不同鲜叶嫩度名优绿茶氨基酸组分差异分析
|
摘要
为通过不同鲜叶嫩度名优绿茶氨基酸组分差异分析,实现分类判别,采用氨基酸分析仪对单芽、1芽1叶、1芽2叶的名优绿茶样品进行游离氨基酸组分测定,通过主成分分析(principal components analysis,PCA)和贝叶斯(Bayes)判别分析法进行判别分析。结果表明:不同鲜叶嫩度名优绿茶氨基酸组分构成存在各自的典型特征,以磷酸丝氨酸、磷酸乙醇胺、天冬氨酸、苏氨酸、天冬酰胺、谷氨酸、茶氨酸、异亮氨酸、亮氨酸、苯丙氨酸、赖氨酸、精氨酸、脯氨酸含量为因子通过Bayes判别能较好进行区分。依据茶样中氨基酸组分含量,通过结合PCA和Bayes判别分析法能够实现对不同鲜叶嫩度名优绿茶的有效判别。
Based on the analysis of amino acid components of famous green tea with different fresh leaf tenderness,a classification and discrimination were realized. The free amino acid components of famous green tea samples of single bud,bud with one leaf,two leaves were determined by automatic amino acid analyzer,and the discriminant analysis was carried out by principal component analysis(PCA)and Bayes discriminant analysis. The results showed that amino acid components of green tea with different tenderness of fresh leaves had their own typical characteristics. Tea samples with different tenderness of fresh leaves could be distinguished through the Bayes discriminant analysis when serine phosphate,ethanolamine phosphate,aspartic acid,threonine,asparagine,glutamic acid,theanine,isoleucine,phenylalanine,lysine,arginine,proline were chosen as factors. According to the content of amino acid in tea samples,combined with PCA and Bayes discriminant analysis,the green tea with different fresh leaf tenderness could be distinguished effectively.
Based on the analysis of amino acid components of famous green tea with different fresh leaf tenderness,a classification and discrimination were realized. The free amino acid components of famous green tea samples of single bud,bud with one leaf,two leaves were determined by automatic amino acid analyzer,and the discriminant analysis was carried out by principal component analysis(PCA)and Bayes discriminant analysis. The results showed that amino acid components of green tea with different tenderness of fresh leaves had their own typical characteristics. Tea samples with different tenderness of fresh leaves could be distinguished through the Bayes discriminant analysis when serine phosphate,ethanolamine phosphate,aspartic acid,threonine,asparagine,glutamic acid,theanine,isoleucine,phenylalanine,lysine,arginine,proline were chosen as factors. According to the content of amino acid in tea samples,combined with PCA and Bayes discriminant analysis,the green tea with different fresh leaf tenderness could be distinguished effectively.
引文
[1]张丹丹,叶小辉,赵峰,等.基于游离氨基酸组分的白茶滋味品质研究[J].福建农业学报,2016,31(5):515-520
[2]赵璇,李新生,韩豪,等.汉中茶叶氨基酸含量测定及营养价值评价分析[J].氨基酸和生物资源,2016,38(1):17-23
[3]刘盼盼,邓余良,尹军峰,等.绿茶滋味量化及其与化学组分的相关性研究[J].中国食品学报,2014,14(12)173-181
[4]范培珍,薄晓培,王梦馨,等.4个等级内山六安瓜片茶叶氨基酸的组成及差异[J].安徽农业大学学报,2017,44(1):14-21
[5]Alcázar A,Ballesteros O,Jurado J M,et al.Differentiation of green,white,black,Oolong,and Pu-erh teas according to their free amino acids content[J].Journal of Agricultural and Food Chemistry,2007,55(15):5960-5965
[6]陈然,孟庆佳,刘海新,等.不同种类茶叶游离氨基酸组分差异分析[J].食品科技,2017,42(6):258-263
[7]赵明明,金钰,周有祥.基于氨基酸指纹图谱的扁形绿茶品种分析[J].湖北农业科学,2018,57(22):120-123
[8]周有祥,赵明明,丁华,等.茶叶中氨基酸特征指纹图谱的初步研究[J].湖北农业科学,2012,51(23):5463-5466
