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The Parallel Factor Analysis of Beer Fluorescence

  • Tatjana Dramićanin
  • Ivana Zeković
  • Jovana Periša
  • Miroslav D. DramićaninEmail author
ORIGINAL ARTICLE
  • 13 Downloads

Abstract

Fluorescence excitation-emission matrices were measured for 111 samples of different types of beer and studied by the parallel factor analysis (PARAFAC). The 5-component PARAFAC model was found to suitably describes the beer fluorescence, accounting for 99.4% of the fluorescence variance in the measured set of samples, and providing the completely resolved excitation and emission spectra of each component. The model was chosen based on a model’s core consistency and split-half analysis. It is shown that beer fluorescence is the sum of fluorescence of aromatic amino acids (tryptophan, tyrosine, and phenylalanine), different forms of vitamin B, and phenolic compounds. Obtained PARAFAC model of beer fluorescence demonstrated the potential for the quantification and quality analysis of beer fluorophores and classification of different beer types.

Keywords

Fluorescence Beer PARAFAC PLS-DA Excitation-emission matrices 

Notes

Acknowledgments

Authors acknowledge the financial support of the Ministry of Education, Science and Technological Development of the Republic of Serbia (Project No: 45020).

References

  1. 1.
    Obara K, Mizutani M, Hitomi Y, Yajima H, Kondo K (2009) Isohumolones, the bitter component of beer, improve hyperglycemia and decrease body fat in Japanese subjects with prediabetes. Clin Nutr 28(3):278–284.  https://doi.org/10.1016/j.clnu.2009.03.012 CrossRefGoogle Scholar
  2. 2.
    Gerhäuser C (2005) Beer constituents as potential cancer chemopreventive agents. Eur J Cancer 41(13):1941–1954.  https://doi.org/10.1016/j.ejca.2005.04.012 CrossRefGoogle Scholar
  3. 3.
    Rendall R, Reis MS, Cristina Pereira A, Pestana C, Pereira V, Marques JC (2015) Chemometric analysis of the volatile fraction evolution of Portuguese beer under shelf storage conditions. Chemom Intell Lab Syst 142:131–142.  https://doi.org/10.1016/j.chemolab.2015.01.015 CrossRefGoogle Scholar
  4. 4.
    Alcázar A, Jurado JM, Palacios-Morillo A, de Pablos F, Martín MJ (2012) Differentiation of blonde beers according to chemical quality indicators by means of pattern recognition techniques. Food Anal Methods 5(4):795–799.  https://doi.org/10.1007/s12161-011-9313-2 CrossRefGoogle Scholar
  5. 5.
    Cajka T, Riddellova K, Tomaniova M, Hajslova J (2010) Recognition of beer brand based on multivariate analysis of volatile fingerprint. J Chromatogr A 1217(25):4195–4203.  https://doi.org/10.1016/j.chroma.2009.12.049 CrossRefGoogle Scholar
  6. 6.
    Floridi S, Montanari L, Marconi O, Fantozzi P (2003) Determination of free phenolic acids in wort and beer by coulometric array detection. J Agric Food Chem 51(6):1548–1554.  https://doi.org/10.1021/jf0260040 CrossRefGoogle Scholar
  7. 7.
    Rehová L, Skeríková V, Jandera P (2004) Optimisation of gradient HPLC analysis of phenolic compounds and flavonoids in beer using a CoulArray detector. J Sep Sci 27(15–16):1345–1359.  https://doi.org/10.1002/jssc.200401916 CrossRefGoogle Scholar
  8. 8.
    Nardini M, Ghiselli A (2004) Determination of free and bound phenolic acids in beer. Food Chem 84(1):137–143.  https://doi.org/10.1016/S0308-8146(03)00257-7 CrossRefGoogle Scholar
  9. 9.
    Vanbeneden N, Delvaux F, Delvaux FR (2006) Determination of hydroxycinnamic acids and volatile phenols in wort and beer by isocratic high-performance liquid chromatography using electrochemical detection. J Chromatogr A 1136(2):237–242.  https://doi.org/10.1016/j.chroma.2006.11.001 CrossRefGoogle Scholar
  10. 10.
    Bartolomé B, Peña-Neira A, Gómez-Cordovés C (2000) Phenolics and related substances in alcohol-free beers. Eur Food Res Technol 210(6):419–423.  https://doi.org/10.1007/s002170050574 CrossRefGoogle Scholar
  11. 11.
