# Advancements in Glucose Monitoring: From Traditional Methods to Wearable Sensors
## 4 Non-Invasive Methods
### Infrared Absorption Spectroscopy  
Infrared absorption spectroscopy is based on the absorption and scattering caused by molecular vibrations and rotations. The typical wavelength for infrared absorption spectroscopy ranges from 780 to 2500 nm (14000-4000 cm\({}^{-1}\)). In the case of glucose sensing, the first overtone (6500-5500 cm\({}^{-1}\)) and combination (5000-4000 cm\({}^{-1}\)) regions are commonly used for measuring the bending and stretching of C-H and O-H bonds in glucose. Transmittance and reflectance are two primary measurement modes that are used in NIR spectroscopy. Figure 11a illustrates the schematic diagrams for both transmittance and reflectance modes. In the transmittance mode (Figure 11a(i)), 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. In contrast, the reflectance mode (Figure 11a(ii)) operates similarly, but the diffraction grating is placed on the same side as the light source, allowing the light to be reflected at a specific angle [97]. Transmission spectroscopy is commonly used for aqueous solutions, while reflectance spectroscopy is preferred for non-invasive measurements [97]. NIR spectroscopy has gained significant popularity in non-invasive glucose monitoring owing to many successful in vivo and clinical studies [101; 102; 103]. For instance, [PERSON] et al. reported of monitoring glucose levels in rats with a fiber probe. The fiber probe has a detection range of 90 to 630 mg/dL (5 to 35 mM) and a standard error of prediction (SEP) of 35.6 mg/dL (1.98 mM) [101]. Apart from animal trials, several research groups have also focused on clinical studies. For instance, [PERSON] and his team developed a non-invasive NIR-based sensor for monitoring glucose levels in diabetic patients and compared the data with healthy individuals. The study involved five healthy participants and seven diabetic patients. The standard error of prediction (SEP) was calculated as 27.2 mg/dL, measured within the glucose concentration range of 50-500 mg/dL [103]. Figure 11b shows the comparison between the predicted and reference glucose values, along with the Clarke error grid (CEG) analysis [103]. Despite the many advancements and clinical trials, this technology suffers from biological noises owing to the various skin conditions. In order to improve this, [PERSON] et al. approached a two-step process to enhance the NIR spectroscopy measurement system. A customized multi-ring Indium Gallium Arsenide (InGaAs) detector with a high signal-to-noise ratio is used to reduce the influence of human variations, and a fixing and aiming method is used to reproduce a consistent measurement condition. Seventeen glucose tolerance tests (GTT) were conducted on both healthy and diabetic volunteers to validate this strategy. The results from the human experiments demonstrated that blood glucose changes were successfully detected at 1550 nm. The average correlation coefficient between the light signal and glucose reference across the 17 GTT cases was found to be 0.84 [102]. In order to improve accuracy, several researchers attempted to use multiple-band NIR (mbNIR) and use this mbNIR to train neural networks (NN). For example, [PERSON] et al. (2021) used an artificial neural network with a combination of NIR and 37-39 GHz frequency to excite and track the glucose response for glucose measurement [104]. The experiment was conducted in an aqueous solution in vitro. A major limitation in previously reported models was the absence of personalized medical features (PMFs) in the training of neural networks. It is important to note that interference from factors such as bones, tissue thickness, fat, and other unknown variables leads to reduced accuracy due to disturbances in the NIR response.