Advertisement

Modeling Food Fluorescence with PARAFAC

  • Lea Lenhardt Acković
  • Ivana Zeković
  • Tatjana Dramićanin
  • Rasmus Bro
  • Miroslav D. DramićaninEmail author
Chapter
First Online:
Part of the Reviews in Fluorescence book series (RFLU)

Abstract

Parallel factor analysis (PARAFAC) of food fluorescence has found many applications in food science, such as in non-contact and non-destructive food characterization, the detection of food adulteration, and the authentication of geographical and botanical origins of food products. This Chapter presents a theoretical background of the PARAFAC method and a step-by-step guide for the practical use of PARAFAC to model fluorescence excitation-emission matrices and interpret the results. For this purpose, several examples of its use in applications of food fluorescence are presented. PARAFAC can decompose complex excitation-emission matrices into emission and excitation spectra of individual components that contribute to the fluorescence of the investigated sample. These components originate from fluorophores; for this reason, Sect. 8.2 of this Chapter is devoted to the description of fluorophores present in food products. Finally, an extensive overview of literature reports on the use of PARAFAC for modeling food fluorescence is provided. Emphasis is given on the measured EEM spectral ranges, the components used for the PARAFAC modeling, and the intended research aim. This Chapter also presents the use of second-order calibration of PARAFAC scores for the quantitative determination of concentrations of fluorophores.

Keywords

Food fluorescence Parallel factor analysis Fluorophores Food adulteration Food authentication Food characterization Excitation-emission matrices 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This work was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia (Grant No. 45020).

