Food Analytical Methods

, Volume 9, Issue 12, pp 3428–3438 | Cite as

Metabolic Characterization of Caviar Specimens by 1H NMR Spectroscopy: Towards Caviar Authenticity and Integrity

  • Clement Heude
  • Karim Elbayed
  • Tangi Jezequel
  • Mathieu Fanuel
  • Raphael Lugan
  • Dimitri Heintz
  • Philippe Benoit
  • Martial Piotto
Article
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Accepted:

Abstract

The specific metabolic profile of aqueous extracts of caviar samples (n = 91) originating from producers in the Aquitaine region in France was determined using 1H NMR spectroscopy. This metabolic profile was used to develop multivariate statistical models (Soft Independent Modeling by Class Analogy (SIMCA) and Orthogonal Partial Least Square Discriminant Analysis (OPLS-DA)) for the purpose of differentiating the Aquitaine production from other productions. The main purpose of these models is to help Aquitaine producers establish a protected geographical indication (PGI) to protect their know-how in caviar production from aquaculture. The parameters characterizing both the OPLS-DA and the SIMCA models were quite satisfactory, and the analysis of blind test samples with the two models provided a good level of discrimination between Aquitaine caviars and other caviars. The same NMR metabolic profile could also be used to provide an estimation of the freshness of caviar samples and define a shelf life for caviar cans. The first characterization of the different metabolites detected in caviar specimens by 1H NMR spectroscopy is also presented. The statistical models obtained could be used to prevent counterfeiting of Aquitaine caviar and guarantee an adequate level of freshness.

Keywords

Caviar PGI NMR Metabolomics Shelf life OPLS-DA SIMCA 
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Notes

Acknowledgments

This work is a part of the AgriFood GPS project and was supported by Bpifrance (formerly Oséo), Bruker BioSpin France, and the caviar producers from the Aquitaine region.

Compliance with Ethical Standards

Funding

This work is a part of the AgriFood GPS project and was supported by Bpifrance (formerly Oséo) and the caviar producers from the Aquitaine region.

Conflict of Interest

The authors declare that they have no competing interests.

Research Involving Human Participants and/or Animals

This article does not contain any studies with human or animal subjects.

Informed Consent

Not applicable.

Supplementary material

12161_2016_540_MOESM1_ESM.docx (318 kb)
ESM 1 (DOCX 318 kb)

