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Scattering-based optical techniques for olive oil characterization and quality control

  • I. Delfino
  • S. Cavella
  • M. LeporeEmail author
Review Paper

Abstract

Olive oil is a major fat source of the Mediterranean diet. For its unique functional and technological properties, olive oil is highly appreciated all over the world. Very sensitive techniques are currently required to determine chemical composition, to evaluate olive oil authenticity and to quantify vegetable adulterants or degradation compounds. A class of techniques that can be particularly interesting in olive oil characterization is represented by those based on light scattering. These techniques can provide important information on physical properties, conservation state and possible adulteration without complicate or time expensive procedures. Among these, static and dynamic light scattering, diffuse wave spectroscopy, different kinds of Raman spectroscopy are the most used. In this short review, basic concepts about the experimental aspects of these techniques are presented together with some of the most generally used data analysis procedures. Some selected examples of the most interesting applications of these techniques are also proposed.

Keywords

Olive oil Olive oil emulsion SLS DLS DWS Raman spectroscopy 

References

  1. 1.
    L. Redondo-Cuevasa, G. Castellano, F. Torrens, V. Raikos, Revealing the relationship between vegetable oil composition and oxidative stability: a multifactorial approach. J. Food Compos. Anal. 66, 221–229 (2018)Google Scholar
  2. 2.
    A.M. Gómez-Caravaca, R.M. Maggio, L. Cerretani, Chemometric applications to asses quality and critical parameters of virgin and extra-virgin oilve oil. A review. Anal. Chim. Acta 913, 1–21 (2016)Google Scholar
  3. 3.
    A. Montaño, M. Hernández, I. Garrido, J.L. Llerena, F. Espinosa, Fatty acid and phenolic compound concentrations in eight different monovarietal virgin olive oils from extremadura and the relationship with oxidative stability. Int. J. Mol. Sci. 17, 1960 (2016)Google Scholar
  4. 4.
    P. Priore, L. Siculella, G.V. Gnoni, Extra virgin olive oil phenols down-regulate lipid synthesis in primary-cultured rat-hepatocytes. J. Nutr. Biochem. 25, 683–691 (2014)Google Scholar
  5. 5.
    S. Lamy, A. Ouanouki, R. Béliveau, R.R. Desrosiers, Olive oil compounds inhibit vascular endothelial growth factor receptor-2 phosphorylation. Exp. Cell Res. 322, 89–98 (2014)Google Scholar
  6. 6.
    European Food Safety Authority, EFSA J. 9, 2033 (2011)Google Scholar
  7. 7.
    S. Portarena, C. Baldacchini, E. Brugnoli, Geographical discriminatio of extra-virgin olive oils from Italian coasts by combaning stable isotope data and carotenoid content within a multivariate analysis. Food Chem. 215, 1–6 (2017)Google Scholar
  8. 8.
    E. Smith, G. Dent, Modern Raman SpectroscopyA Practical Approach. (Wiley, New Jersey, 2005)Google Scholar
  9. 9.
    I.H. Boyaci et al., Dispersive and FT-Raman spectroscopic methods in food analysis. RSC Adv. 5, 56606–56624 (2015)Google Scholar
  10. 10.
    B.J. Berne, R. Pecora, Dynamic Light Scattering (Wiley-Interscience, New York, 1976)Google Scholar
  11. 11.
    M. Kerker, The Scattering of Light, and Other Electromagnetic Radiation (Academic Press, New York, 1969)Google Scholar
  12. 12.
    C.F. Bohren, D.R. Huffman, Absorption and Scattering of Light by Small Particles (Wiley, New York, 1983)Google Scholar
  13. 13.
    A. Ishimaru, Wave Propagation and Scattering in Random Media (Academic Press, New York, 1978)Google Scholar
  14. 14.
    D.J. Pine, D.A. Weitz, P.M. Chaikin, E. Herbolzheimer, Diffusing-wave spectroscopy. Phys. Rev. Lett. 60, 1134–1137 (1988)Google Scholar
  15. 15.
    M. Corredig, M. Alexander, Food emulsions studied by DWS: recent advances. Trends Food Sci. Technol. 19, 67–75 (2008)Google Scholar
  16. 16.
    W. Burchard, in Static and dynamic light scattering from branched polymers and biopolymers, ed. by H. Anger. Light Scattering from Polymers. Advances in Polymer Science (Springer, Berlin, Heidelberg, 1983)Google Scholar
  17. 17.
