Determination of Inorganic Elements in Teas Using Inductively Coupled Plasma Optical Emission Spectrometry and Classification with Exploratory Analysis
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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.
KeywordsInductively coupled plasma optical emission spectrometry Inorganic elements Green teas Multivariate optimization
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.
- ANVISA, RDC no. 154, Brasil (2004) Legislação em Vigilância Sanitária, Resolução -RDC no. 154, de 15 de junho de 2004Google Scholar
- Environmental Protection Agency–EPA (1992) Guidance for methods development and methods validation for the RCRA program SW-846 MethodsGoogle Scholar
- FDA no. 2004N- 0416, USA (2005) Department of health and human services, Rules and Regulations, Federal Register: June 9, 2005, 70, 110Google Scholar
- Flandrin JL, Montanari M (1998) História da alimentação, 2nd edn. vol 1. Estação Liberdade, São Paulo, p 885Google Scholar
- Green JM (1996) Anal Chem 305A–309AGoogle Scholar
- Hussain I, Khan F, Iqbal Y, Khalil SJ (2006) J Chem Soc Pak 28(3):246–251Google Scholar
- Massart DL, Vandeginste BGM, Buydens LMC, De Jong S, Lewin PJ, Smeyers-Verbeke J (1998) Handbook of Chemometrics and Qualimetrics: Part B. Elsevier, AmsterdamGoogle 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 59–95Google Scholar
- Statsoft (1999) Statistica for Windows, Computer Program Manual, TulsaGoogle Scholar
- Street R, Szakova J, Drabek O, Mladkova L (2006) Czech J Food Sci 24(2):62–71Google 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, MansonGoogle Scholar