Beef micronutrients and bioactive compounds reflect distinct physiological patterns across diverse production systems

Summary

This study quantified 14 key micronutrients and bioactive compounds in beef from 489 animals across 53 diverse European farms and used machine learning to identify which farm management practices explain their variation. Heme iron, total iron, and carnosine were best predicted by farm features (R² 0.50–0.76), with heme iron and iron primarily associated with slaughter maturity and animal physiological state, whilst carnosine varied mainly with breed. The remaining compounds showed lower predictive performance, suggesting their concentrations are more tightly physiologically regulated and less sensitive to modifiable farm practices.

Regional applicability

The study was conducted across European farms representing diverse production intensities; UK beef farms may be represented within the sample. Findings on how slaughter age, animal physiological state, and breed influence beef micronutrient composition are directly transferable to UK production systems, though further UK-specific analysis would clarify which farm management levers are most actionable for producers seeking to optimise beef nutrient density.

Key measures

Heme iron, total iron, zinc, selenium, α-tocopherol, carnosine, anserine, thiamin, riboflavin, pantothenic acid, pyridoxal, pyridoxamine, vitamin B6, and cobalamin concentrations in longissimus lumborum muscle; 33 farm management features recorded via structured questionnaires; Random Forest and Conditional Inference Tree models with nested cross-validated R² values

Outcomes reported

The study measured 14 micronutrients and bioactive compounds (heme iron, Fe, Zn, Se, α-tocopherol, carnosine, anserine, B-vitamins) in beef samples from 489 animals across 53 European farms, and modelled their variation in relation to 33 farm management features using machine-learning approaches. Predictive performance varied substantially, with heme iron, Fe, and carnosine showing highest R² values (0.76, 0.55, and 0.50 respectively), whilst other traits showed lower predictive capacity.

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