    C. Henry, Dynamic and static light scattering take different approaches to measuring size. Anal. Chem. 70, 59A–63A (1998)Google Scholar
  18. 18.
    D. Some, Light-scattering-based analysis of biomolecular interactions. Biophys Rev 5, 147–158 (2013)Google Scholar
  19. 19.
    I.N. Serdyuk, N.R. Zaccai, J. Zaccai, Methods in Molecular Biophysics: Structure, Dynamics, Function (Cambridge University Press, Cambridge, 2007)Google Scholar
  20. 20.
    P. Zakharov, F. Scheffold, Advances in dynamic light scattering techniques, ed. by A. Kokhanovsky. Light scattering reviews 4: single light scattering and radiative transfer (Springer, Berlin Heidelberg, 2009), pp. 433–467Google Scholar
  21. 21.
    B. Lorber, F. Fischer, M. Bailly, H. Roy, D. Kern, Protein analysis by dynamic light scattering: methods and techniques for students. Biochem. Mol. Biol. Educ. 40, 372–382 (2012)Google Scholar
  22. 22.
    B.J. Tromberg, L.O. Svaasand, T. Tsay, R.C. Haskell, Properties of photon density waves in multiple-scattering media. Appl. Opt. 32, 607–616 (1993)Google Scholar
  23. 23.
    D.J. Pine, D.A. Weitz, J.X. Zhu, E. Herbolzheimer, Diffusing wave spectroscopy: dynamic light scattering in the multiple scattering limit. J. Phys. France 51, 2101–2127 (1990)Google Scholar
  24. 24.
    Z. Fahimi, F.J. Aangenendt, P. Voudouris, J. Mattsson, H.M. Wyss, Diffusing-wave spectroscopy in a standard dynamic light scattering setup. Phys. Rev. E 96, 062611 (2017)Google Scholar
  25. 25.
    I. Delfino, C. Piccolo, M. Lepore, Experimental study of short- and long-time diffusion regimes of spherical particles in carboxymethylcellulose solutions. Eur. Polym. J. 41, 1772–1780 (2005)Google Scholar
  26. 26.
    I. Delfino, Light scattering methods for tracking gold nanoparticles aggregation induced by biotin–neutravidin interaction. Biophys. Chem. 177–178, 7–13 (2013)Google Scholar
  27. 27.
    I. Delfino, K. Sato, M.D. Harrison, L. Andolfi, A.R. Bizzarri, C. Dennison, S. Cannistraro, Optical spectroscopic investigation of the alkaline transition in umecyanin from horseradish root. Biochemistry 44, 16090–16097 (2005)Google Scholar
  28. 28.
    I. Delfino, S. Cannistraro, Optical investigation of the electron transfer protein azurin–gold nanoparticle system. Biophys. Chem. 139, 1–7 (2009)Google Scholar
  29. 29.
    S.K. Brar, M. Verma, Measurement of nanoparticles by light-scattering techniques. Trends Anal. Chem. 30, 4–17 (2011)Google Scholar
  30. 30.
    J. Stetefeld, S.A. McKenna, T.R. Patel, Dynamic light scattering: a practical guide and applications in biomedical sciences. Biophys Rev 8, 409–427 (2016)Google Scholar
  31. 31.
    D.E. Koppel, Analysis of macromolecular polydispersity in intensity correlation spectroscopy: the method of cumulants. J. Chem. Phys. 57, 4814–4820 (1972)Google Scholar
  32. 32.
    I.D. Morrison, E.F. Grabowski, C.A. Herb, Improved techniques for particle size determination by quasi-elastic light scattering. Langmuir 1, 496–501 (1985)Google Scholar
  33. 33.
    S.W. Provencher, CONTIN: A general purpose constrained regularization programm for inverting noisy linear algebraic and integral equations. Comput. Phys. Commun. 27, 229–242 (1982)Google Scholar
  34. 34.
    S.W. Provencher, P. Stepanek, Global analysis of dynamic light scattering autocorrelation functions. Part Part Syst Char 13, 291–294 (1996)Google Scholar
  35. 35.
    R.S. Das, Y.K. Agrawal, Raman spectroscopy: Recent advancements, techniques and applications. Vib. Spectrosc. 57, 163–176 (2011)Google Scholar
  36. 36.