References

  1. 1.
    Fanali C, Dugo L, Mondello L (2016) 10 – Advances in chromatographic techniques for food authenticity testing A2. In: Downey G (ed) Advances in food authenticity testing. Woodhead Publishing, Duxford, pp 253–284.  https://doi.org/10.1016/B978-0-08-100220-9.00010-2 CrossRefGoogle Scholar
  2. 2.
    Cajka T, Showalter MR, Riddellova K, Fiehn O (2016) 7 – Advances in mass spectrometry for food authenticity testing: an omics perspective A2. In: Downey G (ed) Advances in food authenticity testing. Woodhead Publishing, Duxford, pp 171–200.  https://doi.org/10.1016/B978-0-08-100220-9.00007-2 CrossRefGoogle Scholar
  3. 3.
    Maestri E, Marmiroli N (2016) 11 – advances in polymerase chain reaction technologies for food authenticity testing A2. In: Downey G (ed) Advances in food authenticity testing. Woodhead Publishing, Duxford, pp 285–309.  https://doi.org/10.1016/B978-0-08-100220-9.00011-4 CrossRefGoogle Scholar
  4. 4.
    Śliwińska M, Wiśniewska P, Dymerski T, Wardencki W, Namieśnik J (2016) 8 – Advances in electronic noses and tongues for food authenticity testing A2. In: Downey G (ed) Advances in food authenticity testing. Woodhead Publishing, Duxford, pp 201–225.  https://doi.org/10.1016/B978-0-08-100220-9.00008-4 CrossRefGoogle Scholar
  5. 5.
    Sobolev AP, Circi S, Mannina L (2016) 6 – Advances in nuclear magnetic resonance spectroscopy for food authenticity testing A2. In: Downey G (ed) Advances in food authenticity testing. Woodhead Publishing, Duxford, pp 147–170.  https://doi.org/10.1016/B978-0-08-100220-9.00006-0 CrossRefGoogle Scholar
  6. 6.
    Rodriguez-Saona LE, Giusti MM, Shotts M (2016) 4 – Advances in infrared spectroscopy for food authenticity testing A2. In: Downey G (ed) Advances in food authenticity testing. Woodhead Publishing, Duxford, pp 71–116.  https://doi.org/10.1016/B978-0-08-100220-9.00004-7 CrossRefGoogle Scholar
  7. 7.
    Martens H, Naes T (1989) Multivariate calibration. Wiley, ChichesterGoogle Scholar
  8. 8.
    Amigo JM, Marini F (2013) Chapter 7 – Multiway methods. In: Federico M (ed) Data handling in science and technology, vol 28. Elsevier, Amsterdam, pp 265–313.  https://doi.org/10.1016/B978-0-444-59528-7.00007-7 CrossRefGoogle Scholar
  9. 9.
    Kiers HAL, Ten Berge JMF, Bro R (1999) PARAFAC2–Part I. A direct fitting algorithm for the PARAFAC2 model. J Chemom 13(3–4):275–294.  https://doi.org/10.1002/(SICI)1099-128X(199905/08) CrossRefGoogle Scholar
  10. 10.
    Bro R (1997) PARAFAC. Tutorial and applications. Chemometr Intell Lab 38(2):149–171.  https://doi.org/10.1016/S0169-7439(97)00032-4 CrossRefGoogle Scholar
  11. 11.
    Myhovich MI, Kelman VA (2014) Theoretical and experimental study of spectroscopic characteristics of aromatic amino acids. Ukr J Phys 56(06):581–588CrossRefGoogle Scholar
  12. 12.
    Fennema O (2008) Fennema’s food chemistry. CRC Press/Taylor & Francis, Boca RatonGoogle Scholar
  13. 13.
    Vas D (1998) Vitamin A. In: Kirk-Othmer encyclopedia of chemical technology, vol 45. Wiley, New YorkGoogle Scholar
  14. 14.
    WHO Model Formulary (2008) World health organization. Accessed 8 Dec 2016Google Scholar
  15. 15.
    McCormick DB (2006) Vitamin B6. Present knowledge in nutrition. In: Bowman BA, Russell RM (eds) Present knowledge in nutrition, 9th edn. International Life Sciences Institute, Washington, DCGoogle Scholar
  16. 16.
    Bridges JW, Davies DS, Williams RT (1966) Fluorescence studies on some hydroxypyridines including compounds of the vitamin B6 group. Biochem J 98:451–468CrossRefGoogle Scholar
  17. 17.
    Current EU approved additives and their E Numbers (2007) UK food standards agency. Accessed 3 Dec 2009Google Scholar
  18. 18.
    Muller F (1991) Chemistry and biochemistry of flavoenzymes, vol 1. CRC Press/Boca Raton, Boca RatonGoogle Scholar
  19. 19.
    Islam S, Susdorf T, Penzkofer A, Hegemann P (2003) Fluorescence quenching of flavin adenine dinucleotide in aqueous solution by pH dependent isomerisation and photo-induced electron transfer. Chem Phys 295(2):137–149.  https://doi.org/10.1016/j.chemphys.2003.08.013 CrossRefGoogle Scholar
  20. 20.
