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Quantitative determination and classification of energy drinks using near-infrared spectroscopy

Abstract

Almost a hundred commercially available energy drink samples from Hungary, Slovakia, and Greece were collected for the quantitative determination of their caffeine and sugar content with FT-NIR spectroscopy and high-performance liquid chromatography (HPLC). Calibration models were built with partial least-squares regression (PLSR). An HPLC-UV method was used to measure the reference values for caffeine content, while sugar contents were measured with the Schoorl method. Both the nominal sugar content (as indicated on the cans) and the measured sugar concentration were used as references. Although the Schoorl method has larger error and bias, appropriate models could be developed using both references. The validation of the models was based on sevenfold cross-validation and external validation. FT-NIR analysis is a good candidate to replace the HPLC-UV method, because it is much cheaper than any chromatographic method, while it is also more time-efficient. The combination of FT-NIR with multidimensional chemometric techniques like PLSR can be a good option for the detection of low caffeine concentrations in energy drinks. Moreover, three types of energy drinks that contain (i) taurine, (ii) arginine, and (iii) none of these two components were classified correctly using principal component analysis and linear discriminant analysis. Such classifications are important for the detection of adulterated samples and for quality control, as well. In this case, more than a hundred samples were used for the evaluation. The classification was validated with cross-validation and several randomization tests (X-scrambling).

The way of energy drinks from cans to appropriate chemometric models

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References

  1. Reissig CJ, Strain EC, Griffiths RR. Caffeinated energy drinks—a growing problem. Drug Alcohol Depend. 2009;99:1–10.

    CAS  Article  Google Scholar 

  2. Seifert SM, Schaechter JL, Hershorin ER, Lipshultz SE. Health effects of energy drinks on children, adolescents, and young adults. Pediatrics. 2011;127:511–28.

    Article  Google Scholar 

  3. Heckman MA, Weil J, de Mejia EG. Caffeine (1, 3, 7-trimethylxanthine) in foods: a comprehensive review on consumption, functionality, safety, and regulatory matters. J Food Sci. 2010;75:77–87.

    Article  Google Scholar 

  4. Malik VS, Schulze MB, Hu FB. Intake of sugar-sweetened beverages and weight gain : a systematic review. Am J Clin Nutr. 2006;84:274–88.

    CAS  Google Scholar 

  5. Malinauskas BM, Aeby VG, Overton RF, Carpenter-Aeby T, Barber-Heidal K. A survey of energy drink consumption patterns among college students. Nutr J. 2007;6:35.

    Article  Google Scholar 

  6. Ferreira SE, de Mello MT, Pompéia S, de Souza-Formigoni MLO. Effects of energy drink ingestion on alcohol intoxication. Alcohol Clin Exp Res. 2006;30:598–605.

    Article  Google Scholar 

  7. O’Brien MC, McCoy TP, Rhodes SD, Wagoner A, Wolfson M. Caffeinated cocktails: energy drink consumption, high-risk drinking, and alcohol-related consequences among college students. Acad Emerg Med. 2008;15:453–60.

    Article  Google Scholar 

  8. Armenta S, Garrigues S, de la Guardia M. Solid-phase FT-Raman determination of caffeine in energy drinks. Anal Chim Acta. 2005;547:197–203.

    CAS  Article  Google Scholar 

  9. Pieszko C, Baranowska I, Flores A. Determination of energizers in energy drinks. J Anal Chem. 2010;65:1228–34.

    CAS  Article  Google Scholar 

  10. Abourashed EA, Mossa JS. HPTLC determination of caffeine in stimulant herbal products and power drinks. J Pharm Biomed Anal. 2004;36:617–20.

    CAS  Article  Google Scholar 

  11. Sereshti H, Samadi S. A rapid and simple determination of caffeine in teas, coffees and eight beverages. Food Chem. 2014;158:8–13.

    CAS  Article  Google Scholar 

  12. Grant DC, Helleur RJ. Simultaneous analysis of vitamins and caffeine in energy drinks by surfactant-mediated matrix-assisted laser desorption/ionization. Anal Bioanal Chem. 2008;391:2811–8.

    CAS  Article  Google Scholar 

  13. Vochyánová B, Opekar F, Tůma P, Štulík K. Rapid determinations of saccharides in high-energy drinks by short-capillary electrophoresis with contactless conductivity detection. Anal Bioanal Chem. 2012;404:1549–54.

