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Verifying origin claims on dairy products using stable isotope ratio analysis and random forest classification

Scientifically underpinning geographic origin claims will improve consumer trust in food labels. Stable isotope ratio analysis (SIRA) is an analytical technique that supports origin verification of food products based on naturally occurring differences in isotopic compositions. SIRA of five relevant...

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Detalles Bibliográficos
Autores principales: O' Sullivan, Roisin, Cama-Moncunill, Raquel, Salter-Townshend, Michael, Schmidt, Olaf, Monahan, Frank J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10534209/
https://www.ncbi.nlm.nih.gov/pubmed/37780346
http://dx.doi.org/10.1016/j.fochx.2023.100858
Descripción
Sumario:Scientifically underpinning geographic origin claims will improve consumer trust in food labels. Stable isotope ratio analysis (SIRA) is an analytical technique that supports origin verification of food products based on naturally occurring differences in isotopic compositions. SIRA of five relevant elements (C, H, N, O, S) was conducted on casein isolated from butter (n = 60), cheese (n = 96), and whole milk powder (WMP) (n = 41). Samples were divided into four geographic regions based on their commercial origin: Ireland (n = 79), Europe (n = 67), Australasia (n = 29) and USA (n = 22). A random forest machine learning model built using δ(13)C, δ(2)H, δ(15)N, δ(18)O and δ(34)S values of all products (n = 197) accurately (88% model accuracy rate) predicted the region of origin with class accuracy of 95% for Irish, 84% for European, 71% for Australasia, and 94% for US products.