Non-Targeted Identification of Brine Covered Canned Tuna Species Using Front-Face Fluorescence Spectroscopy Combined with Chemometric Tools

  • Ferdaous Boughattas
  • Bruno Le Fur
  • Romdhane KarouiEmail author


The most common frauds of tuna cans supply chain concern the substitution or mixing of valuable tuna species with cheaper ones, which is strictly forbidden. The objective of the present study was to determine the potential use of front-face fluorescence spectroscopy (FFFS) as a rapid tool to authenticate species in canned tuna: skipjack tuna (Katsuwonus pelamis), yellowfin tuna (Thunnus albacares), Albacore tuna (Thunnus alalunga), and bigeye tuna (Thunnus obesus). Different spectra (tryptophan residues, aromatic amino acids, and nucleic acids (AAA + NA), riboflavin, nicotinamide adenine dinucleotide (NADH), and vitamin A) were recorded on 256 canned tunas, produced at the pilot scale, that were used for the establishment of models. The robustness of the established models was tested on 40 commercial canned tunas. According to the label tunas, the percentage of correct classification reached 75% allowing us to conclude that FFFS may represent a promising tool to be used by both canning industry and governmental control agencies to ascertain correct labeling of canned tuna.


Fluorescence Identification Bigeye tuna (Thunnus obesusYellowfin tuna (Thunnus albacaresSkipjack tuna (Katsuwonus pelamisAlbacore tuna (Thunnus alalunga


Funding Information

This study is a part of the IDThon project supported by the French Region of Hauts-de-France and Bpi France. The authors received financial support from the Hauts-de-France Council. This work has been carried out in the framework of the ALIBIOTECH project, which is financed by the European Union, the French State, and the French Region of Hauts-de-France.

Compliance with Ethical Standards

Conflict of Interest

Ferdaous Boughattas declares that she has no conflict of interest. Bruno Le Fur declares that he has no conflict of interest. Romdhane Karoui declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Not applicable