    I.R. Lewis, H.G.M. Edwards, Handbook of Raman Spectroscopy (Marcel Dekker Inc, New York, 2001)Google Scholar
  37. 37.
    E.V. Efremov, F. Ariese, C. Gooijer, Achievements in resonance Raman spectroscopy Review of a technique with a distinct analytical chemistry potential. Anal. Chim. Acta 606, 119–134 (2008)Google Scholar
  38. 38.
    S. Schlücker, Surface-enhanced Raman spectroscopy: Z. Angew. Chem. Int. Ed. 53, 4756–4795 (2014)Google Scholar
  39. 39.
    P. Liu, B. Zhou, X. Liu, X. Sun, H. Li, M. Lin, Detection of pesticides in fruits by surface-enhanced Raman spectroscopy coupled with gold nanostructures. Food Bioprocess Technol. 6, 710–718 (2013)Google Scholar
  40. 40.
    A.I. Radu, M. Kuellmer, B. Giese, U. Huebner, K. Weber, D. Cialla-May, J. Popp, Surface-enhanced Raman spectroscopy (SERS) in food analytics: detection of vitamins B2 and B12 in cereals. Talanta 160, 289–297 (2016)Google Scholar
  41. 41.
    A. Camerlingo, F. Zenone, I. Delfino, N. Diano, D.G. Mita, M. Lepore, Investigation on clarified fruit juice by using visible light micro-Raman spectroscopy. Sensors 7, 2049–2061 (2007)Google Scholar
  42. 42.
    I. Delfino, C. Camerlingo, M. Portaccio, B. Della Ventura, L. Mita, M. Lepore, Visible Micro-Raman Spectroscopy for determining glucose content in beverage industry. Food Chem. 127, 735–742 (2011)Google Scholar
  43. 43.
    SERS substrate datasheets https://oceanoptics.com/product/sers/
  44. 44.
    I. Delfino, M. Lepore, R. Tatè, M. Portaccio, Preparation and characterization of Au nanoparticles for theranostic applications, International Electronic Conference on Sensors and Applications 1–16 June 2014, http://www.mdpi.com/journal/sensors
  45. 45.
    C. Camerlingo, M. Portaccio, R. Tatè, M. Lepore, I. Delfino, Surface-Enhanced Raman Spectroscopy Study of Commercial Fruit Juices, Proceedings, vol. 1 (2017), p. 25Google Scholar
  46. 46.
    C. Camerlingo, M. Portaccio, R. Tatè, M. Lepore, I. Delfino, Fructose and pectin detection in fruit-based food products by surface-enhanced raman spectroscopy. Sensors 17, 839 (2017)Google Scholar
  47. 47.
    D. Yang, Y. Ying, Applications of Raman spectroscopy in agricultural products and food analysis: a review. Appl. Spectrosc. Rev. 46, 539–560 (2011)Google Scholar
  48. 48.
    H. Jin, Q. Lu, X. Chen, H. Ding, H. Gao, S. Jin, The use of Raman spectroscopy in food processes: A review. Appl. Spectrosc. Rev. 51, 12–22 (2016)Google Scholar
  49. 49.
    C.A. Teixeira dos Santos, R.N.M.J. Pasco, J.A. Lopes, A review on the application of vibrational spectroscopy in the wine industry: From soil to bottle. Trends Anal. Chem. 88, 108–118 (2017)Google Scholar
  50. 50.
    K. Wang, D.W. Sun, H. Pu, Q. Wei, Principles and applications of spectroscopic techniques for evaluating food protein conformational changes: A review. Trends Food Sci. Technol. 67, 207–219 (2017)Google Scholar
  51. 51.
    T. Yaseen, D.W. Sun, J.H. Cheng, Raman imaging for food quality and safety evaluation: Fundamentals and applications Trends in Food. Sci. Technol. 62, 177–189 (2017)Google Scholar
  52. 52.
    S. Chen, X. Lin, C. Yuen, S. Padmanabhan, R.W. Beuerman, Q. Liu, Recovery of Raman spectra with low signal-to-noise ratio using Wiener estimation. Opt. Express 22, 12102–12114 (2014)Google Scholar
  53. 53.
    D. Chen, Z. Chen, E. Grant, Adaptive wavelet transform suppresses background and noise for quantitative analysis by Raman spectrometry. Anal Bioanal Chem 400, 625–634 (2011)Google Scholar
  54. 54.
