Stable isotope and multi-elemental fingerprinting for seafood authenticity: discriminating production type and geographical origin.

Authors

Brina Pavlovič, Lidija Strojnik, Doris Potočnik, Marta Jagodic Hudobivnik, Darja Mazej, Luana Bontempo, Nives Ogrinc

Published in

Food chemistry. Volume 531. Pages 151243. Sep 28, 2026. Epub Sep 28, 2026.

Abstract

Stable isotope and multi-element fingerprinting were applied to discriminate seafood by production method and geographical origin. A total of 222 samples were analysed, comprising 162 gilthead seabream (Sparus aurata) samples (112 farmed and 50 wild) collected from the Mediterranean, Adriatic, and Atlantic regions, and 60 Mediterranean mussel (Mytilus galloprovincialis) samples from Portugal (30 farmed and 30 wild). In addition, gill tissues from selected farmed seabream groups were analysed as an exploratory matrix. Stable isotope values (δ¹³C, δ¹⁵N, δ³⁴S, δ²H, δ¹⁸O and δ¹⁸O_water) were determined by IRMS while elemental concentrations were measured by ICP-MS, and the combined dataset was evaluated using chemometric modelling. For seabream, δ¹³C and δ¹⁵N showed clear differences between farmed and wild samples, while an OPLS-DA model based on stable isotope variables achieved 100% classification accuracy within the investigated dataset. δ³⁴S provided additional discriminatory information for production type and geographical origin in seabream, and for production type in mussels. The highest classification performance for geographical origin was achieved by combining isotopic and elemental variables, reaching 95.5% overall accuracy for seabream. The DD-SIMCA model was used for class modelling and verification of Portuguese-origin samples, achieving 96% accuracy within the investigated dataset, with high sensitivity (93%) and specificity (98%). These findings demonstrate the potential of complementary isotopic and elemental fingerprints for seafood authenticity, while broader applicability requires validation using independent and geographically diverse datasets.

PMID:
42828958
Bibliographic data and abstract were imported from PubMed on 04 Oct 2026.

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