    Masters BR, Chance B (1993) Fluorescent and luminescent probes for biological activity. WT Mason Academic Press, LondonGoogle Scholar
  21. 21.
    Lakowicz J (2006) Principles of fluorescence spectroscopy. In: Principles of fluorescence spectroscopy, 3rd edn. Plenum Press, New York.  https://doi.org/10.1007/978-0-387-46312-4 CrossRefGoogle Scholar
  22. 22.
    Uttamlal M, Sheila Holmes-Smith A (2008) The excitation wavelength dependent fluorescence of porphyrins. Chem Phys Lett 454(4–6):223–228.  https://doi.org/10.1016/j.cplett.2008.02.012 CrossRefGoogle Scholar
  23. 23.
    Clifford MN, Johnston KL, Knight S, Kuhnert N (2003) Hierarchical scheme for LC-MSn identification of chlorogenic Acids. J Agric Food Chem 51(10):2900–2911.  https://doi.org/10.1021/jf026187q CrossRefPubMedGoogle Scholar
  24. 24.
    Tareke E, Rydberg P, Karlsson P, Eriksson S, Törnqvist M (2002) Analysis of acrylamide, a carcinogen formed in heated foodstuffs. J Agric Food Chem 50(17):4998–5006.  https://doi.org/10.1021/jf020302f CrossRefPubMedGoogle Scholar
  25. 25.
    Kiritsakis AK (1998) Olive oil: from the tree to the table. Food and Nutrition Press, TrumbullGoogle Scholar
  26. 26.
    Gutiérrez-Rosales F, Garrido-Fernández J, Gallardo-Guerrero L, Gandul-Rojas B, Minguez-Mosquera MI (1992) Action of chlorophylls on the stability of virgin olive oil. J Am Oil Chem Soc 69(9):866–871.  https://doi.org/10.1007/bf02636334 CrossRefGoogle Scholar
  27. 27.
    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 CrossRefPubMedGoogle Scholar
  28. 28.
    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 CrossRefPubMedGoogle Scholar
  29. 29.
    Garcia R, Boussard A, Rakotozafy L, Nicolas J, Potus J, Rutledge DN, Cordella CBY (2016) 3D-front-face fluorescence spectroscopy and independent components analysis: a new way to monitor bread dough development. Talanta 147:307–314.  https://doi.org/10.1016/j.talanta.2015.10.002 CrossRefPubMedGoogle Scholar
  30. 30.
    Elcoroaristizabal S, Bro R, García JA, Alonso L (2015) PARAFAC models of fluorescence data with scattering: a comparative study. Chemometr Intell Lab 142:124–130.  https://doi.org/10.1016/j.chemolab.2015.01.017 CrossRefGoogle Scholar
  31. 31.
    Bro R (1999) Exploratory study of sugar production using fluorescence spectroscopy and multi-way analysis. Chemometr Intell Lab 46(2):133–147.  https://doi.org/10.1016/S0169-7439(98)00181-6 CrossRefGoogle Scholar
  32. 32.
    Christensen J, Povlsen VT, Sørensen J (2003) Application of fluorescence spectroscopy and chemometrics in the evaluation of processed cheese during storage. J Dairy Sci 86(4):1101–1107.  https://doi.org/10.3168/jds.S0022-0302(03)73692-3 CrossRefPubMedGoogle Scholar
  33. 33.
    Møller JKS, Parolari G, Gabba L, Christensen J, Skibsted LH (2003) Monitoring chemical changes of dry-cured parma ham during processing by surface autofluorescence spectroscopy. J Agric Food Chem 51(5):1224–1230.  https://doi.org/10.1021/jf025662h CrossRefPubMedGoogle Scholar
  34. 34.
    Christensen J, Becker EM, Frederiksen CS (2005) Fluorescence spectroscopy and PARAFAC in the analysis of yogurt. Chemometr Intell Lab 75(2):201–208.  https://doi.org/10.1016/j.chemolab.2004.07.007 CrossRefGoogle Scholar
  35. 35.
    Andersen CM, Frøst MB, Viereck N (2010) Spectroscopic characterization of low- and non-fat cream cheeses. Int Dairy J 20(1):32–39.  https://doi.org/10.1016/j.idairyj.2009.07.006 CrossRefGoogle Scholar
  36. 36.
    Yaacoub R, Saliba R, Nsouli B, Khalaf G, Rizkallah J, Birlouez-Aragon I (2009) Rapid assessment of neoformed compounds in nuts and sesame seeds by front-face fluorescence. Food Chem 115(1):304–312.  https://doi.org/10.1016/j.foodchem.2008.11.104 CrossRefGoogle Scholar
  37. 37.
    Acharid A, Rizkallah J, Ait-Ameur L, Neugnot B, Seidel K, Särkkä-Tirkkonen M, Kahl J, Birlouez-Aragon I (2012) Potential of front face fluorescence as a monitoring tool of neoformed compounds in industrially processed carrot baby food. LWT Food Sci Technol 49(2):305–311.  https://doi.org/10.1016/j.lwt.2012.06.016 CrossRefGoogle Scholar
  38. 38.
