Food Analytical Methods

, Volume 8, Issue 5, pp 1088–1092 | Cite as

A Tool for General Quality Assessment of Black Tea—Retail Price Prediction by an Electronic Tongue

  • Maria Khaydukova
  • Xavier Cetó
  • Dmitry Kirsanov
  • Manel del Valle
  • Andrey Legin
Article

Abstract

Retail price of food products is a complex interplay between multiple factors. Overall product quality has got one of the most serious impacts on the price in many situations. In the present study, an artificial sensory system (potentiometric electronic tongue) was employed for the analysis of black tea samples purchased in the retail stores in Spain and Russia. It was possible to relate the response of a potentiometric sensor system with retail prices of various black tea samples by means of partial least squares (PLS) regression. PLS regression models are allowed for the prediction of retail price with mean relative errors of about 15 and 25 % for Spain’s tea bags and for loose-packed tea from Russia, respectively. The suggested approach shows a promise for the development of an instrumental analytical technique for regulatory authorities to fight with counterfeits and for commercial purposes to evaluate market space.

Keywords

Black tea Multisensor system Electronic tongue Retail price assessment 

Notes

Acknowledgments

Maria Khaydukova acknowledges the St. Petersburg State University for a research Grant 12.42.213.2013 and partial financial support from the Government of the Russian Federation, Grant 074-U01. Dmitry Kirsanov and Andrey Legin have received partial financial support from the Government of the Russian Federation, Grant 074-U01. Manel del Valle thanks the support from the programme ICREA Academia.

Conflict of Interest

Maria Khaydukova declares that she has no conflict of interest. Xavier Cetó declares that he has no conflict of interest. Dmitry Kirsanov declares that he has no conflict of interest. Manel del Valle declares that he has no conflict of interest. Andrey Legin declares that he has no conflict of interest. This article does not describe any studies with human or animal subjects.

