Noninvasive Monitoring of Glucose Using Near-Infrared Reflection Spectroscopy of Skin—Constraints and Effective Novel Strategy in Multivariate Calibration

Abstract

For many years, successful noninvasive blood glucose monitoring assays have been announced, among which near-infrared (NIR) spectroscopy of skin is a promising analytical method. Owing to the tiny absorption bands of the glucose buried among a dominating variable spectral background, multivariate calibration is required to achieve applicability for blood glucose self-monitoring. The most useful spectral range with important analyte fingerprint signatures is the NIR spectral interval containing combination and overtone vibration band regions. A strategy called science-based calibration (SBC) has been developed that relies on a priori information of the glucose signal (“response spectrum”) and the spectral noise, i.e., estimates of the variance of a sample population with negligible glucose dynamics. For the SBC method using transcutaneous reflection skin spectra, the response spectrum requires scaling due to the wavelength-dependent photon penetration depth, as obtained by Monte Carlo simulations of photon migration based on estimates of optical tissue constants. Results for tissue glucose concentrations are presented using lip NIR-spectra of a type-1 diabetic subject recorded under modified oral glucose tolerance test (OGTT) conditions. The results from the SBC method are extremely promising, as statistical calibrations show limitations under the conditions of ill-posed equation systems as experienced for tissue measurements. The temporal profile differences between the glucose concentration in blood and skin tissue were discussed in detail but needed to be further evaluated.

In Vivo Near-Infrared Noninvasive Glucose Measurement and Detection in Humans

“…There are rich research achievements about the optical properties of tissues and their changes with glucose. 6,7,[13][14][15] And mathematical methods such as multivariate analysis and artificial intelligence algorithms also provide powerful mathematical tools. 14,16 Meanwhile, new detection equipment such as quantum cascade laser and high signal-to-noise ratio (SNR) detectors also offer strong hardware support.…”

“…6,7,[13][14][15] And mathematical methods such as multivariate analysis and artificial intelligence algorithms also provide powerful mathematical tools. 14,16 Meanwhile, new detection equipment such as quantum cascade laser and high signal-to-noise ratio (SNR) detectors also offer strong hardware support. 17,18 With the development of new technologies, good in vivo test results under strict experimental conditions have been obtained using mid-infrared absorption spectroscopy, 19 photoacoustic spectroscopy, [9][10][11][12] near-infrared (NIR) spectroscopy, 20 Raman spectroscopy, 8 etc., which have verified the feasibility of in vivo situation with these methods.…”

Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks

“…Importantly, the underdiagnosis of T2D in low-and middleincome countries, where the resources to perform a T2D screening are limited, could be as high as 46% [5]. In order to cope with this public health problem, non-invasive techniques to determine the quantity of glucose and glycated hemoglobin (HbA1c) have been proposed recently, such as NIR spectroscopy, Raman spectroscopy, surface-enhanced Raman spectroscopy, and mid-infrared spectroscopy, among others [6][7][8][9][10].…”

Products for Monitoring Glucose Levels in the Human Body With Noninvasive Optical, Noninvasive Fluid Sampling, or Minimally Invasive Technologies

“…Additional tissue components, such as vascular and intracellular compartments, can also contribute to the spectroscopic response. 8 Most often, measurement sites correspond to accessible body compartments, such as fingers, extremities, the abdominal wall, or earlobes. Such devices are painless and free of medical waste.…”