ICP-AES结合主成分分析和决策树模型的四种品牌白酒鉴别方法研究

郑劼; 吴文林; 万渝平; 梁恒兴; 肖全伟; 朱霞萍

doi:10.13386/j.issn1002-0306.2016.24.006

ICP-AES结合主成分分析和决策树模型的四种品牌白酒鉴别方法研究

1. 成都理工大学材料与化学化工学院
2. 成都市食品药品检验研究院

基金项目:

科技部国家重大科学仪器设备开发专项(2012YQ09016705)；

详细信息

作者简介:
郑劼(1992-),男,硕士研究生,研究方向:分析化学,E-mail:zhengj159@icloud.com。;

吴文林(1986-),男,硕士,工程师,研究方向:食品化学,E-mail:wuwenlin@163.com。;

中图分类号: TS261.7
计量
- 文章访问数: 196
- HTML全文浏览量: 30
- PDF下载量: 170
出版历程
- 收稿日期: 2016-07-04

Study on discrimination of four Chinese brand spirits based on ICP-AES coupled the principal component and decision tree analysis

College of Materials and Chemistry & Chemical Engineering,Chengdu University of Technology
Chengdu Institute for Food and Drug Control

摘要

摘要: 采用电感耦合等离子体原子发射光谱（ICP-AES）测定了四种品牌56个白酒样品（五粮液,郎酒,全兴,五津醇）中的16种元素含量。通过对结果进行z-score标准化,消除各元素间量纲差异,再对其进行主成分分析。结果表明,第一主成分的方差贡献率为40.3%,前十主成分的贡献率达96.3%,基本保留了原变量的所有信息。选择前十主成分建立决策树分类预测模型,模型的交叉验证准确率高达97.6%,再用模型预测未参与建模的15个白酒样品,准确率高达100%。模型能够准确区分五粮液,郎酒,全兴,五津醇四种品牌白酒。
- 白酒 /
- 元素 /
- ICP-AES /
- 主成分分析 /
- 决策树模型
Abstract: The potential of ICP- AES for metal element profiling of Chinese spirit samples was examined. Sixteen elements in fifty six spirits samples representing four varieties of brands( Wuliangye,Lang Liquor,Quanxing,Wujinchun) were determined.The set of data was employed to construct a sample class prediction model based on z- score standardization followed by principal component analysis( PCA) and decision tree analysis( DT),which was employed to explore the structure of the data and construct classification and prediction model. The first principal component explained 40.3% of variance while the top ten components explained 96.3% of variance which was employed to construct the DT model. The validated DT model based on 5- fold cross- validation enabled correct classification of 97.6% of samples,and other 15 spirit samples could be predict correctly. The Wuliangye,Lang Liquor,Quanxing,Wujinchun could be classified intensively.
- spirit /
- element /
- ICP-AES /
- PCA /
- decision tree

HTML全文

参考文献(17)

[1]	Vaclavik L,Lacina O,Hajslova J,et al.The use of high performance liquid chromatography-quadrupole time-of-flight mass spectrometry coupled to advanced data mining and chemometric tools for discrimination and classification of red wines according to their variety[J].Analytica Chimica Acta,2011,685(1):45-51.
[2]	王传荣.白酒的香型及其风味特征研究[J].酿酒科技,2008(9):49-52.
[3]	刘玉平,黄明泉,郑福平,等.中国白酒中挥发性成分研究进展[J].食品科学,2010,31(21):437-441.
[4]	马勇.中国白酒三十年发展报告(上)[J].酿酒科技,2016(2):17-22.
[5]	elih V S,ala M,Drgan V.Multi-element analysis of wines by ICP-MS and ICP-OES and their classification according to geographical origin in Slovenia[J].Food Chemistry,2014,153(2014):414-423.
[6]	Rossano E C,Szilágyi Z,Malorni A,et al.Influence of Winemaking Practices on the Concentration of Rare Earth Elements in White Wines Studied by Inductively Coupled Plasma Mass Spectrometry[J].Journal of Agricultural and Food Chemistry,2007,55(2):311-317.
[7]	Zhuang H,Ni Y,Kokot S.Combining HPLC-DAD and ICPMS data for improved analysis of complex samples:Classification of the root samples from Cortex moutan[J].Chemometrics and Intelligent Laboratory Systems,2014,135(2014):183-191.
[8]	Kruzlicova D,Fiket,Kniewald G.Classification of Croatian wine varieties using multivariate analysis of data obtained by high resolution ICP-MS analysis[J].Food Research International,2013,54(1):621-626.
[9]	Wang M,Avula B,Wang Y-H,et al.An integrated approach utilising chemometrics and GC/MS for classification of chamomile flowers,essential oils and commercial products[J].Food Chemistry,2014,152(2014):391-398.
[10]	Bannur Z,Teh L K,Hennesy T,et al.The differential metabolite profiles of acute lymphoblastic leukaemic patients treated with 6-mercaptopurine using untargeted metabolomics approach[J].Clinical Biochemistry,2014,47(6):427-431.
[11]	Zhu K,Nie S,Gong D,et al.Effect of polysaccharide from Ganoderma atrum on the serum metabolites of type 2 diabetic rats[J].Food Hydrocolloids,2016,53:31-36.
[12]	Zheng J,Liang R,Wu C,et al.Discrimination of different kinds of Luzhou-flavor raw liquors based on their volatile features[J].Food Research International,2014,56:77-84.
[13]	Worley B,Halouska S,Powers R.Utilities for quantifying separation in PCA/PLS-DA scores plots[J].Analytical Biochemistry,2013,433(2):102-104.
[14]	Almeida M R,Fidelis C H V,Barata L E S,et al.Classification of Amazonian rosewood essential oil by Raman spectroscopy and PLS-DA with reliability estimation[J].Talanta,2013,117:305-311.
[15]	朱焯炜,阙立志,吴亚敏,等.三维荧光光谱结合PARAFAC和GA对中国白酒品牌的鉴别[J].中国激光,2015,42(6):315-320.
[16]	Taylor V F,Longerich H P,Greenough J D.Multielement Analysis of Canadian Wines by Inductively Coupled Plasma Mass Spectrometry(ICP-MS)and Multivariate Statistics[J].Journal of Agricultural and Food Chemistry,2003,51(4):856-860.
[17]	Berrueta L A,Alonso-Salces R M,Héberger K.Supervised pattern recognition in food analysis[J].Journal of Chromatography A,2007,1158(12):196-214.