    C. Gallo, V. Capozzi, M. Lasalvia, G. Perna, An algorithm for estimation of background signal of Raman spectra from biological cell samples using polynomial functions of different degrees. Vib. Spectrosc. 83, 132–137 (2016)Google Scholar
  55. 55.
    W. Cai, L. Wang, Z. Pan, J. Zuo, C. Xu, X. Shao, Application of the wavelet transform method in quantitative analysis of Raman spectra. J Raman Spectrosc 32, 207–209 (2001)Google Scholar
  56. 56.
    T.T. Cai, D. Zhang, D.D. Ben-Amotz, Enhanced chemical classification of Raman images using multiresolution wavelet transformation. Appl Spectrosc 55, 1124–1130 (2001)Google Scholar
  57. 57.
    G. Schulze, A. Jirasek, M.M.L. .Yu, A. Lim, M.W. Blades, R.F.B. Turner, Accuracy and precision of manual baseline determination. Appl Spectrosc 58, 1488–1489 (2004)Google Scholar
  58. 58.
    C.M. Galloway, E.C. Le Ru, P.G. Etchegoin, An iterative algorithm for background removal in spectroscopy by wavelet transforms. Appl. Spectrosc. 63, 1370–1376 (2009)Google Scholar
  59. 59.
    A.E. Villanueva-Luna, J. Castro-Ramos, S. Vazquez-Montiel, A. Flores-Gil, J.A. Delgado-Atencio, E.E. Orozco-Guillen, Fluorescence and noise subtraction from Raman spectra by using wavelets, optical memory and neural networks, 19, 310–317 (2010)Google Scholar
  60. 60.
    V.D. Hoang, Wavelet-based spectral analysis. Trends Anal. Chem. 62, 144–153 (2014)Google Scholar
  61. 61.
    A. Martyna, A. Michalska, G. Zadora, Interpretation of FTIR spectra of polymers and Raman spectra of car paints by means of likelihood ratio approach supported by wavelet transform for reducing data dimensionality. Anal Bioanal Chem 407, 3357–3376 (2015)Google Scholar
  62. 62.
    F. Qian, Y. Wu, P. Hao, A fully automated algorithm of baseline correction based on wavelet feature points and segment interpolation. Optics Laser Technol. 96, 202–207 (2017)Google Scholar
  63. 63.
    C. Camerlingo, F. Zenone, G.M. Gaeta, R. Riccio, M. Lepore, Wavelet data processing of micro-Raman spectra of biological samples. Meas. Sci. Technol. 17, 298–303 (2006)Google Scholar
  64. 64.
    C. Camerlingo, F. Zenone, G. Perna, V. Capozzi, N. Cirillo, G.M. Gaeta, M. Lepore, An investigation on micro-Raman Spectra and wavelet data analysis for Phemphigus vulgaris follow-up monitoring. Sensors 8, 3656–3664 (2008)Google Scholar
  65. 65.
    C. Camerlingo, F. d’Apuzzo, V. Grassia, L. Perillo, M. Lepore, Micro-Raman spectroscopy for monitoring changes in periodontal ligament and gingival crevicular fluid. Sensors 14, 22552–22563 (2014)Google Scholar
  66. 66.
    I. Delfino, G. Perna, M. Lasalvia, V. Capozzi, L. Manti, C. Camerlingo, M. Lepore, Visible micro-Raman spectroscopy of single human mammary epithelial cells exposed to X-ray radiation. Journal of Biomedical Optics 20, 035003 (2015)Google Scholar
  67. 67.
    Y. Nievergelt, Wavelet Made Easy (Birkhäuser, Boston, 1999)Google Scholar
  68. 68.
    I. Daubechies, Ten Lectures on Wavelets CBMS-NSF, Series in Applied Mathematics (61 SIAM, Philadelphia, 1992)Google Scholar
  69. 69.
    A. Cohen, I. Daubechies, J.Feauveau, Biorthogonal bases of compactly supported wavelets. Commun. Pure Appl. Math. 45, 485–560 (1992)Google Scholar
  70. 70.
    T.C. O’Haver et al. Derivative spectroscopy and its applications in analysis. Anal. Proc. 19, 22–46 (1982)Google Scholar
  71. 71.
    J. Grdadolnik, Infrared difference spectroscopy Part I. Interpretation of the difference spectrum. Vib. Spectrosc. 31, 279–288 (2003)Google Scholar
  72. 72.