    Article  Google Scholar 

  14. Lucena R, Cárdenas S, Gallego M, Valcárcel M. Continuous flow autoanalyzer for the sequential determination of total sugars, colorant and caffeine contents in soft drinks. Anal Chim Acta. 2005;530:283–9.

    CAS  Article  Google Scholar 

  15. Aranda M, Morlock G. Simultaneous determination of riboflavin, pyridoxine, nicotinamide, caffeine and taurine in energy drinks by planar chromatography-multiple detection with confirmation by electrospray ionization mass spectrometry. J Chromatogr A. 2006;1131:253–60.

    CAS  Article  Google Scholar 

  16. Khasanov VV, Slizhov YG, Khasanov VV. Energy drink analysis by capillary electrophoresis. J Anal Chem. 2013;68:357–9.

    CAS  Article  Google Scholar 

  17. Huck CW, Guggenbichler W, Bonn GK. Analysis of caffeine, theobromine and theophylline in coffee by near infrared spectroscopy (NIRS) compared to high-performance liquid chromatography (HPLC) coupled to mass spectrometry. Anal Chim Acta. 2005;538:195–203.

    CAS  Article  Google Scholar 

  18. Zhang X, Li W, Yin B, Chen W, Kelly DP, Wang X, et al. Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS). Spectrochim Acta - Part A Mol Biomol Spectrosc. 2013;114:350–6.

    CAS  Article  Google Scholar 

  19. Geladi P, Kowalski BR. Partial least-squares regression: a tutorial. Anal Chim Acta. 1986;185:1–17.

    CAS  Article  Google Scholar 

  20. Naes T, Isaksson T, Fearn T, Davies T. Validation. A user-friendly Guid. to Multivar. Calibration Classif., Chicester: NIR Publications; 2002, pp. 155–75.

  21. Hastie T, Tibshirani R, Friedman J. Model assessment and selection. Elem. Stat. Learn. Data Mining, Inference, Predict., Springer, New York; 2001, pp. 214–6.

  22. Rácz A, Bajusz D, Héberger K. Consistency of QSAR models: correct split of training and test sets, ranking of models and performance parameters. SAR QSAR Environ Res. 2015;26:683–700.

    Article  Google Scholar 

  23. Bruker Corporation, Billerica, MA U. OPUS 6.5 n.d.

  24. Hastie T, Tibshirani R, Friedman J. Linear methods for classification. Elem. Stat. Learn. Data Mining, Inference, Predict., Springer, New York; 2001, pp. 84–90.

  25. Wold S, Esbensen K, Geladi P. Principal component analysis. Chemom Intell Lab Syst. 1987;2:37–52.

    CAS  Article  Google Scholar 

  26. Statsoft Inc., Tulsa, OK U. STATISTICA 12 n.d.

  27. Workman J, Jr. Functional groupings and calculated locations in wavenumbers (cm-1) for IR spectroscopy. Handb. Org. Compd. NIR, IR, Raman, UV-Vis Spectra Featur. Polym. Surfactants, San Diego: Academic Press; 2000, pp. 229–36.

  28. Magalhães LM, Machado S, Segundo MA, Lopes JA, Páscoa RNMJ. Rapid assessment of bioactive phenolics and methylxanthines in spent coffee grounds by FT-NIR spectroscopy. Talanta. 2016;147:460–7.

    Article  Google Scholar 

  29. Ribeiro JS, Ferreira MMC, Salva TJG. Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy. Talanta. 2011;83:1352–8.

    CAS  Article  Google Scholar 

  30. Horovitz W. Sugar and sugar products. Off. methods Anal. Assoc. Off. Anal. Chem., Washington, USA: AOAC; 1975, p. 573.

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Acknowledgments

The authors would like to thank Dr. Mihály Dernovics and Andrea Vass for their help and the useful advices in HPLC-UV measurements and Csilla Biróova for her help in sugar content measurements. The authors are also greatful to David Bajusz for reading the manuscript.

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Correspondence to Károly Héberger.

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Rácz, A., Héberger, K. & Fodor, M. Quantitative determination and classification of energy drinks using near-infrared spectroscopy. Anal Bioanal Chem 408, 6403–6411 (2016). https://doi.org/10.1007/s00216-016-9757-8

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  • DOI: https://doi.org/10.1007/s00216-016-9757-8

Keywords

  • Energy drink
  • Caffeine
  • Sugar
  • Classification
  • PCA
  • LDA
  • FT-NIR