    Hougaard AB, Lawaetz AJ, Ipsen RH (2013) Front face fluorescence spectroscopy and multi-way data analysis for characterization of milk pasteurized using instant infusion. LWT Food Sci Technol 53(1):331–337.  https://doi.org/10.1016/j.lwt.2013.01.010 CrossRefGoogle Scholar
  39. 39.
    Svensson VT, Andersen CM (2014) EEM fluorescence spectroscopy as a fast method to assess the brine composition of salted herring. LWT Food Sci Technol 57(2):775–781.  https://doi.org/10.1016/j.lwt.2014.01.030 CrossRefGoogle Scholar
  40. 40.
    Botelho BG, Oliveira LS, Franca AS (2017) Fluorescence spectroscopy as tool for the geographical discrimination of coffees produced in different regions of Minas Gerais State in Brazil. Food Control 77:25–31.  https://doi.org/10.1016/j.foodcont.2017.01.020 CrossRefGoogle Scholar
  41. 41.
    Tena N, Aparicio R, García-González DL (2012) Chemical changes of thermoxidized virgin olive oil determined by excitation–emission fluorescence spectroscopy (EEFS). Food Res Int 45(1):103–108.  https://doi.org/10.1016/j.foodres.2011.10.015 CrossRefGoogle Scholar
  42. 42.
    Guimet F, Ferré J, Boqué R, Rius FX (2004) Application of unfold principal component analysis and parallel factor analysis to the exploratory analysis of olive oils by means of excitation–emission matrix fluorescence spectroscopy. Anal Chim Acta 515(1):75–85.  https://doi.org/10.1016/j.aca.2004.01.008 CrossRefGoogle Scholar
  43. 43.
    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 CrossRefPubMedGoogle Scholar
  44. 44.
    Elcoroaristizabal S, Callejón RM, Amigo JM, Ocaña-González JA, Morales ML, Ubeda C (2016) Fluorescence excitation–emission matrix spectroscopy as a tool for determining quality of sparkling wines. Food Chem 206:284–290.  https://doi.org/10.1016/j.foodchem.2016.03.037 CrossRefPubMedGoogle Scholar
  45. 45.
    Airado-Rodríguez D, Durán-Merás I, Galeano-Díaz T, Wold JP (2011) Front-face fluorescence spectroscopy: a new tool for control in the wine industry. J Food Compos Anal 24(2):257–264.  https://doi.org/10.1016/j.jfca.2010.10.005 CrossRefGoogle Scholar
  46. 46.
    Cabrera-Bañegil M, Hurtado-Sánchez MC, Galeano-Díaz T, Durán-Merás I (2017) Front-face fluorescence spectroscopy combined with second-order multivariate algorithms for the quantification of polyphenols in red wine samples. Food Chem 220:168–176.  https://doi.org/10.1016/j.foodchem.2016.09.152 CrossRefPubMedGoogle Scholar
  47. 47.
    Airado-Rodríguez D, Galeano-Díaz T, Durán-Merás I, Wold JP (2009) Usefulness of fluorescence excitation−emission matrices in combination with PARAFAC, as fingerprints of red wines. J Agric Food Chem 57(5):1711–1720.  https://doi.org/10.1021/jf8033623 CrossRefPubMedGoogle Scholar
  48. 48.
    Jakubíková M, Sádecká J, Májek P (2015) Determination of adulterants in adulterant-fruit spirit blends using excitation-emission matrix fluorescence spectroscopy. Acta Chim Slov 8:52.  https://doi.org/10.1515/acs-2015-0010 CrossRefGoogle Scholar
  49. 49.
    Coelho C, Aron A, Roullier-Gall C, Gonsior M, Schmitt-Kopplin P, Gougeon RD (2015) Fluorescence fingerprinting of bottled white wines can reveal memories related to sulfur dioxide treatments of the must. Anal Chem 87(16):8132–8137.  https://doi.org/10.1021/acs.analchem.5b00388 CrossRefPubMedGoogle Scholar
  50. 50.
    Włodarska K, Pawlak-Lemańska K, Khmelinskii I, Sikorska E (2016) Explorative study of apple juice fluorescence in relation to antioxidant properties. Food Chem 210:593–599.  https://doi.org/10.1016/j.foodchem.2016.05.007 CrossRefPubMedGoogle Scholar
  51. 51.
    Monago-Maraña O, Durán-Merás I, Galeano-Díaz T, Muñoz de la Peña A (2016) Fluorescence properties of flavonoid compounds. Quantification in paprika samples using spectrofluorimetry coupled to second order chemometric tools. Food Chem 196:1058–1065.  https://doi.org/10.1016/j.foodchem.2015.10.041 CrossRefPubMedGoogle Scholar
  52. 52.
    Mbogning Feudjio W, Ghalila H, Nsangou M, Mbesse Kongbonga YG, Majdi Y (2014) Excitation-emission matrix fluorescence coupled to chemometrics for the exploration of essential oils. Talanta 130:148–154.  https://doi.org/10.1016/j.talanta.2014.06.048 CrossRefPubMedGoogle Scholar
