New article in Current Research in Food Science

A new publication from Cheng-En Tan and Prof. Ilias Tagkopoulos about Single nucleotide polymorphism information estimates breed and variety composition ratio in food.

Abstract: The quality of food products can be influenced by the breed or variety of origin, as well as the composition ratios in mixtures of breeds or varieties. We present a method to estimate the breed or variety composition ratio in food samples using single-nucleotide polymorphism (SNP) allele frequency data and a non-negative least squares (NNLS) optimization approach. To evaluate the method’s performance, we simulated two datasets (cow and cacao) containing simulated samples with specified breed or variety composition ratios, then compared the predicted ratios to the actual values. Results show that the method estimates the composition ratios of breeds and varieties with significantly lower average absolute error than a uniform probability baseline (4.1 % vs 24.6 % for cows, p-value = 1.9 × 10⁻¹⁷; and 11.8 % vs 24.6 % for cacao, p-value = 1.1 × 10⁻⁸). Additionally, the accuracy of identifying the majority breed or variety in a sample is also significantly higher than assuming equal probability of breed mixing (92 % vs 28 % for cows and 72 % vs 28 % for cacao). The corresponding code for the breed or variety composition ratio estimation is available in the Github repository: (https://github.com/IBPA/NNLS-SNP).

Reference: Tan, Cheng-En, and Ilias Tagkopoulos. “Single Nucleotide Polymorphism Information Estimates Breed and Variety Composition Ratio in Food.” Current Research in Food Science (2026). doi: 10.1016/j.crfs.2026.101312