基于Python/MATLAB的常见茶叶中重金属元素的ICP-MS检验研究

为了实现以重金属元素含量作为识别茶叶厂家和产地的指标,选取市面上常见的红茶、绿茶、铁观音三类茶叶共计37种,经过前处理后采用电感耦合等离子体质谱仪(ICP-MS)同时对37种茶叶进行五种常见重金属铬(Cr)、砷(As)、铅(Pb)、镉(Cd)和汞(Hg)含量的检测,使用MATLAB进行主成分分析和聚类分析,使用Python设计识别界面实现测试样本的分类识别。结果表明:市售常用茶叶中Cr、As、Cd、Pb四种重金属平均含量分别为1.83 ng/mL、1.09 ng/mL、0.10 ng/mL和2.61 ng/mL,均低于国家规定限量,且未检测到重金属Hg;主成分分析法和聚类分析法对实验数据进行分析可以很好地区分37种茶叶的厂家和产地;Python识别界面可对未知茶叶的种类进行识别并得知其产地。

ICP-MS Detection of Heavy Metals in Common Tea Based on Python/MATLAB

In order to use the content of the heavy metal elements as an index to identify tea manufacturers and producing areas,this study selected 37 kinds of common black tea,green tea and Tieguanyin tea on the market,after pretreatment,determined the contents of five kinds of common heavy metals of chromium(Cr),arsenic(As),lead(Pb),cadmium(Cd)and mercury(Hg)by inductively coupled plasma mass spectrometry(ICP-MS)at the same time,used MATLAB to do the principal component analysis and cluster analysis,and Python to design the identification interface to realize the classification of test samples.The results showed that the average contents of Cr,As,Cd and Pb in common tea sold on the market were 1.83ng/mL,1.09 ng/mL,0.1 ng/mL and 2.61 ng/mL respectively,all lower than the national limit,and no heavy metal Hg was detected.Principal component analysis and cluster analysis were used to analyze the experimental data,which could distinguish the manufacturers and producing areas of 37 kinds of tea.Python identification interface could identify the unknown tea species and know their origin,which provides data support for the analysis of tea species and producing areas to ensure the safety of tea drinking.