References

  1. Bhattacharyya R, Tudu B, Das SC, Bhattacharyya N, Bandyopadhyay R, Pramanik P (2012) Classification of black tea liquor using cyclic voltammetry. J Food Eng 109:120–126CrossRefGoogle Scholar
  2. Cao J, Zhao Y, Li Y, Deng HJ, Yi J, Liu JW (2006) Fluoride levels in various black tea commodities: measurement and safety evaluation. Food Chem Toxicol 44:1131–1137CrossRefGoogle Scholar
  3. Cetó X, Llobet M, Marco J, del Valle M (2013) Application of an electronic tongue towards the analysis of brandies. Anal Methods 5:1120–1129CrossRefGoogle Scholar
  4. Chen Q, Zhao J, Zhang H, Wang X (2006) Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration. Anal Chim Acta 752:77–84Google Scholar
  5. Chen Q, Zhao J, Vittayapadung S (2008) Identification of the green tea grade level using electronic tongue and pattern recognition. Food Res Int 41:500–504CrossRefGoogle Scholar
  6. Chen Q, Zhao J, Guo Z, Wang X (2010) Determination of caffeine content and main catechins contents in green tea (Camellia sinensis L.) using taste sensor technique and multivariate calibration. J Food Compos Anal 23:353–358CrossRefGoogle Scholar
  7. Del Valle M (2010) Electronic tongues employing electrochemical sensors. Electroanalysis 22(14):1539–1555Google Scholar
  8. Eckert C, Pein M, Reimann J, Breitkreutz J (2014) Taste evaluation of multicomponent mixtures using a human taste panel, electronic taste sensing systems and HPLC. Sensors Actuator B 182:294–299CrossRefGoogle Scholar
  9. Esbensen KH (2001) Multivariate data analysis—in practice. An introduction to multivariate data analysis and experimental design, 5th edn. CAMO AS Publ, OsloGoogle Scholar
  10. Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic, San DiegoGoogle Scholar
  11. Gallardo J, Alegret S, del Valle M (2005) Application of a potentiometric electronic tongue as a classification tool in food analysis. Talanta 66:1303–1309CrossRefGoogle Scholar
  12. Ghosh A, Tamuly P, Bhattacharyya N, Tudu B, Gogoi N, Bandyopadhyay R (2012) Estimation of theaflavin content in black tea using electronic tongue. J Food Eng 110:71–79CrossRefGoogle Scholar
  13. Harbowy ME, Balentine DA, Davies AP, Cai Y (1997) Tea chemistry. Crit Rev Plant Sci 16(5):415–480CrossRefGoogle Scholar
  14. He W, Hu X, Zhao L, Liao X, Zhang Y, Zhang M, Wu J (2009) Evaluation of Chinese tea by the electronic tongue: correlation with sensory properties and classification according to geographical origin and grade level. Food Res Int 42:1462–1467CrossRefGoogle Scholar
  15. Kang B-S, Lee J-E, Park H-J (2014) Electronic tongue-based discrimination of Korean rice wines (makgeolli) including prediction of sensory evaluation and instrumental measurements. Food Chem 151:317–323CrossRefGoogle Scholar
  16. Legin A, Rudnitskaya A, Vlasov Y, Di Natale C, Davide F, D’Amico F (1997) Tasting of beverages using an electronic tongue. Sensors Actuator B 44:291–296CrossRefGoogle Scholar
  17. Legin A, Rudnitskaya A, Lvova L, Vlasov Y, Di Natale C, D’Amico A (2003) Evaluation of Italian wine by the electronic tongue: recognition, quantitative analysis and correlation with human sensory perception. Anal Chim Acta 484:33–44CrossRefGoogle Scholar
  18. Liu N, Liang Y, Bin J, Zhang Z, Huang J, Shu RX, Yang K (2014) Classification of green and black teas by PCA and SVM analysis of cyclic voltammetric signals from metallic oxide-modified electrode. Food Anal Methods 7:472–480CrossRefGoogle Scholar
  19. Lu Y, Guo W-F, Yang X-Q (2004) Fluoride content in tea and its relationship with tea quality. J Agric Food Chem 52:4472–4476CrossRefGoogle Scholar
  20. Meyerhoff ME, Opdycke WN (1986) Ion-selective electrodes. Adv Clin Chem 25:1–47CrossRefGoogle Scholar
  21. Rudnitskaya A, Polshin E, Kirsanov D, Lammertyn J, Nicolai B, Saison D, Delvaux FR, Delvaux F, Legin A (2009) Instrumental measurement of beer taste attributes using an electronic tongue. Anal Chim Acta 646:111–118CrossRefGoogle Scholar
  22. Sang S, Lambert JD, Ho C-T, Yang CS (2011) The chemistry and biotransformation of tea constituents. Pharmacol Res 64:87–99CrossRefGoogle Scholar
  23. Vinzi EV, Chin WW, Henseler J, Wang H (2010) Handbook of partial least squares. Concepts, methods and applications. Springer, BerlinCrossRefGoogle Scholar
  24. Zhao M, Ma Y, L-l D, D-l Z, J-h L, W-x Y, Y-l L, H-j Z (2013) A high-performance liquid chromatographic method for simultaneous determination of 21 free amino acids in tea. Food Anal Methods 6:69–75CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Maria Khaydukova
    • 1
    • 2
  • Xavier Cetó
    • 3
  • Dmitry Kirsanov
    • 1
    • 2
  • Manel del Valle
    • 3
  • Andrey Legin
    • 1
    • 2
  1. 1.Laboratory of Chemical Sensors, Institute of ChemistrySt. Petersburg State UniversitySt. PetersburgRussia
  2. 2.Laboratory of Artificial Sensory SystemsITMO UniversitySt. PetersburgRussia
  3. 3.Sensors & Biosensors Group, Department of ChemistryUniversitat Autònoma de BarcelonaBellaterraSpain

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