Discussion on the validity of NIR spectral data in non-invasive blood glucose sensing

Enhancing the accuracy of blood-glucose tests by upgrading FTIR with multiple-reflections, quantum cascade laser, two-dimensional correlation spectroscopy and machine learning

The accuracy of screening diabetes from non-diabetes is drastically enhanced by strategically upgrading the bench-marking infrared spectroscopy technique for non-invasive tests of blood-glucose, both with state-of-the-art instrumentation-retrofits and with intelligent spectral-datamining tools. First, the signal-to-noise performance of FTIR in measuring the spectral features of a glucose solution containing bovine serum albumin is improved by 2-3 times with the common single-pass attenuated total-reflection setup replaced by a multi-passes-reflections setup. Second, replacing the ordinary infrared lamp with a quantum cascade laser further improves the signal-to-noise by 3 times. The performance of the upgraded spectrometer in screening hyperglycemia is gauged by the accuracy of such screens derived from 100 repetitive spectral-measurements per glucose concentration, for 24 glucose concentrations spanning the range of 70-300 mg/dL, a range which covers the blood-glucose contents of all non-diabetic and diabetic human-subjects. Third, intelligent datamining methods are exploited to implement decision trees for screening hyperglycemia. Their decisions are mapped into a confusion matrix and the matrix-elements are used to calculate the accuracy merits of each method. Evidently, the accuracy of the multi-passes-FTIR with the standard principal-components datamining method is 80 %. The adoptions of the quantum cascade laser and two-dimensional correlation spectroscopy datamining technique raises this to 96.3 %. Finally, a novel machine learning method, which comprises three different decision-tree tools to generate trial screening decisions and a "majority-voting" datamining tool to reach a final screening decision, yields the best accuracy of 98.8 % ever reported in the literature.

Optical blood glucose non-invasive detection and its research progress

Blood glucose concentration is an important index for the diagnosis of diabetes, its self-monitoring technology is the method for scientific diabetes management. Currently, the typical household blood glucose meters have achieved great success in diabetes management, but they are discrete detection methods, and involve invasive blood sampling procedures. Optical detection technologies, which use the physical properties of light to detect the glucose concentration in body fluids non-invasively, have shown great potential in non-invasive blood glucose detection. This article summarized and analyzed the basic principles, research status, existing problems, and application prospects of different optical glucose detection technologies. In addition, this article also discusses the problems of optical detection technology in wearable sensors and perspectives on the future of non-invasive blood glucose detection technology to improve blood glucose monitoring in diabetic patients.

Application of NIR spectroscopy and multivariate analysis for Non-destructive evaluation of apple moisture content during ultrasonic drying

Direct-contact ultrasonic drying is a novel approach to dehydrate fruits and vegetables to reduce microbial growth and post-harvest loss while preserving nutrients and the quality of the final product. Moisture content is a critical component for food behavior during drying, and its accurate evaluation in real-time is essential for food quality control. This study conveys the potential implementation of portable near-infrared spectroscopy (NIRS) combined with multivariate analysis for real-time assessment of moisture content in apple slices during direct-contact ultrasonic drying. Partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed, and their performances for different pre-treatments methods and data partitioning algorithms were evaluated with both internal cross-validation and an external dataset. Three wavelengths were selected by SPA (1359, 1517, and 1594 nm) which were then used to introduce a closed-form equation for moisture content prediction with R²_p = 0.99 and RMSEP = 3.32%. The results revealed that portable NIRS combined with multivariate analysis is quite promising for monitoring and evaluating the moisture content during ultrasonic drying.

Single-wavelength water muted photoacoustic system for detecting physiological concentrations of endogenous molecules

Based on the breakthrough technology of water muting on photoacoustic spectroscopy, a single wavelength photoacoustic system in the short-wavelength-infrared (SWIR) region was developed to sense the endogenous molecules (e.g. glucose, lactate, triglyceride, and serum albumin found in blood and interstitial fluid) in aqueous media. The system implemented a robust photoacoustic resonant cell that can significantly enhance the signal-to-noise ratio of the acoustic waves. The sensitivity of the system was explored, and the experimental results exhibit a precision detection of physiological concentrations of biomolecules by combining the techniques of water muting and photoacoustic resonant amplification in a portable and low-cost single wavelength laser system.

Water as a probe for serum-based diagnosis by temperature- dependent near-infrared spectroscopy

Diagnosis based on the compositional variation of biological liquids such as serum has drawn much attention. For exploring the potential diagnostic information from serum samples, temperature-dependent near-infrared (NIR) spectroscopy was developed to obtain the spectral change of water reflecting the interactions in serum solution, and chemometric methods were employed to mine the information from the temperature-dependent NIR spectra. The spectra of 68 healthy controls, 42 patients with the type II diabetes and 16 patients with coronary heart disease were measured and analyzed by chemometric calculations. Continuous wavelet transform (CWT) was used to enhance the resolution of the spectra. From the processed spectra, the information of non-hydrogen-bonded (NHB), weakly hydrogen-bonded (WHB) and strongly hydrogen-bonded (SHB) water species was observed. For explaining the variation of the spectra with temperature, two-dimensional (2D) correlation analysis was adopted. A clear difference in SHB/NHB ratio in the synchronous maps was found between the spectra of the patients and the controls. 86.8% of the controls and 98.3% of the patients can be correctly identified. Furthermore, combining the maps of the synchronous and asynchronous analysis, the correlation between SHB and WHB water species was discovered to have an ability to discriminate the patients of diabetes and heart disease with an accuracy of 83.7% and 75.0%, respectively. Therefore, water may be a probe for providing diagnostic information by temperature-dependent NIR spectroscopy.

