Total white blood cell counts (WBC) are an important indication of infection diagnosis in both human and veterinary medicine. State-of-the-art WBC counts are performed by flow cytometry combined with light scattering or impedance measurements in the clinical analysis laboratory. These technologies are complex and difficult to be miniaturized into a portable point-of-care (POC) system. Spectroscopy is one of the most powerful technologies for POC miniaturization due to its capacity to analyze low sample quantities, little to no sample preparation, and 'real-time' results. WBC is in the proportion of ~1:1000 to red blood cells (RBC), and the latter dominate visible-near infrared (Vis-NIR) information by their large quantities and hemoglobin absorbance. WBC are difficult to be detected by traditional spectral analysis because their information is contained within the interference of hemoglobin bands.
Herein, we perform a feasibility study for the direct detection of WBC counts in canine blood by Vis-NIR spectroscopy for veterinarian applications, benchmarking current chemometric techniques with self-learning artificial intelligence - a new advanced method for high-accuracy quantification from spectral information. Results show that total WBC counts can be detected by Vis-NIR spectroscopy to an average detection limit of 7.8×109 cells/L, with an R2 of 0.9880 between impedance flow cytometry analysis and spectral quantification. This result opens new possibilities for reagent-less POC technology in infection diagnosis. As WBC counts in dogs range from 5 to 45 ×109 cells/L, the detection limit obtained in this research allows concluding that the combined use of spectroscopy with SL-AI new algorithm is a step towards the existence of portable and miniaturized Spectral POC hemogram analysis.