Optimization of Dual-Channel Near-Infrared Non-Invasive Glucose Level Measurement Sensors Based On Monte-Carlo Simulations

Abstract

Non-invasive glucose monitoring sensors are promising techniques in diabetes management. In particular, optical-based near-infrared glucose sensors are label-free, compact, user-friendly, and inexpensive. They require no daily calibration and can provide continuous glucose level monitoring. However, these sensors are still in the development stage since their accuracy is still not widely accepted by medical professionals. Here, we introduce an optimized dual-channel approach for this kind of sensor where four optodes for short and long channels are employed. The long channel signal that contains useful information about the glucose level can be utilized to calculate the glucose contents in the dermis layer of the skin while the short channel is utilized to measure the interference “noise” signal originating from the epidermis layer. So, the latter can then be removed from the long channel signal. This work investigates the optimal source-detector separation (SDS) of both short and long channels for a wide range of wavelengths from 1200 nm to 1900 nm. Based on the sensitivity distributions of both dermis and epidermis layers and the signal-to-noise ratio, it is suggested that a 2 mm SDS distance with a wavelength of 1450 nm is a potential choice for the short channel. Moreover, 6 mm SDS at the wavelength of 1750 nm is the optimal choice for the long channel. This work may pave the way for further optimization of non-invasive near-infrared glucose sensors that will be widely adopted in the healthcare sector.

Keywords

• Near-infrared
• glucose sensors
• Monte-Carlo simulations