[9]邱晓红,张丹丹,陈静,等.基于HPLC和模式识别方法的福建水仙茶产地判别分析[J].福建农林大学学报(自然科学版),2017,46(3):260-264
[10]龚自明,王雪萍,高士伟,等.矿物元素分析判别绿茶产地来源研究[J].四川农业大学学报,2012,30(4):429-433
[11]王洁,石元值,张群峰,等.基于矿物元素指纹的龙井茶产地溯源[J].核农学报,2017,31(3):547-558
[12]刘宏程,林昕,和丽忠,等.基于稀土元素含量的普洱茶产地识别研究[J].茶叶科学,2014,34(5):451-457
[13]王洁,伊晓云,倪康,等.基于稀土元素指纹的扁形茶产地判别分析[J].浙江农业科学,2016,57(7):1118-1124
[14]张丹丹,韦航,邱晓红,等.基于质子转移反应-飞行时间质谱快速鉴别不同产地闽北水仙茶[J].分析化学,2017(6):914-921
[15]马会杰,蒋宾,潘玉兰,等.电子鼻对不同产地名优绿茶和红茶香气特征的判别研究[J].食品科技,2019,44(1):336-344
[16]刘志,张永志,周铁锋,等.不同烘干方式对茶叶中稳定同位素特征及其产地溯源的影响[J].核农学报,2018,32(7):1408-1416
[17]王胜鹏,龚自明,高士伟,等.近红外光谱结合神经网络判别茶鲜叶产地[J].中国计量学院学报,2014,25(4):419-423
[18]张龙,潘家荣,朱诚.基于近红外光谱的西湖龙井茶产地的精细判别[J].红外,2015,36(12):41-46
[19]McKenzie J S,Jurado J M,de Pablos F.Characterisation of tea leaves according to their total mineral content by means of probabilistic neural networks[J].Food Chemistry,2010,123(3):859-864
[20]吴全金,董青华,孙威江,基于傅里叶红外光谱的多茶类判别研究[J].茶叶科学,2014,34(1):63-70
[21]陈美丽,唐德松,张颖彬,等.GC-MS结合化学计量学对茶叶品质的判别研究[J].浙江大学学报(农业与生命科学版),2013,39(1):84-91
[22]刘洋,余天星,李明玺,等.基于近红外光谱技术的信阳毛尖品质判别研究[J].现代食品科技,2018,34(8):225-230
[23]陈婷,蒋明忠,彭文,等.基于电子鼻技术对云南普洱熟茶的香气品质判别[J].西南农业大学学报,2017,30(2):339-344
[24]王胜鹏,郑鹏程,龚自明,等.基于近红外光谱技术的茶鲜叶海拔高度判别模型建立[J].华中农业大学学报,2018,37(1):89-94
[2]赵璇,李新生,韩豪,等.汉中茶叶氨基酸含量测定及营养价值评价分析[J].氨基酸和生物资源,2016,38(1):17-23
[3]刘盼盼,邓余良,尹军峰,等.绿茶滋味量化及其与化学组分的相关性研究[J].中国食品学报,2014,14(12)173-181
[4]范培珍,薄晓培,王梦馨,等.4个等级内山六安瓜片茶叶氨基酸的组成及差异[J].安徽农业大学学报,2017,44(1):14-21
[5]Alcázar A,Ballesteros O,Jurado J M,et al.Differentiation of green,white,black,Oolong,and Pu-erh teas according to their free amino acids content[J].Journal of Agricultural and Food Chemistry,2007,55(15):5960-5965
[6]陈然,孟庆佳,刘海新,等.不同种类茶叶游离氨基酸组分差异分析[J].食品科技,2017,42(6):258-263
[7]赵明明,金钰,周有祥.基于氨基酸指纹图谱的扁形绿茶品种分析[J].湖北农业科学,2018,57(22):120-123
[8]周有祥,赵明明,丁华,等.茶叶中氨基酸特征指纹图谱的初步研究[J].湖北农业科学,2012,51(23):5463-5466
[9]邱晓红,张丹丹,陈静,等.基于HPLC和模式识别方法的福建水仙茶产地判别分析[J].福建农林大学学报(自然科学版),2017,46(3):260-264
[10]龚自明,王雪萍,高士伟,等.矿物元素分析判别绿茶产地来源研究[J].四川农业大学学报,2012,30(4):429-433
[11]王洁,石元值,张群峰,等.基于矿物元素指纹的龙井茶产地溯源[J].核农学报,2017,31(3):547-558
[12]刘宏程,林昕,和丽忠,等.基于稀土元素含量的普洱茶产地识别研究[J].茶叶科学,2014,34(5):451-457
[13]王洁,伊晓云,倪康,等.基于稀土元素指纹的扁形茶产地判别分析[J].浙江农业科学,2016,57(7):1118-1124
[14]张丹丹,韦航,邱晓红,等.基于质子转移反应-飞行时间质谱快速鉴别不同产地闽北水仙茶[J].分析化学,2017(6):914-921
[15]马会杰,蒋宾,潘玉兰,等.电子鼻对不同产地名优绿茶和红茶香气特征的判别研究[J].食品科技,2019,44(1):336-344
[16]刘志,张永志,周铁锋,等.不同烘干方式对茶叶中稳定同位素特征及其产地溯源的影响[J].核农学报,2018,32(7):1408-1416
[17]王胜鹏,龚自明,高士伟,等.近红外光谱结合神经网络判别茶鲜叶产地[J].中国计量学院学报,2014,25(4):419-423
[18]张龙,潘家荣,朱诚.基于近红外光谱的西湖龙井茶产地的精细判别[J].红外,2015,36(12):41-46
[19]McKenzie J S,Jurado J M,de Pablos F.Characterisation of tea leaves according to their total mineral content by means of probabilistic neural networks[J].Food Chemistry,2010,123(3):859-864
[20]吴全金,董青华,孙威江,基于傅里叶红外光谱的多茶类判别研究[J].茶叶科学,2014,34(1):63-70
[21]陈美丽,唐德松,张颖彬,等.GC-MS结合化学计量学对茶叶品质的判别研究[J].浙江大学学报(农业与生命科学版),2013,39(1):84-91
[22]刘洋,余天星,李明玺,等.基于近红外光谱技术的信阳毛尖品质判别研究[J].现代食品科技,2018,34(8):225-230
[23]陈婷,蒋明忠,彭文,等.基于电子鼻技术对云南普洱熟茶的香气品质判别[J].西南农业大学学报,2017,30(2):339-344
[24]王胜鹏,郑鹏程,龚自明,等.基于近红外光谱技术的茶鲜叶海拔高度判别模型建立[J].华中农业大学学报,2018,37(1):89-94