References

  1. Billard R (2002) Sturgeons and caviar. In: Fisheries and Aquaculture vol IIIGoogle Scholar
  2. Birstein VJ, Doukakis P, Sorkin B, DeSalle R (1998) Population aggregation analysis of three caviar-producing species of sturgeons and implications for the species identification of black caviar. Conserv Biol 12:766–775CrossRefGoogle Scholar
  3. Bligh EG, Dyer WJ (1959) A rapid method for total lipid extraction and purification. Can J Biochem Physiol 37:911–917CrossRefGoogle Scholar
  4. Boscari E, Barmintseva A, Pujolar JM, Doukakis P, Mugue N, Congiu L (2014) Species and hybrid identification of sturgeon caviar: a new molecular approach to detect illegal trade. Mol Ecol Ressour 14:489–498CrossRefGoogle Scholar
  5. Czesny S, Dabrowski K, Christensen JE, Eenennaam JV, Doroshov S (2000) Discrimination of wild and domestic origin of sturgeon ova based on lipids and fatty acid analysis. Aquaculture 189:145–153CrossRefGoogle Scholar
  6. Davis AL, Laue ED, Keeler J, Moskau D, Lohman J (1991) Absorption-mode two-dimensional NMR spectra recorded using pulsed field gradients. J Magn Reson 1969 94:637–644Google Scholar
  7. Gilkolaei SR (2010) DNA PCR amplification for species diagnosis of caviar from Caspian Sea sturgeon. J Agric Sci Technol 4:51–61Google Scholar
  8. Godelmann R, Fang F, Humpfer E, Schütz B, Bansbach M, Schäfer H, Spraul M (2013) Targeted and nontargeted wine analysis by 1H NMR spectroscopy combined with multivariate statistical analysis. Differentiation of important parameters: grape variety, geographical origin, year of vintage. J Agric Food Chem 61:5610–5619CrossRefGoogle Scholar
  9. Gong Y, Huang Y, Gao L, Lu J, Hu Y, Xia L, Huang H (2013) Nutritional composition of caviar from three commercially farmed sturgeon species in China. J Food Nutr Res 1:108–112Google Scholar
  10. Gussoni M, Greco F, Vezzoli A, Paleari MA, Moretti VM, Beretta G, Caprino F, Lanza B, Zetta L (2006) Monitoring the effects of storage in caviar from farmed Acipenser transmontanus using chemical, SEM, and NMR methods. J Agric Food Chem 54:6725–6732CrossRefGoogle Scholar
  11. Heude C, Lemasson E, Elbayed K, Piotto M (2015) Rapid assessment of Fish freshness and quality by 1H HR-MAS NMR spectroscopy. Food Anal Methods 8:907–915. doi: 10.1007/s12161-014-9969-5 CrossRefGoogle Scholar
  12. Marcone MF, Wang S, Albabish W, Nie S, Somnarain D, Hill A (2013) Diverse food-based applications of nuclear magnetic resonance (NMR) technology. Food Res Int 51:729–747. doi: 10.1016/j.foodres.2012.12.046 CrossRefGoogle Scholar
  13. Marini F (2013) Chemometrics in food chemistry. Data handling in science and technology. Elsevier, AmsterdamGoogle Scholar
  14. Marini F, Bucci R, Magrì AL, Magrì AD (2006a) Authentication of Italian CDO wines by class-modeling techniques. Chemom Intell Lab Syst 84:164–171. doi: 10.1016/j.chemolab.2006.04.017 CrossRefGoogle Scholar
  15. Marini F, Magrì AL, Bucci R, Balestrieri F, Marini D (2006b) Class-modeling techniques in the authentication of Italian oils from Sicily with a Protected Denomination of Origin (PDO). Chemom Intell Lab Syst 80:140–149. doi: 10.1016/j.chemolab.2005.05.002 CrossRefGoogle Scholar
  16. McKay RT (2011) How the 1D-NOESY suppresses solvent signal in metabonomics NMR spectroscopy: an examination of the pulse sequence components and evolution. Concepts Magn Reson Part A 38A:197–220. doi: 10.1002/cmr.a.20223 CrossRefGoogle Scholar
  17. Monakhova YB, Godelmann R, Hermann A, Kuballa T, Cannet C, Schäfer H, Spraul M, Rutledge DN (2014) Synergistic effect of the simultaneous chemometric analysis of 1H NMR spectroscopic and stable isotope (SNIF-NMR, 18O, 13C) data: application to wine analysis. Anal Chim Acta 833:29–39. doi: 10.1016/j.aca.2014.05.005 CrossRefGoogle Scholar
  18. Oliveri P, Downey G (2012) Multivariate class modeling for the verification of food-authenticity claims. TrAC Trends Anal Chem 35:74–86. doi: 10.1016/j.trac.2012.02.005 CrossRefGoogle Scholar
  19. Oliveri P, Di Egidio V, Woodcock T, Downey G (2011) Application of class-modelling techniques to near infrared data for food authentication purposes. Food Chem 125:1450–1456. doi: 10.1016/j.foodchem.2010.10.047 CrossRefGoogle Scholar
  20. Park KS, Kang KH, Bae EY, Baek KA, Shin MH, Kim DU, Kang HK, Kim KJ, Choi YJ, Im JS (2015) General and biochemical composition of caviar from sturgeon (Acipenser ruthenus) farmed in Korea. Int Food Res J 22:777–781Google Scholar
  21. Rehbein H, Gonzales-Sotelo C, Perez-Martin R, Quinteiro J, Rey-Mendez M, Pryde S, Mackie IM, Santos AT (1999) Differentiation of sturgeon caviar by single strand conformation polymorphism (PCR-SSCP) analysis. Arch Leb 50:1–24Google Scholar
  22. Rucker SP, Shaka AJ (1989) Broadband homonuclear cross polarization in 2D N.M.R. using DIPSI-2. Mol Phys 68:509–517. doi: 10.1080/00268978900102331 CrossRefGoogle Scholar
  23. Shaka AJ, Lee CJ, Pines A (1988) Iterative schemes for bilinear operators; application to spin decoupling. J Magn Reson 77:274–293Google Scholar
  24. Shintu L, Ziarelli F, Caldarelli S (2004) Is high-resolution magic angle spinning NMR a practical speciation tool for cheese samples? Parmigiano Reggiano as a case study. Magn Reson Chem 42:396–401CrossRefGoogle Scholar
  25. Shintu L, Caldarelli S, Franke BM (2007) Pre-selection of potential molecular markers for the geographic origin of dried beef by HR-MAS NMR spectroscopy. Meat Sci 76:700–707CrossRefGoogle Scholar
  26. Spraul M, Schütz B, Rinke P, Koswig S, Humpfer E, Schäfer H, Mörtter M, Fang F, Marx UC, Minoja A (2009) NMR-based multi parametric quality control of fruit juices: SGF profiling. Nutrients 1:148–155CrossRefGoogle Scholar
  27. Trygg J, Wold S (2002) Orthogonal projections to latent structures (O-PLS). J Chemom 16:119–128. doi: 10.1002/cem.695 CrossRefGoogle Scholar
  28. Valentini M, Ritota M, Cafiero C, Cozzolino S, Leita L, Sequi P (2011) The HRMAS-NMR tool in foodstuff characterisation. Magn Reson Chem 49:S121–S125. doi: 10.1002/mrc.2826 CrossRefGoogle Scholar
  29. Williot P, Sabeau L, Gessner J, Arlati G, Bronzi P, Gulyas T, Berni P (2001) Sturgeon farming in Western Europe: recent developments and perspectives. Aquat Living Resour 14:367–374CrossRefGoogle Scholar
  30. Wold S, Sjostrom M (1977) SIMCA: a method for analyzing chemical data in terms of similarity and analogy. In: Kowalski BR (ed) Chemometrics theory and application, American chemical society symposium series 52. American Chemical Society, Washington D.C., pp 243–282CrossRefGoogle Scholar
  31. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2:37–52CrossRefGoogle Scholar
  32. Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58:109–130CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Clement Heude
    • 1
    • 2
  • Karim Elbayed
    • 2
  • Tangi Jezequel
    • 1
  • Mathieu Fanuel
    • 1
  • Raphael Lugan
    • 3
  • Dimitri Heintz
    • 3
  • Philippe Benoit
    • 4
  • Martial Piotto
    • 1
    • 2
  1. 1.Bruker BioSpin FranceWissembourgFrance
  2. 2.IMIS/ICube LaboratoryStrasbourg UniversityStrasbourgFrance
  3. 3.Institut de Biologie Moléculaire des Plantes-UPR 2357StrasbourgFrance
  4. 4.SCEA SturgeonSaint Sulpice et CameyracFrance

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