    Ceslova L, Holcapek M, Fidler M, Drstickova J, Lisa M (2009) Characterization of prenylflavonoids and hop bitter acids in various classes of Czech beers and hop extracts using high-performance liquid chromatography–mass spectrometry. J Chromatogr A 1216(43):7249–7257.  https://doi.org/10.1016/j.chroma.2009.09.022 CrossRefGoogle Scholar
  12. 12.
    Quifer-Rada P, Vallverdú-Queralt A, Martínez-Huélamo M, Chiva-Blanch G, Jáuregui O, Estruch R, Lamuela-Raventós R (2015) A comprehensive characterisation of beer polyphenols by high resolution mass spectrometry (LC–ESI-LTQ-Orbitrap-MS). Food Chem 169:336–343.  https://doi.org/10.1016/j.foodchem.2014.07.154 CrossRefGoogle Scholar
  13. 13.
    Luterotti S, Kljak K (2010) Spectrophotometric estimation of total carotenoids in cereal grain products. Acta Chim Slov 57(4):781–787Google Scholar
  14. 14.
    Sádecká J, Uríčková V, Hroboňová K, Májek P (2015) Classification of juniper-flavoured spirit drinks by multivariate analysis of spectroscopic and chromatographic data. Food Anal Methods 8(1):58–69.  https://doi.org/10.1007/s12161-014-9869-8 CrossRefGoogle Scholar
  15. 15.
    Almeida C, Duarte IF, Barros A, Rodrigues J, Spraul M, Gil AM (2006) Composition of beer by 1H NMR spectroscopy: effects of brewing site and date of production. J Agric Food Chem 54(3):700–706.  https://doi.org/10.1021/jf0526947 CrossRefGoogle Scholar
  16. 16.
    Inon FA, Garrigues S, De la Guardia M (2006) Combination of mid- and near-infrared spectroscopy for the determination of the quality properties of beers. Anal Chim Acta 571:167–174.  https://doi.org/10.1016/j.aca.2006.04.070 CrossRefGoogle Scholar
  17. 17.
    Lachenmeier DW (2007) Rapid quality control of spirit drinks and beer using multivariate data analysis of Fourier transform infrared spectra. Food Chem 101:825–832.  https://doi.org/10.1016/j.foodchem.2005.12.032 CrossRefGoogle Scholar
  18. 18.
    Christensen J, Norgaard L, Bro R, Englesen SB (2006) Multivariate autofluorescence of intact food systems. Chem Rev 106(6):1979–1989.  https://doi.org/10.1021/cr050019q CrossRefGoogle Scholar
  19. 19.
    Sadecka J, Jakubíkova M, Majek P (2018) Fluorescence spectroscopy for discrimination of botrytized wines. Food Control 88:75–84.  https://doi.org/10.1016/j.foodcont.2017.12.033 CrossRefGoogle Scholar
  20. 20.
    Lenhardt L, Zeković I, Dramićanin T, Dramićanin MD, Bro R (2014) Determination of the botanical origin of honey by front face synchronous fluorescence spectroscopy. Appl Spectrosc 68(5):557–563.  https://doi.org/10.1366/13-07325 CrossRefGoogle Scholar
  21. 21.
    Sikorska E, Gliszczynska-Swigłl A, Insinska-Rak M, Khmelinskii I, De Keukeleire D, Sikorski M (2008) Simultaneous analysis of riboflavin and aromatic amino acids in beer using fluorescence and multivariate calibration methods. Anal Chim Acta 613(2):207–217.  https://doi.org/10.1016/j.aca.2008.02.063 CrossRefGoogle Scholar
  22. 22.
    Sikorska E, Górecki T, Khmelinskii IV, Sikorski M, De Keukeleire D (2006) Monitoring beer during storage by fluorescence spectroscopy. Food Chem 96(4):632–639 https://lib.ugent.be/catalog/pug01:412896 CrossRefGoogle Scholar
  23. 23.
    Tan J, Li R, Jiang ZT (2015) Chemometric classification of Chinese lager beers according to manufacturer based on data fusion of fluorescence, UV and visible spectroscopies. Food Chem 184:30–36.  https://doi.org/10.1016/j.foodchem.2015.03.085 CrossRefGoogle Scholar
  24. 24.
    Sikorska E, Gorecki T, Khmelinskii IV, Sikorski M, De Keukeleire D (2004) Fluorescence spectroscopy for characterization and differentiation of beers. J Inst Brew 110(4):267–275.  https://doi.org/10.1002/j.2050-0416.2004.tb00621.x CrossRefGoogle Scholar
  25. 25.
    Amigo JM, Marini F (2013) Multiway methods. In: Federico M (ed) Data handling in science and technology. Elsevier, Amsterdam, pp 265–313.  https://doi.org/10.1016/B978-0-444-59528-7.00007-7 Google Scholar
  26. 26.
    Lenhardt L, Zeković I, Dramićanin T, Bro R, Dramićanin M (2018) Modeling food fluorescence with PARAFAC. In: Geddes CD (ed) Reviews in fluorescence 2017, reviews in fluorescence. Springer, Basel, pp 161–197.  https://doi.org/10.1007/978-3-030-01569-5_8 CrossRefGoogle Scholar
  27. 27.
