In Silico Investigation of SNR and Dermis Sensitivity for Optimum Dual-Channel Near-Infrared Glucose Sensor Designs for Different Skin Colors

Abstract

Diabetes is a serious health condition that requires patients to regularly monitor their blood glucose level, making the development of practical, compact, and non-invasive techniques essential. Optical glucose sensors—and, specifically, NIR sensors—have the advantages of being non-invasive, compact, inexpensive, and user-friendly devices. However, these sensors have low accuracy and are yet to be adopted by healthcare providers. In our previous work, we introduced a non-invasive dual-channel technique for NIR sensors, in which a long channel is utilized to measure the glucose level in the inner skin (dermis) layer, while a short channel is used to measure the noise signal of the superficial skin (epidermis) layer. In this work, we investigated the use of dual-NIR channels for patients with different skin colors (i.e., having different melanin concentrations). We also adopted a Monte Carlo simulation model that takes into consideration the differences between different skin layers, in terms of blood content, water content, melanin concentration in the epidermis layer, and skin optical proprieties. On the basis of the signal-to-noise ratio, as well as the sensitivities of both the epidermis and dermis layers, we suggest the selection of wavelengths and source-to-detector separation for optimal NIR channels under different skin melanin concentrations. This work facilitates the improved design of a compact and non-invasive NIR glucose sensor that can be utilized by patients with different skin colors.

Estimation of the Differential Pathlength Factor for Human Skin Using Monte Carlo Simulations

“…This figure illustrates that the source-to-detector range used in this study was sufficient to penetrate into the deep blood net dermis, which is a necessary layer for blood content measurement, including glucose. The source-to-detector range verses the depth of penetration of different skin layers is discussed in [24]. In addition, the two figures show the details of the optical pathlength of photon propagation in each layer of the skin.…”

“…The simulation was run for a range of distances (d; 0.5 mm to 8 mm, with a step size of 0.5 mm). The maximum distance considered here (d = 8 mm) was enough for the NIR to reach the deep blood net dermis of the skin with an acceptable signal-to-noise ratio (SNR) [24]. In particular, the SNR for the shorter wavelengths (550 nm and 650 nm) dropped very quickly as the source-to-detector distance increased.…”

“…In particular, the SNR for the shorter wavelengths (550 nm and 650 nm) dropped very quickly as the source-to-detector distance increased. Longer wavelengths (750 nm to 1050 nm) provided better SNRs at longer sourceto-detector distances, which meant that deeper tissue layers could be measured [24]. For each distance (d), simulations were also conducted for a range of light-source wavelengths.…”

“…The optical properties and the thickness of each layer used to run the simulations are summarized in Table 1. The absorption coefficients of each layer were calculated as explained in [24]. In brief, the blood and water volume fractions V blood and V H 2 O and the absorption coefficients for water, deoxyhemoglobin, and oxyhemoglobin for each layer were used to estimate the layer absorption coefficients.…”

“…Blood glucose NIR sensors are also based on the reflection mode, since the glucose contents are found in the Diagnostics 2023, 13, 309 2 of 10 skin dermis layer. In a previous work [24], we proposed dual NIR sensors, wherein two channels were used to measure both the signal of interest and the interfering noise arising from the superficial skin layer. Furthermore, the factors affecting the NIR penetration depth and the suitable source-to-detector separation (SDS) for a range of NIR wavelengths were investigated.…”