Scattering-independent glucose absorption measurement using a spectrally resolved reflectance setup with specialized variable source-detector separations

Machine Learning Method and Hyperspectral Imaging for Precise Determination of Glucose and Silicon Levels

This article introduces an algorithm for detecting glucose and silicon levels in solution. The research focuses on addressing the critical need for accurate and efficient glucose monitoring, particularly in the context of diabetic management. Understanding and monitoring silicon levels in the body is crucial due to its significant role in various physiological processes. Silicon, while often overshadowed by other minerals, plays a vital role in bone health, collagen formation, and connective tissue integrity. Moreover, recent research suggests its potential involvement in neurological health and the prevention of certain degenerative diseases. Investigating silicon levels becomes essential for a comprehensive understanding of its impact on overall health and well-being and paves the way for targeted interventions and personalized healthcare strategies. The approach presented in this paper is based on the integration of hyperspectral data and artificial intelligence techniques. The algorithm investigates the effectiveness of two distinct models utilizing SVMR and a perceptron independently. SVMR is employed to establish a robust regression model that maps input features to continuous glucose and silicon values. The study outlines the methodology, including feature selection, model training, and evaluation metrics. Experimental results demonstrate the algorithm's effectiveness at accurately predicting glucose and silicon concentrations and showcases its potential for real-world application in continuous glucose and silicon monitoring systems.

The Use of Supercontinuum Laser Sources in Biomedical Diffuse Optics: Unlocking the Power of Multispectral Imaging

Optical techniques based on diffuse optics have been around for decades now and are making their way into the day-to-day medical applications. Even though the physics foundations of these techniques have been known for many years, practical implementation of these technique were hindered by technological limitations, mainly from the light sources and/or detection electronics. In the past 20 years, the developments of supercontinuum laser (SCL) enabled to unlock some of these limitations, enabling the development of system and methodologies relevant for medical use, notably in terms of spectral monitoring. In this review, we focus on the use of SCL in biomedical diffuse optics, from instrumentation and methods developments to their use for medical applications. A total of 95 publications were identified, from 1993 to 2021. We discuss the advantages of the SCL to cover a large spectral bandwidth with a high spectral power and fast switching against the disadvantages of cost, bulkiness, and long warm up times. Finally, we summarize the utility of using such light sources in the development and application of diffuse optics in biomedical sciences and clinical applications.

Specialized source-detector separations in near-infrared reflectance spectroscopy platform enable effective separation of diffusion and absorption for glucose sensing

We present an approach for accurate glucose sensing in turbid media using a spectrally resolved reflectance setup. Our proposed reflectance setup uses specialized source-detector separations (SDSs) to enable an effective separation of diffusion and absorption signals. Additionally, we adjust the selected SDSs to their optimal values to acquire maximum sensitivity to glucose in the two signals. The separation can help to enhance the sensitivity to glucose both for the diffusion and absorption signals, as they always suppress each other by causing opposite effects on the reflected diffuse light intensity. Monte Carlo simulations and experiments for glucose sensing are used to test the method. The acquired optimal SDSs could provide a reference for noninvasive blood glucose sensing.