    J. Grdadolnik, Y. Maréchal, Infrared difference spectroscopy Part II. Spectral Decomposition. Vib. Spectrosc. 31, 289–294 (2003)Google Scholar
  73. 73.
    H.J. Bowley, S.M.H. Collin, D.L. Gerrard, D.I. James, W.F. Maddams, P.B. Tooke, I.D. Wyatt, The fourier self-deconvolution of Raman spectra. Appl Spectrosc. 39, 1004–1009 (1985)Google Scholar
  74. 74.
    M. Bradley, ‘Curve Fitting in Raman and IR Spectroscopy: Basic Theory of Line Shapes and Applications, Application Note 50733 (Thermo Fisher Scientific, Madison, WI, 2007)Google Scholar
  75. 75.
    M.J. Pelletier, Quantitative analysis using Raman spectrometry. Appl Spectrosc 57, 20A–42A (2003)Google Scholar
  76. 76.
    Y. Hu, J. Liu, W. Li, Resolution of overlapping spectra by curve- fitting. Anal. Chim. Acta 538, 383–389 (2005)Google Scholar
  77. 77.
    H.G. Schulze, C.G. Atkins, D.V. Devine, M.W. Blades, R.F.B. Turner, Fully automated decomposition of Raman spectra into individual Pearson’s type VII distributions applied to biological and biomedical Samples. Appl Spectrosc 69, 26–35 (2015)Google Scholar
  78. 78.
    X. Yuan, R.A. Mayanovic, An empirical study on Raman peak fitting and its application to Raman quantitative research. Appl Spectrosc 71, 2325–2338 (2017)Google Scholar
  79. 79.
    L. Silveira Jr., E. do Carmo Martins, R. Motta, M. Amaro Zângaro, C.J. Tadeu Tavares Pacheco, L.H. de Lima, Moreira, Characterization of nutritional parameters in bovine milk by Raman spectroscopy with least squares modeling. Instrum. Sci. Technol. 44, 85–97 (2016)Google Scholar
  80. 80.
    M. Lepore, M. Portaccio, I. Delfino, L. Sironi, A. La Gatta, A. D’Agostino, E. Izzo, C. Schiraldi, Physico-optical properties of a crosslinked hyaluronic acid scaffold for biomedical applications. J. Appl. Polym. Sci. 134, e45243 (2017).  https://doi.org/10.1002/APP.45243 Google Scholar
  81. 81.
    M. Portaccio, R. Esposito, I. Delfino, M. Lepore, Characterization of secondary structure and FAD conformational state in free and sol–gel immobilized glucose oxidase. J. Sol-Gel. Sci. Technol. 71, 580–588 (2014)Google Scholar
  82. 82.
    C. Camerlingo, F. d’Apuzzo, V. Grassia, G. Parente, L. Perillo, M. Lepore, Micro-Raman spectroscopy during orthodontic tooth movement: follow-up of gingival status. Biophotonics (2015).  https://doi.org/10.1109/BioPhotonics.2015.7304028 Google Scholar
  83. 83.
    F. d’Apuzzo, L. Perillo, I. Delfino, M. Portaccio, M. Lepore, C. Camerlingo, Monitoring early phases of orthodontic treatment by means of Raman spectroscopies. J. Biomed. Opt. 22, 115001 (2017)Google Scholar
  84. 84.
    A.G. Asuero, A. Sayago, A.G. Gonzälez, The correlation coefficient: an overview. Crit Rev. Anal. Chem. 36, 41–59 (2006)Google Scholar
  85. 85.
    M. Blanco, J. Cruz, M. Bautista, Development of a univariate calibration model for pharmaceutical analysis based on NIR spectra. Anal. Bioanal. Chem. 392, 1367–1372 (2008)Google Scholar
  86. 86.
    C. Camerlingo, I. Delfino, G. Perna, V. Capozzi, M. Lepore, Micro-Raman spectroscopy and univariate analysis for monitoring disease follow-up. Sensors 11, 8309–8322 (2011)Google Scholar
  87. 87.
    M. Portaccio, C. Menale, N. Diano, C. Serri, D.G. Mita, M. Lepore, Monitoring production process of cisplatin-loaded PLGA nanoparticle by FT-IR microspectroscopy and univariate data analysis. J. Appl. Polym. Sci. 132, 41305 (2015)Google Scholar
  88. 88.
