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Monitoring physiological correlates or biomarkers of welfare in animals – practical applications of vibrational spectroscopy and machine learning
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AI
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DOI：10.1080/05704928.2026.2637926.png)
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Abstract
En 中文
The scientific assessment of animal welfare depends on the ability to accurately and reliably measure objective correlates of animals’ emotional experiences. While animal behavioral events can be recorded easily by trained observers, often with a high degree of inter-observer agreement, physiological or neurobiological markers (biomarkers) are more challenging to measure within the practical contexts of animal rearing and handling. Traditionally their measurement has required the collection of biological samples from the animal, which can be time consuming for animal handlers as well as invasive and stressful for the animal. Multiple assays are required while the interpretation of the results can be challenging, given that most physiological indicators respond to both positive and negative stimuli. Preliminary research on these minimally invasive tools for biological sample analysis has allowed for the development of predictive models of stress and welfare in a variety of species. With recent technological advances, vibrational spectroscopy techniques (NIR and MIR spectroscopy) are now within practical reach for operational facilities with animals under their care. This review aims to present the current state of this research and to discuss the advantages and limitations of using vibrational spectroscopy techniques combined with machine learning to measure biomarkers as physiological correlates of animal welfare.
Keywords:
Infrared
stress
animal welfare
animal welfare assessment
biomarkers
spectroscopy
machine learning
physiological measures
animal-based indicators
Journal
A
IF:
5.4
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779
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3.7K
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