福建寿宁高山红茶矿质元素和稀土元素特征分析

Minerals and Rare-earth Elements in Alpine Black Tea from Shouning,Fujian

【目的】探明福建寿宁高山红茶的矿质元素和稀土元素的含量特征及变化规律,为福建高山红茶的品质特征研究及高山红茶良种的推广应用提供参考依据。【方法】以60份不同等级寿宁高山金牡丹红茶为研究对象,分别进行感官审评、矿质元素和稀土元素含量测定,并结合化学计量学方法分析其化学元素差异特征,构建高山红茶等级判别模型。【结果】15种矿质元素含量由高到依次为钾(K)>钙(Ca)>镁(Mg)>锰(Mn)>铝(Al)>铁(Fe)>钠(Na)>锌(Zn)>铜(Cu)>铅(Pb)>锡(Sn)>硒(Se)>砷(As)>镉(Cd)>汞(Hg),其中,K、Ca、Mg、Mn在寿宁高山红茶样品中含量丰富,占矿质元素总量的96.78%;15种稀土元素含量由高到依次为铈(Ce)>镧(La)>钇(Y)>钕(Nd)>钆(Gd)>镨(Pr)>镝(Dy)>铒(Er)>镱(Yb)>钐(Sm)>铕(Eu)>钬(Ho)>镥(Lu)>铽(Tb)>铥(Tm),其中,寿宁高山红茶Ce、La、Y、Nd含量较高,占稀土总量的82.44%。单因素方差分析结合受试者工作特征曲线共筛选出Cu、Al、Sn、Ce、Tm、Lu、Se、Mn、Dy、Ho、Gd、Tb、Er和Yb等14种特征元素(P<0.05,AUC>0.7)。基于此14种元素含量构建多层感知神经网络判别模型,训练组和检验组判别率分别为90.48%和94.44%。【结论】寿宁高山金牡丹红茶K、Ca、Mg、Mn等矿质元素含量丰富,基于化学元素含量构建多元统计分类模型可以有效地区分红茶样品等级。

【Objective】Contents of minerals and rare-earth elements in the alpine black teas from Shouning,Fujian were analyzed for grade classification.【Methods】On 60 Jinmudan alpine black tea of various grades,sensory evaluation and chemical analysis were performed.A chemometric method was applied on the data to construct a grade discrimination model for the tea.【Results】Based on the analytical data,15 minerals detected in the specimens were ranked by their contents as K>Ca>Mg>Mn>Al>Fe>Na>Zn>Cu>Pb>Sn>Se>As>Cd>Hg.Among them,K,Ca,Mg,and Mn were more abundant and accounted for 96.78%of the total.The rank on the 15 rare-earth elements was Ce>La>Y>Nd>Gd>Pr>Dy>Er>Yb>Sm>Eu>Ho>Lu>Tb>Tm,with Ce,La,Y,and Nd being the dominant at contribution rate of 82.44%.For constructing a grading model,14 characteristic elements,including Cu,Al,Sn,Ce,Tm,Lu,Se,Mn,Dy,Ho,Gd,Tb,Er,and Yb,were selected by the analysis of variance(ANOVA)combined with receiver operating characteristic curve(ROC curve)(P<0.05,AUC>0.7).A discriminant model of multi-layer perceptual neural network with a discriminant rate of 90.48%on the training group and 94.44%on the test group was established.【Conclusion】K,Ca,Mg,Mn,and other minerals were richly found in the Shouning Jinmudan alpine black teas.The constructed multivariate statistical model based on the chemical composition could be used to classify the grades of the teas.