    M.L. O’Connell, A.G. Ryder, M.N. Leger, T. Howley, Qualitative analysis using Raman spectroscopy and chemometrics: a comprehensive model system for narcotics analysis. Appl Spectrosc. 64, 1109–1121 (2010)Google Scholar
  89. 89.
    L.A. Reisner, A. Cao, A.K. Pandya, An integrated software system for processing, analyzing, and classifying Raman spectra. Chemometr. Intell. Lab 105, 83–90 (2011)Google Scholar
  90. 90.
    Y. Li, J.S. Church, Raman spectroscopy in the analysis of food and pharmaceutical nanomaterials. J. Food Drug Anal. 22, 29–48 (2014)Google Scholar
  91. 91.
    R. Gautam, S. Vanga, F. Ariese, S. Umapathy, Review of multidimensional data processing approaches for Raman and infrared spectroscopy. EPJ Techn. Instrum. 2, 8 (2015)Google Scholar
  92. 92.
    L. Leardl, L. Nørgaard, Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions. J. Chemom. 18, 486–497 (2004)Google Scholar
  93. 93.
    J.L. Lambert, C.C. Pelletier, M. Borchert, Glucose determination in human aqueous humor with Raman spectroscopy. J. Biomed. Opt. 10, 031111–031118 (2005)Google Scholar
  94. 94.
    B.G.M. Vandeginste, D.L. Massart, L.M.C. Buydens, S.D.E. Jong, P.J. Lewi, J. Smeyers-Verbeke, Handbook of Chemometrics and Qualimetrics, Part B. (Elsevier, Amsterdam, 1998)Google Scholar
  95. 95.
    V. Papadimitriou, M. Dulle, W. Wachter, T.G. Sotiroudis, O. Glatter, A. Xenakis, Structure and dynamics of veiled virgin olive oil: influence of production conditions and relation to its antioxidant capacity. Food Biophys. 8, 112–121 (2013)Google Scholar
  96. 96.
    V. Papadimitriou, E.D. Tzika, S. Pispas, T.G. Sotiroudis, A. Xenakis, Microemulsions based on virgin olive oil: A model biomimetic system forstudying native oxidative enzymatic activities. Coll. Surf. A 382, 232–237 (2011)Google Scholar
  97. 97.
    M. Medebach, C. Moitzi, N. Freiberger, O. Glatter, Dynamic light scattering in turbid colloidal dispersions: a comparison between the modified flat-cell light-scattering instrument and 3D dynamic light-scattering instrument. J. Coll. Interf. Sci. 305, 88–93 (2007)Google Scholar
  98. 98.
    H. Yan, X. Chen, H. Song, J. Li, Y. Feng, Z. Shi, X. Wang, Q. Lin, Synthesis of bacterial cellulose and bacterial cellulose nanocrystals for their applications in the stabilization of olive oil pickering emulsion. Food Hydrocoll. 72, 127–135 (2017)Google Scholar
  99. 99.
    M. Alexander, M. Corredig, On line diffusing wave spectroscopy during rheological measurements: a new instrumental setup to measure colloidal instability and structure formation in situ. Food Res. Int. 54, 367–372 (2013)Google Scholar
  100. 100.
    Y. Hemar, D.N. Pinder, R.J. Hunter, H. Singh, P. Hébraud, D.S. Horne, Monitoring of flocculation and creaming of sodium-caseinate-stabilized emulsions using diffusing-wave spectroscopy. J. Coll. Interface Sci. 264, 502–508 (2003)Google Scholar
  101. 101.
    C. Eliot, D.S. Horne, E. Dickinson, Understanding temperature-sensitive caseinate emulsions: new information from diffusing wave spectroscopy. Food Hydrocoll. 19, 279–287 (2005)Google Scholar
  102. 102.
    C. Huck-Iriart, M.S. Álvarez-Cerimedo, R.J. Candal, M.L. Herrera, Structures and stability of lipid emulsions formulated with sodium caseinate. Curr. Opin. Coll. Interface Sci. 16, 412–420 (2011)Google Scholar
  103. 103.
    M.S. Álvarez Cerimedo, C. Huck Iriart, R.J. Candal, M.L. Herrera, Stability of emulsions formulated with high concentrations of sodium caseinate and trehalose. Food Res. Int. 43, 1482–1493 (2010)Google Scholar
  104. 104.