  1. Bojolly D, Doyen P, Le Fur B et al (2017) Development of a qPCR method for the identification and quantification of two closely related tuna species, bigeye tuna (Thunnus obesus) and yellowfin tuna (Thunnus albacares) in canned tuna. J Agric Food Chem:241–243.
  2. Borràs E, Ferré J, Boqué R, Mestres M, Aceña L, Busto O (2015) Data fusion methodologies for food and beverage authentication and quality assessment – a review. Anal Chim Acta 891:1–14. CrossRefGoogle Scholar
  3. Chuang P-S, Chen M-I, Shiao J-C (2012) Identification of tuna species by a real-time polymerase chain reaction technique. Food Chem 133:1055–1061. CrossRefGoogle Scholar
  4. Collette BB, Nauen CE (1983) Fao species catalogue. FAO Fish Synop 2Google Scholar
  5. Dominguez-Vidal A, Pantoja-de la Rosa J, Cuadros-Rodríguez L, Ayora-Cañada MJ (2016) Authentication of canned fish packing oils by means of Fourier transform infrared spectroscopy. Food Chem 190:122–127. CrossRefGoogle Scholar
  6. Etienne M (1998) Méthodes d’évaluation de la qualité. Rech Mar no 18:6–11Google Scholar
  7. Hassoun A, Karoui R (2015) Front-face fluorescence spectroscopy coupled with chemometric tools for monitoring fish freshness stored under different refrigerated conditions. Food Control 54:240–249. CrossRefGoogle Scholar
  8. Karoui R, Blecker C (2011) Fluorescence spectroscopy measurement for quality assessment of food systems—a review. Food Bioprocess Technol 4:364–386. CrossRefGoogle Scholar
  9. Karoui R, Laguet A, Dufour É (2003) Fluorescence spectroscopy: a tool for the investigation of cheese melting – correlation with rheological characteristics. Lait 83:251–264. CrossRefGoogle Scholar
  10. Karoui R, Dufour É, Pillonel L et al (2004) Determining the geographic origin of Emmental cheeses produced during winter and summer using a technique based on the concatenation of MIR and fluorescence spectroscopic data. Eur Food Res Technol 219:184–189. CrossRefGoogle Scholar
  11. Karoui R, Martin B, Dufour É (2005) Potentiality of front-face fluorescence spectroscopy to determine the geographic origin of milks from the Haute-Loire department (France). Lait 85:223–236. CrossRefGoogle Scholar
  12. Karoui R, Dufour E, De Baerdemaeker J (2006a) Monitoring the molecular changes by front face fluorescence spectroscopy throughout ripening of a semi-hard cheese. Food Chem 104:409–420. CrossRefGoogle Scholar
  13. Karoui R, Mouazen AM, Dufour E, Pillonel L, Picque D, de Baerdemaeker J, Bosset JO (2006b) Application of the MIR for the determination of some chemical parameters in European Emmental cheeses produced during summer. Eur Food Res Technol 222:165–170CrossRefGoogle Scholar
  14. Karoui R, Mouazen AM, Ramon H, Schoonheydt R, Baerdemaeker JD (2006c) Feasibility study of discriminating the manufacturing process and sampling zone in ripened soft cheeses using attenuated total reflectance MIR and fiber optic diffuse reflectance VIS-NIR spectroscopy. Food Res Int 39:588–597. CrossRefGoogle Scholar
  15. Karoui R, Thomas E, Dufour E (2006d) Utilisation of a rapid technique based on front-face fluorescence spectroscopy for differentiating between fresh and frozen-thawed fish fillets. Food Res Int 39:349–355. CrossRefGoogle Scholar
  16. Karoui R, Lefur B, Grondin C, Thomas E, Demeulemester C, Baerdemaeker JD, Guillard AS (2007) Mid-infrared spectroscopy as a new tool for the evaluation of fish freshness. Int J Food Sci Technol 42:57–64. CrossRefGoogle Scholar
  17. Leriche F, Bordessoules A, Fayolle K, Karoui R, Laval K, Leblanc L, Dufour E (2004) Alteration of raw-milk cheese by Pseudomonas spp.: monitoring the sources of contamination using fluorescence spectroscopy and metabolic profiling. J Microbiol Methods 59:33–41. CrossRefGoogle Scholar
  18. Lopez I, Pardo MA (2005) Application of relative quantification TaqMan real-time polymerase chain reaction technology for the identification and quantification of Thunnus alalunga and Thunnus albacares. J Agric Food Chem 53:4554–4560CrossRefGoogle Scholar
  19. Mahaliyana AS, Jinadasa BKKK, Liyanage NPP et al (2015) Nutritional composition of skipjack tuna (Katsuwonus pelamis) caught from the oceanic waters around Sri Lanka. Am J Food Nutr 3:106–111. Google Scholar
  20. Paine MA, McDowell JR, Graves JE (2007) Specific identification of western Atlantic Ocean scombrids using mitochondrial DNA cytochrome C oxidase subunit I (COI) gene region sequences. Bull Mar Sci 80:353–367Google Scholar
  21. Pegels N, González I, López-Calleja I, García T, Martín R (2013) Detection of fish-derived ingredients in animal feeds by a TaqMan real-time PCR assay. Food Anal Methods 6:1040–1048. CrossRefGoogle Scholar
  22. Reis MM, Martínez E, Saitua E, Rodríguez R, Pérez I, Olabarrieta I (2017) Non-invasive differentiation between fresh and frozen-thawed tuna fillets using near infrared spectroscopy (Vis-NIRS). LWT Food Sci Technol 78:129–137. CrossRefGoogle Scholar
  23. Srikornkarn S, Sirisomboon P (2014) Feasibility of evaluation of salt content in canned sardine in oil by near infrared spectroscopy. Ital Oral Surg 2:381–385. Google Scholar
  24. Unseld M, Brandt P, Hiesel R (1995) Identification of the species origin of highly processed meat products by mitochondrial DNA sequences. Genome Res 4:241–243. CrossRefGoogle Scholar
  25. Wang J, Jun S, Bittenbender HC, Gautz L, Li QX (2009) Fourier transform infrared spectroscopy for kona coffee authentication. J Food Sci 74:385–391. CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Univ. Artois, EA 7394, ICV-Institut Charles VIOLLETTELensFrance
  2. 2.PFINVBoulogne-sur-MerFrance
  3. 3.INRA, USC 1281LilleFrance
  4. 4.ISALilleFrance
  5. 5.UlcoBoulogne-sur-MerFrance
  6. 6.Univ. LilleLilleFrance
  7. 7.ADRIANORTilloy Les MofflainesFrance

Personalised recommendations