Shazzad Rassel

Diabetes is a widespread metabolic disease in which the body’s inability to regulate blood glucose levels leads to severe health complications. Current limitations in noninvasive glucose sensing make finger-prick glucometers the standard for personal monitoring despite their discomfort and challenges for frequent measurements. By combining machine learning techniques with transmittance spectroscopy, this study presents a noninvasive approach for estimating BGL in personal healthcare. The system employs multispectral transmittance measurements at 650, 808, and 940 nm to evaluate glucose concentrations in aqueous solutions. Simulation of light absorption in skin layers and in vitro experiments identified 940 nm as the optimal wavelength, offering high sensitivity with minimal interference from water absorption. Using this wavelength, an in vivo system based on transmittance spectroscopy was developed for noninvasive glucose measurement. A machine learning pipeline incorporating ensemble models was implemented to predict glucose levels from optical data. Trained on 200 clinical samples obtained from the in vivo experimental setup, the XGBoost model outperformed other algorithms, achieving an R² score of 0.94 with a root-mean-squared error (RMSE) of 23.92 mg/dL. The Clarke error grid analysis (CEGA) showed that 92.5% of predictions fell within Zone A and 7.5% within Zone B, with no samples in the critical zones (C–E), demonstrating the system’s robustness. These results highlight the potential of the proposed method as a practical, low-cost, and reliable solution for continuous noninvasive glucose monitoring in diabetes management.

Noninvasive blood glucose monitoring is crucial for diabetes management, and photoacoustic spectroscopy (PAS) offers a promising solution by detecting glucose levels through human skin. However, weak acoustic signals in PAS systems require optimized resonator designs for enhanced detection sensitivity. Designing such resonators physically is complex, requiring the precise identification of critical parameters before practical implementation. This study focused on optimizing a T-shaped photoacoustic resonator using finite element modeling in a COMSOL Multiphysics environment. By systematically varying the geometric design parameters of the T-cell resonator, a maximum increase in the pressure amplitude of 12.76 times with a quality factor (Q-factor) of 47.5 was achieved compared to the previously designed reference acoustic resonator. This study took a significant step forward by identifying key geometric parameters that influence resonator performance, paving the way for more sensitive and reliable noninvasive glucose monitoring systems.

Noninvasive blood glucose monitoring is crucial for diabetes management, and photoacoustic spectroscopy (PAS) offers a promising solution by detecting glucose levels through the human skin. However, weak acoustic signals in PAS systems require optimized resonator designs for enhanced detection sensitivity. Designing such resonators physically is complex, requiring precise identification of critical parameters before practical implementation. This study focuses on optimizing a T-shaped photoacoustic resonator using finite element modeling in a COMSOL Multiphysics environment. By systematically varying the geometric design parameters of the T-cell resonator, a maximum increase in pressure amplitude of 12.76 times with a quality factor (Q-factor) of 47.5 was achieved to the previously designed reference acoustic resonator. This study takes a significant step forward by identifying key geometric parameters that influence resonator performance, paving the way for more sensitive and reliable noninvasive glucose monitoring systems.

Classification of device structures and applications of self-powered ultraviolet photodetectors.

The contribution of QCL in detecting blood glucose levels noninvasively by midinfrared and photoacoustic spectroscopy.