Point-of-Care Veterinary Diagnostics Using Vis–NIR Spectroscopy: Current Opportunities and Future Directions

TL;DR

This paper explores how Vis-NIR spectroscopy can enable fast, portable diagnostics in veterinary medicine, with AI helping to overcome technical challenges.

Contribution

The study highlights the integration of Vis-NIR spectroscopy with self-learning AI to enhance veterinary diagnostics at the point of care.

Findings

Vis-NIR spectroscopy is rapid, non-invasive, and suitable for portable devices in veterinary diagnostics.

Combining Vis-NIR with self-learning AI improves accuracy by mitigating spectral interferences.

Applications include hemogram analysis in dogs, cats, and salmon, and parasite detection in sheep.

Abstract

This study explores how Visible-Near-Infrared (Vis-NIR) spectroscopy can transform veterinary diagnostics at the point of care (POC). Unlike more complex laboratory techniques, Vis-NIR is fast, non-invasive, and compatible with portable, low-cost POC devices. By measuring how light interacts with biological samples, this technology provides immediate biochemical results without the need for chemical reagents. Although challenges remain, combining Vis-NIR with Self-Learning Artificial Intelligence (SLAI) is essential to improve accuracy and filter out technical interferences. This synergy not only speeds up diagnosis but also makes healthcare more accessible, directly supporting the One Health initiative.

Visible-Near-Infrared (Vis-NIR) spectroscopy, spanning approximately 400 to 2500 nm, is an innovative technology with growing relevance for diagnostics performed at the point of care (POC). This review explores the potential of Vis-NIR in veterinary medicine, highlighting its advantages over complex techniques like Raman and Fourier transform infrared spectroscopy (FTIR) by being rapid, non-invasive, reagent-free, and compatible with miniaturized, portable devices. The methodology involves directing a broadband light source, often using LEDs, toward the sample (e.g., blood, urine, faeces), collecting spectral information related to molecular vibrations, which is then analyzed using chemometric methods. Successful veterinary applications include hemogram analysis in dogs, cats, and Atlantic salmon, and quantifying blood in ovine faeces for parasite detection. Key limitations include spectral interference from strong absorbers like water and hemoglobin, and the limited penetration depth of light. However, combining Vis-NIR with Self-Learning Artificial Intelligence (SLAI) is shown to isolate and mitigate these multi-scale interferences. Vis-NIR spectroscopy serves as an important complement to centralized laboratory testing, holding significant potential to accelerate clinical decisions, minimize stress on animals during assessment, and improve diagnostic capabilities for both human and animal health, aligning with the One Health concept.

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