  53. 53.
    Montemurro M, Siano GG, Culzoni MJ, Goicoechea HC (2017) Automatic generation of photochemically induced excitation-emission-kinetic four-way data for the highly selective determination of azinphos-methyl in fruit juices. Sens Actuat B Chem 239:397–404.  https://doi.org/10.1016/j.snb.2016.08.033 CrossRefGoogle Scholar
  54. 54.
    Sajjadi SM, Abdollahi H, Rahmanian R, Bagheri L (2016) Quantifying aflatoxins in peanuts using fluorescence spectroscopy coupled with multi-way methods: resurrecting second-order advantage in excitation–emission matrices with rank overlap problem. Spectrochim Acta A 156:63–69.  https://doi.org/10.1016/j.saa.2015.11.022 CrossRefGoogle Scholar
  55. 55.
    Hashemi J, Kram GA, Alizadeh N (2008) Enhanced spectrofluorimetric determination of aflatoxin B1 in wheat by second-order standard addition method. Talanta 75(4):1075–1081.  https://doi.org/10.1016/j.talanta.2008.01.036 CrossRefPubMedGoogle Scholar
  56. 56.
    Mahedero MC, Díaz NM, Muñoz de la Peña A, Espinosa Mansilla A, Gónzalez Gómez D, Bohoyo Gil D (2005) Strategies for solving matrix effects in the analysis of sulfathiazole in honey samples using three-way photochemically induced fluorescence data. Talanta 65(3):806–813.  https://doi.org/10.1016/j.talanta.2004.08.055 CrossRefPubMedGoogle Scholar
  57. 57.
    Muñoz de la Peña A, Mora Diez N, Mahedero García MC, Bohoyo Gil D, Cañada-Cañada F (2007) A chemometric sensor for determining sulphaguanidine residues in honey samples. Talanta 73(2):304–313.  https://doi.org/10.1016/j.talanta.2007.03.047 CrossRefPubMedGoogle Scholar
  58. 58.
    Morales R, Ortiz MC, Sarabia LA, Sánchez MS (2011) D-optimal designs and N-way techniques to determine sulfathiazole in milk by molecular fluorescence spectroscopy. Anal Chim Acta 707(1–2):38–46.  https://doi.org/10.1016/j.aca.2011.09.014 CrossRefPubMedGoogle Scholar
  59. 59.
    Cañada-Cañada F, Espinosa-Mansilla A, AMdl P, Girón AJ, González-Gómez D (2009) Determination of danofloxacin in milk combining second-order calibration and standard addition method using excitation–emission fluorescence data. Food Chem 113(4):1260–1265.  https://doi.org/10.1016/j.foodchem.2008.08.032 CrossRefGoogle Scholar
  60. 60.
    Rodríguez N, Real BD, Cruz Ortiz M, Sarabia LA, Herrero A (2009) Usefulness of parallel factor analysis to handle the matrix effect in the fluorescence determination of tetracycline in whey milk. Anal Chim Acta 632(1):42–51.  https://doi.org/10.1016/j.aca.2008.10.051 CrossRefPubMedGoogle Scholar
  61. 61.
    Markechová D, Májek P, Sádecká J (2014) Fluorescence spectroscopy and multivariate methods for the determination of brandy adulteration with mixed wine spirit. Food Chem 159:193–199.  https://doi.org/10.1016/j.foodchem.2014.02.085 CrossRefPubMedGoogle Scholar
  62. 62.
    Petter Wold J, Møller Andersen C, Balling Engelsen S (2008) Autofluorescence spectroscopy in food analysis. In: Handbook of food analysis instruments. CRC Press.  https://doi.org/10.1201/9781420045673.ch15 Google Scholar
  63. 63.
    Kothawala DN, Murphy KR, Stedmon CA, Weyhenmeyer GA, Tranvik LJ (2013) Inner filter correction of dissolved organic matter fluorescence. Limnol Oceanogr Methods 11(12):616–630.  https://doi.org/10.4319/lom.2013.11.616 CrossRefGoogle Scholar
  64. 64.
    Rao CR, Mitra SK (1971) Generalized inverse of matrices and its applications. Wiley, New YorkGoogle Scholar
  65. 65.
    Smilde A, Bro R, Geladi P (2005) Algorithms. In: Multi-way analysis with applications in the chemical sciences. Wiley, Chichester, pp 111–144.  https://doi.org/10.1002/0470012110.ch6 CrossRefGoogle Scholar
  66. 66.
    Tomasi G, Bro R (2006) A comparison of algorithms for fitting the PARAFAC model. Comp Stat Data Anal 50(7):1700–1734CrossRefGoogle Scholar
  67. 67.
    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
  68. 68.
    Smilde A, Bro R, Geladi P (2005) Validation and diagnostics. In: Multi-way analysis with applications in the chemical sciences. Wiley, Chichester, pp 145–173.  https://doi.org/10.1002/0470012110.ch7 CrossRefGoogle Scholar
  69. 69.