    M. Reufer, A.H.E. Machado, A. Niederquell, K. Bohenenblust, B. Muller, A.C. Volker, M. Kuentz, Introducing diffusing wave spectroscopy as a process analytical tool for pharmaceutical emulsion manufacturing. J. Pharm. Sci. 103, 3902–3913 (2014)Google Scholar
  105. 105.
    O. Mengual, G. Meunier, I. Cayre, K. Puech, P. Snabre, TURBISCAN MA 2000: multiple light scattering measurement for concentrated emulsion and suspension instability analysis. Talanta 50, 445–456 (1999)Google Scholar
  106. 106.
    E. Sánchez-López, M.I. Sánchez-Rodrìguez, A. Marinas, J.M. Marinas, F.J. Urbano, J.M. Caridad, M. Moalem, Chemometric study of Andalusian extra virgin olive oils Raman spectra: Qualitative and quantitative information. Talanta 156–157, 180–190 (2016)Google Scholar
  107. 107.
    A. Naveed, M. Saleem, H. Ali, M. Bilal, S. Khan, U. Rahat, M. Ahmed, S. Mahmood, Defining the temperature range for cooking with extra virgin olive oil using Raman spectroscopy. Laser Phys. Lett. 14, 095603 (2017)Google Scholar
  108. 108.
    C. Camerlingo, M. Portaccio, M. Lepore, Olive oil characterization by visible micro Raman spectroscopy in Proceedings of 2nd IMEKOFOODS Promoting Objective and Measurable Food Quality & Safety October, 2–5 2016 Benevento (Italy) pp. 287–290Google Scholar
  109. 109.
    G.Y. Tiryaki, H. Ayvaz, Quantification of soybean oil adulteration in extra virgin olive oil using portable Raman spectroscopy. Food Meas. 11, 523–529 (2017)Google Scholar
  110. 110.
    R.M. El-Abassy, P. Donfack, A. Materny, Visible Raman spectroscopy for the discrimination of olive oils from different vegetable oils and the detection of adulteration. J. Raman Spectrosc. 40, 1284–1289 (2009)Google Scholar
  111. 111.
    D. Ryoo, J. Hwang, H. Chung, Probing temperature able to improve Raman spectroscopic discrimination of adulterated olive oils. Microchem. J. 134, 224–229 (2017)Google Scholar
  112. 112.
    B.R. Alvarenga, F.A.N. Xavier, F.L.F. Soares, R.L. Carneiro, Thermal stability assessment of vegetable oils by Raman spectroscopy and chemometrics. Food Anal. Methods (2018).  https://doi.org/10.1007/s12161-018-1160-y Google Scholar
  113. 113.
    I. Gouvinhas I, N. Machado, T. Carvalho, J.M.M.M. De Almeida, Short wavelength Raman Spectroscopy applied to the discrimination and characterization of three cultivars of extra virgin olive oils in different maturation stages. Talanta 132, 829–835 (2015)Google Scholar
  114. 114.
    T.O. Mendes, R.A. da Rocha, B.L.S. Porto, M.A.L. de Oliveira, V. de C. dos Anjos, Quantification of extra-virgin olive oil adulteration with soybean oil: a comparative study of NIR, MIR, and Raman spectroscopy associated with chemometric approaches. Food Anal. Methods 8, 2339–2346 (2015)Google Scholar
  115. 115.
    M. Wrona, J. Salafranca, M. Rocchia, C. Nerín, Application of SERS to the determination of butylated hydroxyanisole in edible and essential oils. Spectroscopy 30, 40–45 (2015)Google Scholar
  116. 116.
    W.N. Lian et al., Rapid detection of copper chlorophyll in vegetable oils based on surface-enhanced Raman spectroscopy. Food Addit. Contam. 32, 627–634 (2015)Google Scholar
  117. 117.
    P. Zhang et al., A one-step green route to synthesize copper nanocrystals and their applications in catalysis and surface enhanced Raman scattering Nanoscale, 6, 5343–5350 (2014)Google Scholar

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Authors and Affiliations

  1. 1.Dipartimento di Scienze Ecologiche e BiologicheUniversità della TusciaViterboItaly
  2. 2.Dipartimento di AgrariaUniversità “Federico II”NapoliItaly
  3. 3.Dipartimento di Medicina SperimentaleUniversità “Luigi Vanvitelli”NapoliItaly

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