What is it about?

This study explores how hyperspectral imaging and artificial intelligence can identify the feeding regimes of broiler chickens without damaging the meat. We analyzed 60 chickens from three different feeding groups and evaluated four anatomical regions. By combining visible and near-infrared light with machine learning, we achieved approximately 98% classification accuracy using breast meat. These findings demonstrate the potential of this technology for verifying poultry feeding practices and improving food traceability.

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Why is it important?

Consumers increasingly demand transparency about how animals are raised and fed. However, verifying poultry feeding practices often relies on documents that may be incomplete or unreliable. Our research demonstrates that hyperspectral imaging and artificial intelligence can provide an objective, non-destructive method for distinguishing chicken feeding regimes. This technology could help prevent food fraud, strengthen supply chain transparency, support quality certification, and increase consumer confidence in poultry products.

Perspectives

These findings open opportunities for developing portable and automated systems to verify poultry feeding practices. Future research should validate the technology across different breeds, farms, diets, and processing conditions. Reducing the number of spectral bands could facilitate the development of affordable multispectral sensors for industrial applications. Integrating these systems with artificial intelligence could support real-time meat authentication, strengthen food traceability, and improve quality assurance throughout the poultry supply chain.

PhD Jimy Oblitas
Universidad Privada del Norte

Read the Original

This page is a summary of: Non-destructive discrimination of broiler chicken (Gallus gallus domesticus) feeding regimes using VIS–NIR hyperspectral imaging and machine learning, European Food Research and Technology, May 2026, Springer Science + Business Media,
DOI: 10.1007/s00217-026-05147-z.
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