    Bro R (1999) Exploratory study of sugar production using fluorescence spectroscopy and multi way analysis. Chemom Intell Lab Syst 46(2):133–147.  https://doi.org/10.1016/S0169-7439(98)00181-6 CrossRefGoogle Scholar
  28. 28.
    Callejón RM, Amigo JM, Pairo E, Garmón S, Ocaña JA, Morales ML (2012) Classification of Sherry vinegars by combining multidimensional fluorescence, PARAFAC and different classification approaches. Talanta 88:456–462.  https://doi.org/10.1016/j.talanta.2011.11.014 CrossRefGoogle Scholar
  29. 29.
    Christensen J, Miquel Becker E, Frederiksen CS (2005) Fluorescence spectroscopy and PARAFAC in the analysis of yogurt. Chemom Intell Lab Syst 75(2):201–208.  https://doi.org/10.1016/j.chemolab.2004.07.007 CrossRefGoogle Scholar
  30. 30.
    Lenhardt L, Bro R, Zeković I, Dramićanin T, Dramićanin MD (2015) Fluorescence spectroscopy coupled with PARAFAC and PLS DA for characterization and classification of honey. Food Chem 175:284–291.  https://doi.org/10.1016/j.foodchem.2014.11.162 CrossRefGoogle Scholar
  31. 31.
    Murphy KR, Stedmon CA, Graeber D, Bro R (2013) Fluorescence spectroscopy and multi-way techniques PARAFAC. Anal Methods 5(23):6557–6566.  https://doi.org/10.1039/C3AY41160E CrossRefGoogle Scholar
  32. 32.
    Lenhardt L, Zeković I, Dramićanin T, Milićević B, Burojević J, Dramićanin MD (2017) Characterization of cereal flours by fluorescence spectroscopy coupled with PARAFAC. Food Chem 229:165–171.  https://doi.org/10.1016/j.foodchem.2017.02.070 CrossRefGoogle Scholar
  33. 33.
    Elcoroaristizabal S, Bro R, García JA, Alonso L (2015) PARAFAC models of fluorescence data with scattering: a comparative study. Chemom Intell Lab Syst 142:124–130.  https://doi.org/10.1016/j.chemolab.2015.01.017 CrossRefGoogle Scholar
  34. 34.
    Bro R (1997) PARAFAC. Tutorial and applications. Chemom Intell Lab Syst 38(2):149–171.  https://doi.org/10.1016/S0169-7439(97)00032-4 CrossRefGoogle Scholar
  35. 35.
    Bro R, Kiers HAL (2003) A new efficient method for determining the number of components in PARAFAC models. J Chemom 17(5):274–286.  https://doi.org/10.1002/cem.801 CrossRefGoogle Scholar
  36. 36.
    Smilde A, Bro R, Geladi P (2004) Selecting the number of components. In: Multi-way analysis: applications in the chemical sciences. Wiley, Chichester, pp 156–166. (Chapter 7).  https://doi.org/10.1002/0470012110.ch7 CrossRefGoogle Scholar
  37. 37.
    Indahl UG, Martens H, Næs T (2007) From dummy regression to prior probabilities in PLS DA. J Chemom 21(12):529–536.  https://doi.org/10.1002/cem.1061 CrossRefGoogle Scholar
  38. 38.
    Nocairi H, Qannari EM, Vigneau E, Bertrand D (2005) Discrimination on latent components with respect to patterns. Application to multicollinear data. Comput Stat Data An 48(1):139–147.  https://doi.org/10.1016/j.csda.2003.09.008 CrossRefGoogle Scholar
  39. 39.
    de Ridder D, Tax DM, Lei B, Xu G, Feng M, Zou Y, van der Heijden F (2017) State estimation. In: Classification, parameter estimation and state estimation: in de Ridder D. Tax DM, Lei B, Xu G, Feng M, Zou Y, van der Heijden F (eds) An engineering approach using MatLab, 1st edn. Wiley, Chichester, pp 115–205. (Chapter 5).  https://doi.org/10.1002/9781119152484.ch5
  40. 40.
    Sikorska E, Khmelinskii I, Sikorski M (2009) Fluorescence methods for analysis of beer. In: Preedy VR (ed) Beer in health and disease prevention, 4th edn. Elsevier, London, pp 963–976CrossRefGoogle Scholar
  41. 41.
    Hough JS (1982) Malting and brewing science, Vol. 2: hopped wort and beer, chapter 22 chemical and physical properties of beer. Aspen Publishers, Gaithersburg, p 1982CrossRefGoogle Scholar
  42. 42.
    Pai TV, Sawant SY, Ghatak AA, Chaturvedi PA, Gupte AM, Desai NS (2013) Characterization of Indian beers: chemical composition and antioxidant potential. J Food Sci Technol 52(3):1414–1423.  https://doi.org/10.1007/s13197-013-1152-2 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Vinča Institute of Nuclear SciencesUniversity of BelgradeBelgradeSerbia

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