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Quantitative determination of sunset yellow concentration in soft drinks via digital image processing

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Abstract

Image processing technique (IPT) can be used to evaluate the quality of food and drink products. Therefore, this study was focused on the relationship between colorimetric parameters of IPT and different concentrations of sunset yellow in Miranda soft drink. The amount of the sunset yellow color in Miranda soft drink were assessed using HPLC and a specific doses of 0, 25, 60, 90, 120 and 150 mg kg−1 sunset yellow were maintained for this study. The image processing results showed that with increasing the concentration of sunset yellow, all colorimetric values were changed except for b* values. Regression analysis between colorimetric parameters and sunset yellow showed a significant multivariate linear relationship (P < 0.05) with a high coefficient of determination (R2 adjusted = 98.7). The results showed that the predictive equation could be used to estimate the concentration of sunset yellow color in Miranda soft drink. The result of the present study showed that digital image processing technique approach can be a successful tool for the prediction of sunset yellow concentration in drink products. Therefore, IPT could be a potential prediction tool for determining the quantity of synthetic colorant in the food and drink products.

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References

  1. B.S. Dissing, M.E. Nielsen, B.K. Ersbøll, S. Frosch, Plos One 6, e19032 (2011). doi:10.1371/journal.pone.0019032

    Article  CAS  Google Scholar 

  2. Z. Guler, J Food Qual 28, 98 (2005)

    Article  Google Scholar 

  3. S.M. Ghoreishi, M. Behpour, M. Golestaneh, Food Chem. 132, 637 (2012)

    Article  CAS  Google Scholar 

  4. P. Naik, Current Sci 106, 143 (2014)

    Google Scholar 

  5. F. Aguilar, H. Autrup, S. Barlow, L. Castle, R. Crebelli, W. Dekant, F. Toldrá, The EFSA J. 660, 1 (2008)

    Google Scholar 

  6. Y.S. Al-Degs, Food Chem 117, 485 (2009)

    Article  CAS  Google Scholar 

  7. O. Sha, X. Zhu, Y. Feng, W. Ma, J. Anal. Methods Chem. 1–9 (2014). doi:10.1155/2014/964273

  8. A.H. El-Sheikh, Y.S. Al-degs, Dyes Pigments 97, 330 (2013)

    Article  CAS  Google Scholar 

  9. M.Z. ABdullah, S.A. Aziz, A. Mohamed, J. Food Qual. 23, 39 (2000)

    Article  Google Scholar 

  10. Y.J. Cho, S.B. Cho, H.J. Kim, H.S. Chun, J. Food Qual. 26, 511 (2003)

    Article  Google Scholar 

  11. A. Antonelli, M. Cocchi, P. Fava, G. Foca, G.C. Franchini, D. Manzini, A. Ulrici, Anal. Chim. Acta. 515, 3 (2004)

    Article  CAS  Google Scholar 

  12. D. Wu, D. Sun, Trends Food Sci. Tech. 29, 5 (2013)

    Article  Google Scholar 

  13. F. Francis, Food Qual. Prefence, 6,149 (1995)

    Article  Google Scholar 

  14. D. Macdougall, Colour in food. First published. (Woodhead Publishing Limited, Cambridge, England, 2002), pp. 51–54

  15. J. Lu, J. Tan, P. Shatadal, E.D., Gerrard. Meat Sci. 56, 57 (2000)

    Article  CAS  Google Scholar 

  16. S. Majumdar, D.S. Jayas, Trans. ASAE. 43, 1677 (2000)

    Article  Google Scholar 

  17. V. Leemans, M.F. Destain, Food Eng. 61, 83 (2004)

    Article  Google Scholar 

  18. M. Mohebbi, M.R. Akbarzadeh, F. Shahidi, M. Moussavi, H. Ghoddusi, Comput. Electron. Agric. 69, 128 (2009)

    Article  Google Scholar 

  19. F. Mendoza, J.M. Aguilera, J. Food Sci. 69, E471 (2004)

    Article  CAS  Google Scholar 

  20. L. Fernandez, C. Castillero, J.M. Aguilera, J. Food Eng. 67, 185 (2005)

    Article  Google Scholar 

  21. S.R. Gallagher, J. Curr. Protoc. Essent. Lab. Tech. A-3C (2010)

  22. E.K. Pool, F. Shahidi, S.A. Mortazavi, M. Azizpour, E. Daneshzad, Food Meas. 10, 634–642 (2016).

    Article  Google Scholar 

  23. J.H. Zar, Biostatistical Analysis, (Prentice-Hall, Inc., New Jersy, 1999)

    Google Scholar 

  24. M.H. Kamani, O. Safari, S.A. Mortazavi, M. Mehrabansangatash, Qual. Assur. Saf. Crop. 7, 589 (2014)

    Article  Google Scholar 

  25. M.C. ZGHAL, M.G. Scanlon, H.D. Sapirstein, Cereal Chem. 76, 734 (1999)

    Article  Google Scholar 

  26. L. Stien, F. Manne, K. Ruohonene, A. Kause, K. Rungruangsak-Torrissen, A. Kiessling, Aquaculture 261, 695 (2006)

    Article  Google Scholar 

  27. F. Mendoza, P. Dejmek, J.M. Aguilera, Food Res. Int. 40, 1146 (2007)

    Article  Google Scholar 

  28. Statgraphics. Advanced regression. Using logistic regression. In user manual (Chap. 7). Statgraphics Plus for Windows. Version 5.1. Professional edition. (Manugistics, Inc. Rockville, 1999)

  29. F. Mendoza, P. Dejmek, J.M. Aguilera, Postharvest Biol. Technol. 41, 285 (2006)

    Article  Google Scholar 

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Acknowledgements

The authors are thankful to the vice chancellor of research and vice chancellor of food and drug at Mashhad University of Medical Sciences for their financial supports.

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Correspondence to Mohammad Hassan Kamani.

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Hosseininia, S.A., Kamani, M.H. & Rani, S. Quantitative determination of sunset yellow concentration in soft drinks via digital image processing. Food Measure 11, 1065–1070 (2017). https://doi.org/10.1007/s11694-017-9483-8

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  • DOI: https://doi.org/10.1007/s11694-017-9483-8

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