# Advancements in Glucose Monitoring: From Traditional Methods to Wearable Sensors
## 4 Non-Invasive Methods
### Infrared Absorption Spectroscopy
To overcome this problem and enhance the measurement accuracy, [PERSON] and his team employed shallow dense neural networks (SDNNs) for developing a multiple photonic band near-infrared (mbNIR) sensor, integrated with personalized medical features (PMFs) (Figure 11c) [105]. Datasets from 401 blood samples were randomized and trained using ten-fold validation. In addition, a cohort of 234 individuals, who were not a part of the training set, was used to assess the model's performance. This model achieved an impressive 97.8% accuracy and 94.8% sensitivity with 96.0% precision and 98.7% specificity for diabetes mellitus classification, using a fasting blood glucose threshold of 126 mg/dL. For non-invasive, real-time glucose monitoring, the shallow dense neural network (SDNN) model showed an accuracy of 98.5% along with 98.0% precision, 96.2% sensitivity, and 99.3% specificity within a detection range of 60-400 mg/dL [105]. To improve the accuracy further, [PERSON] et al. used machine learning (ML) algorithms with multiple wavelength measurements for glucose monitoring. Their Clarke error grid analysis indicates that 99.75% of glucose readings fall within the clinically acceptable zones, marking a significant advancement towards reliable diagnosis in emergency patient situations [106]. However, currently, there are no glucose-sensing devices on the market that utilize NIR technology. NIR-based glucose sensors face challenges due to the variability of skin conditions, including factors like sunburn, sweating, or tattoos. As a result, developing personalized calibration models is essential to accurately detect different physiological states across patients. Figure 11: (**a**) The schematic illustration demonstrating the NIR process. (**i**) In the transmittance mode, the sample is exposed to polychromatic light from a light source (tungsten halogen or LED). The transmitted light is then separated into its component wavelengths using a diffraction grating, and the optical information is analyzed by a detector and computer. (**ii**) In the case of reflectance mode, the diffraction grating is placed on the same side as the light source, allowing the light to be reflected at a specific angle [Redrawn from [97]]. (**b**) The Clarke error grid demonstrating the correlation between the reference and predicted blood glucose levels in the second-stage experiment, evaluated using the Clarke error grid, showed a correlation coefficient (r) of 0.97 and a standard error of prediction (SEP) of 27.2 mg/dL. Out of the 304 data points, 80.3% fell within Zone A, 19.7% in Zone B, and none were in Zones C, D, or E, indicating high prediction accuracy for ICU patients. Reprinted with permission from [103]. (**c**) The schematic illustration of the mbNIR/PMF + SDNN system demonstrating that the light scattered from the finger was filtered prior to entering the readout channel of the controller, with specific bands selected, i.e., band #1 ranging from 800 to 900 nm centered at 850 nm, band #2 ranging from 900 to 1000 nm centered at 950 nm, and band #3 ranging from 1100 to 1200 nm centered at 1150 nm. Reprinted with permission from [105]. Apart from NIR-based sensors, OCT, and reverse iontophoresis, researchers have explored several advanced alternative methods, such as photoacoustic spectroscopy [107] and microwave resonators [108], which have shown potential for higher precision in glucose detection. Photoacoustic spectroscopy works by combining laser-emitted light with the acoustic response generated. The ultrasonic transmitter detects pressure wave variations, which occur due to absorption [107]. Earlier in vivo studies have shown that this technique provides greater sensitivity for glucose detection compared to traditional spectroscopic methods [107]. This enhanced sensitivity is attributed to the weak photoacoustic response of water, which helps in detecting compounds like glucose and hydrocarbons more effectively. Recently, [PERSON] et al.