Skip to main content
Log in

Determination of Inorganic Elements in Teas Using Inductively Coupled Plasma Optical Emission Spectrometry and Classification with Exploratory Analysis

  • Published:
Food Analytical Methods Aims and scope Submit manuscript

Abstract

Multivariate optimization was employed to obtain the best conditions of the inductively coupled optical emission spectrometer (ICP OES) (nebulization gas flow rate of 0.47 L min−1 and applied power of 1.36 kW) for the determination of Al, Ba, Ca, Cu, Fe, K, Mg, Na, and Mn in 27 green tea samples. In the hierarchical cluster analysis, it was possible to observe the formation of five different groups (imported Japanese samples, samples without specifications, organically cultivated samples, samples in capsules, and ready-to-drink iced tea samples) besides the separation according to brand. In the principal component analysis we verified that the first four main components explained 99.98 % of the total variance. The ICP OES technique and the exploratory analysis were shown effective tools that can be used jointly in the quality control and classification of green tea samples.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • ANVISA, RDC no. 154, Brasil (2004) Legislação em Vigilância Sanitária, Resolução -RDC no. 154, de 15 de junho de 2004

  • Environmental Protection Agency–EPA (1992) Guidance for methods development and methods validation for the RCRA program SW-846 Methods

  • FDA no. 2004N- 0416, USA (2005) Department of health and human services, Rules and Regulations, Federal Register: June 9, 2005, 70, 110

  • Fernández PL, Pablos F, Martín MJ, González AG (2002) Food Chem 76:483–489

    Article  Google Scholar 

  • Flandrin JL, Montanari M (1998) História da alimentação, 2nd edn. vol 1. Estação Liberdade, São Paulo, p 885

    Google Scholar 

  • Fujiki H, Imai K, Nakachi K, Shimizu M, Moriwaki H, Suganuma M (2012) J Cancer Res Clin Oncol 138(8):1259–1270

    Article  CAS  Google Scholar 

  • Gómez MR, Cerutti S, Sombra LL, Silva MF, Martinez LD (2007) Food Chem Toxicol 45(6):1060–1064

    Article  Google Scholar 

  • Green JM (1996) Anal Chem 305A–309A

  • Herrador MA, Gonzáles AG (2001) Talanta 53:1249–1257

    Article  CAS  Google Scholar 

  • Hussain I, Khan F, Iqbal Y, Khalil SJ (2006) J Chem Soc Pak 28(3):246–251

    CAS  Google Scholar 

  • Li F, Li S, Li HB, Deng GF, Ling WH, Xu XR (2013) Food Funct 4(4):530–538

    Article  CAS  Google Scholar 

  • Lozak A, Soltyk K, Ostapczuk P, Fijalek Z (2002) Sci Total Environ 289:33–40

    Article  CAS  Google Scholar 

  • Marchisio PF, Sales A, Cerutti S, Marchevski E, Martinez LD (2005) J Hazard Mater 124:113–118

    Article  CAS  Google Scholar 

  • Massart DL, Vandeginste BGM, Buydens LMC, De Jong S, Lewin PJ, Smeyers-Verbeke J (1998) Handbook of Chemometrics and Qualimetrics: Part B. Elsevier, Amsterdam

    Google Scholar 

  • Mingoti SA (2005) Análise de componente principais. Análise de dados através de métodos de estatística multivariada: uma abordagem aplicada, 1st edn. vol. 1, (pp 59–95). UFMG, Belo Horizonte p 59–95

  • Piñero AM, Fishen A, Hill SJ (2003) J Food Comp Anal 16:195–211

    Article  Google Scholar 

  • Powell JJ, Burden TJ, Thompson RPH (1998) Analyst 123:1721–1724

    Article  CAS  Google Scholar 

  • Silva JCJ, Santos DM, Baccan N, Cadore S, Nóbrega JA (2004) Microchem J 77:185

    Article  CAS  Google Scholar 

  • Souza PP, Siebaldi HGL, Augusti DV, Neto WB, Amorim VM, Catharino RR, Eberlin MN, Augusti R (2007) J Agric Food Chem 55:2094–2102

    Article  Google Scholar 

  • Statsoft (1999) Statistica for Windows, Computer Program Manual, Tulsa

  • Street R, Szakova J, Drabek O, Mladkova L (2006) Czech J Food Sci 24(2):62–71

    CAS  Google Scholar 

  • Teófilo RF, Ferreira MM (2006) Quimiometria II: planilhas eletrônicas para cálculos de planejamentos experimentais, um tutorial. Quim Nova 29:338

    Article  Google Scholar 

  • Wise BM, Gallagher NB, Bro R, Shaver JM, Windig W, Koch RS (2005) PLS Toolbox 3.5 for use with MATLAB. Eigenvector Research Inc, Manson

    Google Scholar 

Download references

Acknowledgments

The authors wish to thank the Ezequiel Dias Foundation (FUNED) for their donation of laboratory equipment for this research, the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Brazil), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), and the Fundação de Desenvolvimento da Pesquisa (FUNDEP) for their financial support.

Conflict of Interest

Roberta E.S. Froes declares that she has no conflict of interest. Waldomiro Borges Neto declares that he has no conflict of interest. Mark A. Beinner declares that he has no conflict of interest. Clésia C. Nascentes declares that she has no conflict of interest. José Bento B. da Silva declares that he has no conflict of interest. This article does not contain any studies with human or animal subjects.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Roberta E. S. Froes.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Froes, R.E.S., Borges Neto, W., Beinner, M.A. et al. Determination of Inorganic Elements in Teas Using Inductively Coupled Plasma Optical Emission Spectrometry and Classification with Exploratory Analysis. Food Anal. Methods 7, 540–546 (2014). https://doi.org/10.1007/s12161-013-9651-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12161-013-9651-3

Keywords

Navigation