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Food Analytical Methods

, Volume 11, Issue 7, pp 1958–1968 | Cite as

Voltammetric e-Tongue Based on a Single Sensor and Variable Selection for the Classification of Teas

  • Dayvison R. Rodrigues
  • Diana S. M. de Oliveira
  • Marcio J. C. Pontes
  • Sherlan G. LemosEmail author
Article
  • 190 Downloads

Abstract

This work presents a simple and low-cost strategy to obtain voltammetric tongues by using a single voltammetric sensor aided by variable selection techniques. A usual electronic tongue consists of an array of physical sensors followed by data compression before pattern recognition modeling. Alternatively, a single voltammetric sensor can also act as an array of pseudo-sensors—applied potentials in a voltammogram—that can be properly selected to perform a given discrimination. The applicability of this strategy was evaluated in the discrimination of teas. Teas prepared for immediate intake (simulating a home-made tea cup) were analyzed with staircase voltammetry at an epoxy-graphite electrode. Voltammograms were submitted to variable selection previously to linear discriminant analysis (LDA) to identify the applied potentials that improve discrimination of teas according to variety, country of origin, and manufacturer. Successive projections algorithm (SPA), genetic algorithm (GA), and stepwise (SW) formulation were the variable selection techniques evaluated. Best results were achieved with SPA/LDA models, with correct classification rates for the prediction set close to 100%.

Keywords

Tea Voltammetric electronic tongue Multivariate classification Successive projections algorithm 

Notes

Acknowledgements

We are grateful to the Brazilian agency Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for financial support (research fellowship and scholarship). The authors would like to thank Dr. Marcelo Pistonesi from Universidad Nacional del Sur, Bahía Blanca, Argentina, for providing tea samples from Argentina, and Dr. Mário C. U. Araújo for providing tea samples from Brazil.

Funding

This study was funded by the National Institute of Advanced Analytical Science and Technology (INCTAA), CNPq grant number 573894/2008-6, FAPESP grant number 2008/57808-1, and CNPq grant number 300884/2009-5.

Compliance with Ethical Standards

Conflict of Interest

Author Dayvison R. Rodrigues declares that he has no conflict of interest. Author Diana S. M. de Oliveira declares that she has no conflict of interest. Author Marcio J. C. Pontes declares that he has no conflict of interest. Author Sherlan G. Lemos declares that he has no conflict of interest.

Informed Consent

Informed consent is not applicable in this study.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

12161_2018_1162_MOESM1_ESM.docx (27 kb)
ESM 1 (DOCX 27 kb).

