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

Roisin O' Sullivan; Raquel Cama-Moncunill; Michael Salter-Townshend; Olaf Schmidt; Frank J Monahan

doi:10.1016/j.fochx.2023.100858

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

Food Chem X. 2023 Sep 3:19:100858. doi: 10.1016/j.fochx.2023.100858. eCollection 2023 Oct 30.

Authors

Roisin O' Sullivan¹, Raquel Cama-Moncunill¹, Michael Salter-Townshend², Olaf Schmidt¹, Frank J Monahan¹

Affiliations

¹ UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin D04 V1W8, Ireland.
² UCD School of Mathematics and Statistics, University College Dublin, Belfield, Dublin D04 V1W8, Ireland.

Abstract

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 δ¹³C, δ²H, δ¹⁵N, δ¹⁸O and δ³⁴S 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.

Keywords: Authenticity; Butter; Cheese; Geographic; IRMS; Milk.