According to the International Diabetes Federation, 530 million people worldwide have diabetes, with more than 6.7 million reported deaths in 2021. Monitoring blood glucose levels is essential for individuals with diabetes, and developing noninvasive monitors has been a long-standing aspiration in diabetes management. The ideal method for monitoring diabetes is to obtain the glucose concentration level with a fast, accurate, and pain-free measurement that does not require blood drawing or a surgical operation. Multiple noninvasive glucose detection techniques have been developed, including bio-impedance spectroscopy, electromagnetic sensing, and metabolic heat conformation. Nevertheless, reliability and consistency challenges were reported for these methods due to ambient temperature and environmental condition sensitivity. Among all the noninvasive glucose detection techniques, optical spectroscopy has rapidly advanced. A photoacoustic system has been developed using a single wavelength quantum cascade laser, lasing at a glucose fingerprint of 1080 cm${}^{-1}$ for noninvasive glucose monitoring. The system has been examined using artificial skin phantoms, covering the normal and hyperglycemia blood glucose ranges. The detection sensitivity of the system has been improved to $\pm 25$ mg/dL using a single wavelength for the entire range of blood glucose. Machine learning has been employed to detect glucose levels using photoacoustic spectroscopy in skin samples. Ensemble machine learning models have been developed to measure glucose concentration using classification techniques. The model has achieved a 90.4% prediction accuracy with 100% of the predicted data located in zones A and B of Clarke’s error grid analysis. This finding fulfills the US Food and Drug Administration requirements for glucose monitors.

The combination of mid-infrared and photoacoustic spectroscopy has shown promising developments as a substitute for invasive glucose detection technology. A dual single-wavelength quantum cascade laser system has been developed using photoacoustic spectroscopy for noninvasive glucose monitoring. Biomedical skin phantoms with similar properties to human skin have been prepared with blood components at different glucose concentrations as test models for the setup. The detection sensitivity of the system has been improved to ± 12.5 mg/dL in the hyperglycemia blood glucose ranges. An ensemble machine learning classifier has been developed to predict the glucose level in the presence of blood components. The model, which was trained with 72,360 unprocessed datasets, achieved a 96.7% prediction accuracy with 100% of the predicted data located in zones A and B of Clarke’s error grid analysis. These findings fulfill both the US Food and Drug Administration and Health Canada requirements for glucose monitors.

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.