References

  1. Abdallah M, Vergara-Barberán M, Lerma-García MJ, Herrero-Martínez JM, Simó-Alfonso EF, Guerfel M (2016) Cultivar discrimination and prediction of mixtures of Tunisian extra virgin olive oils by FTIR. Eur J Lipid Sci Technol 118(8):1236–1242.  https://doi.org/10.1002/ejlt.201500041 CrossRefGoogle Scholar
  2. Abreu REL, Paz JEM, Silva AC, Pontes MJC, Lemos SG (2015) Ethanol fuel adulteration with methanol assessed by cyclic voltammetry and multivariate calibration. Fuel 156:20–25.  https://doi.org/10.1016/j.fuel.2015.04.024 CrossRefGoogle Scholar
  3. Aguilar-Lira GY, Gutiérrez-Salgado JM, Rojas-Hernández A, Rodríguez-Ávila JA, Páez-Hernández ME, Álvarez-Romero GA (2017) Artificial neural network for the voltamperometric quantification of diclofenac in presence of other nonsteroidal anti-inflammatory drugs and some commercial excipients. J Electroanal Chem 801:527–535.  https://doi.org/10.1016/j.jelechem.2017.08.029 CrossRefGoogle Scholar
  4. Baciu A, Manea F, Pop A, Pode R, Schoonman J (2017) Simultaneous voltammetric detection of ammonium and nitrite from groundwater at silver-electrodecorated carbon nanotube electrode. Process Saf Environ 108:18–25.  https://doi.org/10.1016/j.psep.2016.05.006 CrossRefGoogle Scholar
  5. Borazjani M, Mehdinia A, Jabbari A (2017) Betamethasone-based chiral electrochemical sensor coupled to chemometric methods for determination of mandelic acid enantiomers. J Mol Recognit 30:2653–2662CrossRefGoogle Scholar
  6. Bougrini M, Tahri K, Saidi T, Hassani NEAE, Bouchikhi B, Bari NE (2016) Classification of honey according to geographical and botanical origins and detection of its adulteration using voltammetric electronic tongue. Food Anal Methods 9(8):2161–2173.  https://doi.org/10.1007/s12161-015-0393-2 CrossRefGoogle Scholar
  7. Brett AMO, Ghica M-E (2003) Electrochemical oxidation of quercetin. Electroanalysis 15(22):1745–1750.  https://doi.org/10.1002/elan.200302800 CrossRefGoogle Scholar
  8. Cetó X, Saint C, Chow CWK, Voelcker NH, Prieto-Simón B (2017) Electrochemical fingerprints of brominated trihaloacetic acids (HAA3) mixtures in water. Sensor Actuat B 247:70–77.  https://doi.org/10.1016/j.snb.2017.02.179 CrossRefGoogle Scholar
  9. Chen Q, Sun C, Ouyang Q, Wang Y, Liu A, Li H, Zhao J (2015) Classification of different varieties of oolong tea using novel artificial sensing tools and data fusion. LWT-Food Sci Technol 60:781–787CrossRefGoogle Scholar
  10. Diniz PHGD, Gomes AA, Pistonesi MF, Band BSF, Araújo MCU (2014) Simultaneous classification of teas according their varieties and geographic origins by using NIR spectroscopy and SPA-LDA. Food Anal Methods 7:1712–1718Google Scholar
  11. Gambarra-Neto FF, Marino G, Araújo MCU, Galvão RKH, Pontes MJC, Medeiros EP (2009) Classification of edible vegetable oils using square wave voltammetry with multivariate data analysis. Talanta 77(5):1660–1666.  https://doi.org/10.1016/j.talanta.2008.10.003 CrossRefPubMedGoogle Scholar
  12. Holmin S, Spangeus P, Krantz-Rülcker C, Winquist F (2001) Compression of electronic tongue data based on voltammetry—a comparative study. Sensor Actuat B-Chem 76(1-3):455–464.  https://doi.org/10.1016/S0925-4005(01)00585-8 CrossRefGoogle Scholar
  13. Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11(1):137–148.  https://doi.org/10.1080/00401706.1969.10490666 CrossRefGoogle Scholar
  14. Kilmartin PA, Hsu CF (2003) Characterisation of polyphenols in green, oolong, and black teas, and in coffee, using cyclic voltammetry. Food Chem 82(4):501–512.  https://doi.org/10.1016/S0308-8146(03)00066-9 CrossRefGoogle Scholar
  15. Lemos SG, Nogueira ARA, Torre-Neto A, Parra A, Artigas J, Alonso J (2004) In-soil potassium sensor system. J Agr Food Chem 52:5810−5815CrossRefGoogle Scholar
  16. Marreto PD, Zimer AM, Faria RC, Mascaro LH, Pereira EC, Fragoso WD, Lemos SG (2014) Multivariate linear regression with variable selection by a successive projections algorithm applied to the analysis of anodic stripping voltammetry data. Electrochim Acta 127:68–78.  https://doi.org/10.1016/j.electacta.2014.02.029 CrossRefGoogle Scholar
  17. Nascimento DS, Insausti M, Band BSF, Lemos SG (2014) Simultaneous determination of Cu, Pb, Cd, Ni, Co and Zn in bioethanol fuel by adsorptive stripping voltammetry and multivariate linear regression. Fuel 137:172–178.  https://doi.org/10.1016/j.fuel.2014.07.100 CrossRefGoogle Scholar
  18. Niazi A, Leardi R (2012) Genetic algorithms in chemometrics. J Chemom 26(6):345–351.  https://doi.org/10.1002/cem.2426 CrossRefGoogle Scholar
  19. Novak I, Šeruga M, Komorsky-Lovric Š (2009) Electrochemical characterization of epigallocatechin gallate using square-wave voltammetry. Electroanalysis 21(9):1019–1025.  https://doi.org/10.1002/elan.200804509 CrossRefGoogle Scholar
  20. Novak I, Šeruga M, Komorsky-Lovric Š (2010) Characterisation of catechins in green and black teas using square-wave voltammetry and RP-HPLC-ECD. Food Chem 122(4):1283–1289.  https://doi.org/10.1016/j.foodchem.2010.03.084 CrossRefGoogle Scholar
  21. Prieto N, Oliveri P, Leardi R, Gay M, Apetrei C, Rodriguez-Méndez ML, de Saja JA (2013) Application of a GA–PLS strategy for variable reduction of electronic tongue signals. Sensor Actuat B-Chem 183:52–57.  https://doi.org/10.1016/j.snb.2013.03.114 CrossRefGoogle Scholar
  22. Remes A, Pop A, Manea F, Baciu A, Picken SJ, Schoonman J (2012) Electrochemical determination of pentachlorophenol in water on a multi-wall carbon nanotubes-epoxy composite electrode. Sensors 12(12):7033–7046.  https://doi.org/10.3390/s120607033 CrossRefPubMedGoogle Scholar
  23. Schreyer SK, Mikkelsen SR (2000) Chemometric analysis of square wave voltammograms for classification and quantitation of untreated beverage samples. Sensor Actuat B-Chem 71(1-2):147–153.  https://doi.org/10.1016/S0925-4005(00)00601-8 CrossRefGoogle Scholar
  24. Silva AC, Paz JEM, Pontes LFBL, Lemos SG, Pontes MJC (2013) An electroanalytical method to detect adulteration of ethanol fuel by using multivariate analysis. Electrochim Acta 111:160–164.  https://doi.org/10.1016/j.electacta.2013.07.208 CrossRefGoogle Scholar
  25. Soares SFC, Gomes AA, Galvão-Filho AR, Araujo MCU, Galvão RKH (2013) The successive projections algorithm. Trac-Trend Anal Chem 42:84CrossRefGoogle Scholar
  26. Sutter JM, Kalivas JH (1993) Comparison of forward selection, backward elimination, and generalized simulated annealing for variable selection. Microchem J 47(1-2):60–66.  https://doi.org/10.1006/mchj.1993.1012 CrossRefGoogle Scholar
  27. USDA (2003) USDA database for the flavonoid contents of selected foods. Beltsville, US Department of AgricultureGoogle Scholar
  28. Ziyatdinova GK, Nizamova AM, Aytuganova II, Budnikov HC (2013) Voltammetric evaluation of the antioxidant capacity of tea on electrodes modified with multi-walled carbon nanotubes. J Anal Chem 68(2):132–113.  https://doi.org/10.1134/S1061934813020172 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Dayvison R. Rodrigues
    • 1
  • Diana S. M. de Oliveira
    • 1
  • Marcio J. C. Pontes
    • 1
  • Sherlan G. Lemos
    • 1
    Email author
  1. 1.Departamento de QuímicaUniversidade Federal da ParaíbaJoão PessoaBrazil

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