SMITH R F.Spectrophotometric measurements of theaflavins and thearubigins in black tea liquors in assessments of quality in teas[J].Analyst,1961,86(10):94-98.DOI:10.1039/an9618600094.
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[27]SHAHDOOSTI H R,GHASSEMIAN H.Combining the spectral PCAand spatial PCA fusion methods by an optimal filter[J].Information Fusion,2016,27:150-160.DOI:10.1016/j.inffus.2015.06.006.
[28]NORGAARD L,SAUDLAND A,WAGNER J,et al.Interval partial least squares regression(iPLS):a comparative chemometric study with an example from near-infrared spectroscopy[J].Applied Spectroscopy,2000,54:413-419.DOI:10.1366/0003702001949500.
[29]宛晓春.茶叶生物化学[M].北京:中国农业出版社,2003.