Un enfoque integral de metabolómica para mejorar la detección de prácticas de promotores del crecimiento en el ganado

Ensuring food safety requires robust screening approaches capable of detecting the administration of growth-promoting agents in livestock. While previous metabolomics studies have demonstrated proof-of-concept, most were limited to a single class of compounds and offered only partial metabolome coverage due to the use of individual analytical platforms. The present study aimed to develop a comprehensive classification model through a more global exploration of the metabolome. This integrative workflow represents a major step towards implementing effect-based metabolomics screening within official food control frameworks. A total of 502 urine samples from six experiments (n = 59 cattle) involving β-agonists, steroids, and SARMs were analysed using four complementary LC-HRMS platforms. This multi-platform approach expanded metabolome coverage by capturing a wide range of molecular fingerprints. Multiblock Consensus-OPLS analysis was applied to integrate the four datasets into a joint modelling strategy, thereby enhancing data interpretation. This strategy provided an innovative framework that increased the predictive power of the classification model and underscored the complementarity of the LC-HRMS techniques. Overall, this comprehensive workflow enabled the efficient classification of samples from animals subjected to multiple anabolic treatments within a single analysis. Such advances not only strengthen detection capabilities but also offer a versatile tool to address key public health concerns.