    Smilde A, Bro R, Geladi P (2005) Applications. In: Multi-way analysis with applications in the chemical sciences. Wiley, Chichester, pp 257–349.  https://doi.org/10.1002/0470012110.ch10 CrossRefGoogle Scholar
  70. 70.
    Sanchez E, Kowalski BR (1988) Tensorial calibration: II. Second-order calibration. J Chemom 2:265–280CrossRefGoogle Scholar
  71. 71.
    Andersson CA, Bro R (2000) The N-way toolbox for MATLAB. Chemometr Intell Lab 52(1):1–4.  https://doi.org/10.1016/S0169-7439(00)00071-X CrossRefGoogle Scholar
  72. 72.
  73. 73.
    Vervliet N, Debals O, Sorber L, Van Barel M, De Lathauwer L (2016) Tensorlab 3.0Google Scholar
  74. 74.
    Helwig NE (2017) Multiway package for RGoogle Scholar
  75. 75.
    Smilde A, Bro R, Geladi P (2005) Preprocessing. In: Multi-way analysis with applications in the chemical sciences. Wiley, Chichester, pp 221–255.  https://doi.org/10.1002/0470012110.ch9 CrossRefGoogle Scholar
  76. 76.
    Murphy KR, Stedmon CA, Graeber D, Bro R (2013) Fluorescence spectroscopy and multi-way techniques. PARAFAC Anal Methods-UK 5(23):6557–6566.  https://doi.org/10.1039/C3AY41160E CrossRefGoogle Scholar
  77. 77.
    Azcarate SM, de Araújo Gomes A, Alcaraz MR, Ugulino de Araújo MC, Camiña JM, Goicoechea HC (2015) Modeling excitation–emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety. Food Chem 184:214–219.  https://doi.org/10.1016/j.foodchem.2015.03.081 CrossRefPubMedGoogle Scholar
  78. 78.
    Silvestri M, Elia A, Bertelli D, Salvatore E, Durante C, Li Vigni M, Marchetti A, Cocchi M (2014) A mid level data fusion strategy for the varietal classification of Lambrusco PDO wines. Chemometr Intell Lab 137:181–189.  https://doi.org/10.1016/j.chemolab.2014.06.012 CrossRefGoogle Scholar
  79. 79.
    Guimet F, Ferré J, Boqué R (2005) Rapid detection of olive–pomace oil adulteration in extra virgin olive oils from the protected denomination of origin “Siurana” using excitation–emission fluorescence spectroscopy and three-way methods of analysis. Anal Chim Acta 544(1–2):143–152.  https://doi.org/10.1016/j.aca.2005.02.013 CrossRefGoogle Scholar
  80. 80.
    Baunsgaard D, Nørgaard L, Godshall MA (2000) Fluorescence of raw cane sugars evaluated by chemometrics. J Agric Food Chem 48(10):4955–4962.  https://doi.org/10.1021/jf000131j CrossRefPubMedGoogle Scholar
  81. 81.
    Pedersen DK, Munck L, Engelsen SB (2002) Screening for dioxin contamination in fish oil by PARAFAC and N-PLSR analysis of fluorescence landscapes. J Chemom 16(8–10):451–460.  https://doi.org/10.1002/cem.735 CrossRefGoogle Scholar
  82. 82.
    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 68:557–563CrossRefGoogle Scholar
  83. 83.
    Zeković I, Lenhardt L, Dramićanin T, Dramićanin MD (2012) Classification of intact cereal flours by front-face synchronous fluorescence spectroscopy. Food Anal Methods 5(5):1205–1213.  https://doi.org/10.1007/s12161-011-9359-1 CrossRefGoogle Scholar
  84. 84.
    Montemurro M, Pinto L, Véras G, de Araújo Gomes A, Culzoni MJ, Ugulino de Araújo MC, Goicoechea HC (2016) Highly sensitive quantitation of pesticides in fruit juice samples by modeling four-way data gathered with high-performance liquid chromatography with fluorescence excitation-emission detection. Talanta 154:208–218.  https://doi.org/10.1016/j.talanta.2016.03.078 CrossRefPubMedGoogle Scholar
  85. 85.
    Alarcón F, Báez ME, Bravo M, Richter P, Escandar GM, Olivieri AC, Fuentes E (2013) Feasibility of the determination of polycyclic aromatic hydrocarbons in edible oils via unfolded partial least-squares/residual bilinearization and parallel factor analysis of fluorescence excitation emission matrices. Talanta 103:361–370.  https://doi.org/10.1016/j.talanta.2012.10.080 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Lea Lenhardt Acković
    • 1
  • Ivana Zeković
    • 1
  • Tatjana Dramićanin
    • 1
  • Rasmus Bro
    • 2
  • Miroslav D. Dramićanin
    • 1
    Email author
  1. 1.Vinča Institute of Nuclear SciencesUniversity of BelgradeBelgradeSerbia
  2. 2.Department of Food Science, Faculty of Life SciencesUniversity of CopenhagenKøbenhavnDenmark

Personalised recommendations

Cite chapter

Buy options