A Single-Wavelength Near-Infrared Photoacoustic Spectroscopy for Noninvasive Glucose Detection Using Machine Learning.

According to the International Diabetes Federation, 589 million adults worldwide live with diabetes in 2025 (approximately 1 in 9 adults). The development of convenient noninvasive blood glucose monitoring systems has been a central focus in diabetes management. Optical spectroscopy has advanced significantly among all noninvasive glucose detection techniques. A photoacoustic system has been developed using a single-wavelength near-infrared laser, operating at 1625 nm, where glucose exhibits an overtone absorption band with relatively low water interference. The noninvasive system has been evaluated using artificial skin phantoms, with different glucose concentrations, covering both normoglycemic and hyperglycemic blood glucose levels. The detection sensitivity of the developed system has been enhanced to ±15 mg/dL across the entire clinically relevant glucose range. K-nearest neighbours and wide neural network machine learning models were developed for noninvasive glucose classification. The models achieved prediction accuracies of 80.0% and 81.5%, respectively, with 100% of the predicted data located within zones A and B of Clarke's error grid analysis. These findings satisfy the regulatory requirements for glucose monitors established by Health Canada and the U.S. Food and Drug Administration.