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 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 delta 13C, delta 2H, delta 15N, delta 18O and delta 34S 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.

Automated summary

Scientifically supporting geographic origin claims on food labels enhances consumer trust. Stable isotope ratio analysis (SIRA) verifies the origin of food products by examining naturally occurring differences in isotopic compositions. This study conducted SIRA on casein from butter, cheese, and milk powder, and successfully predicted the region of origin using machine learning models based on isotopic values.