Non-invasive monitoring of blood glucose using optical methods for skin spectroscopy-opportunities and recent advances

Diabetes mellitus is a widespread disease with greatly rising patient numbers expected in the future, not only for industrialized countries but also for regions in the developing world. There is a need for efficient therapy, which can be via self-monitoring of blood glucose levels to provide tight glycemic control for reducing the risks of severe health complications. Advancements in diabetes technology can nowadays offer different sensor approaches, even for continuous blood glucose monitoring. Non-invasive blood glucose assays have been promised for many years and various vibrational spectroscopy-based methods of the skin are candidates for achieving this goal. Due to the small spectral signatures of the glucose hidden among a largely variable background, the largest signal-to-noise ratios and multivariate calibration are essential to provide the method applicability for self-monitoring of blood glucose. Besides multiparameter approaches, recently presented devices based on photoplethysmography with wavelengths in the visible and near-infrared range are evaluated for their potential of providing reliable blood glucose concentration predictions. Graphical abstract ᅟ.

Extended Near-Infrared Optoacoustic Spectrometry for Sensing Physiological Concentrations of Glucose

Glucose sensing is pursued extensively in biomedical research and clinical practice for assessment of the carbohydrate and fat metabolism as well as in the context of an array of disorders, including diabetes, morbid obesity, and cancer. Currently used methods for real-time glucose measurements are invasive and require access to body fluids, with novel tools and methods for non-invasive sensing of the glucose levels highly desired. In this study, we introduce a near-infrared (NIR) optoacoustic spectrometer for sensing physiological concentrations of glucose within aqueous media and describe the glucose spectra within 850-1,900 nm and various concentration ranges. We apply the ratiometric and dictionary learning methods with a training set of data and validate their utility for glucose concentration measurements with optoacoustics in the probe dataset. We demonstrate the superior signal-to-noise ratio (factor of ~3.9) achieved with dictionary learning over the ratiometric approach across the wide glucose concentration range. Our data show a linear relationship between the optoacoustic signal intensity and physiological glucose concentration, in line with the results of optical spectroscopy. Thus, the feasibility of detecting physiological glucose concentrations using NIR optoacoustic spectroscopy is demonstrated, enabling the sensing glucose with ±10 mg/dl precision.

Floating reference position-based correction method for near-infrared spectroscopy in long-term glucose concentration monitoring

We present a floating reference position (FRP)-based drift correction method for near-infrared (NIR) spectroscopy-based long-term blood glucose concentration (BGC) monitoring. Previously, we reported that it is difficult to quantify the systematic drift caused by the fluctuation of incident light intensity at different source–detector (SD) separations based on the absolute FRP change. We use the relative FRP change as a baseline reference to quantitatively characterize the signal drift at different SD separations. For the wavelengths that were used, a uniform equation was developed to describe the relationship between the drift and the relative FRP change. With the help of this equation, the correction can easily be performed by subtracting the systematic drift estimated by the equation. A theoretical analysis and an experimental phantom study demonstrated that our method could be used for systematic drift correction in NIR spectroscopy for long-term BGC monitoring. Moreover, the analysis method can also be referenced to reduce drifts from multiple sources.

Investigations on Multisensor-Based Noninvasive Blood Glucose Measurement System

Noninvasive blood glucose (NIBG) measurement technique has been explored for the last three decades to facilitate diabetes management. Photoplethysmogram (PPG) signal may be used to measure the variations in blood glucose concentration. However, the literature reveals that physiological perturbations such as temperature, skin moisture, and sweat lead to less accurate NIBG measurements. The task of minimizing the effect of these perturbations for accurate measurements is an important research area. Therefore, in the present work, galvanic skin response (GSR) and temperature measurements along with PPG were used to measure blood glucose noninvasively. The data extracted from the sensors were used to estimate blood glucose concentration with the help of two machine learning (ML) techniques, i.e., multiple linear regression (MLR) and artificial neural network (ANN). The accuracy of proposed multisensor system was evaluated by pairing and comparing noninvasive measurements with invasively measured readings. The study was performed on 50 nondiabetic subjects with body mass index (BMI) 27.3 ± 3 kg/m2. The results revealed that multisensor NIBG measurement system significantly improves mean absolute prediction error and correlation coefficient in comparison to the techniques reported in the literature.

Identification of informative bands in the short-wavelength NIR region for non-invasive blood glucose measurement

The "glucose-linked wavelength" in the short-wavelength near-infrared (NIR) region, in which the light intensity reflected from the hand palm exhibits a good correlation to the blood glucose value, was investigated. We performed 391 2-h carbohydrate tolerance tests (CTTs) using 34 participants and a glucose-linked wavelength was successfully observed in almost every CTT; however, this wavelength varied between CTTs even for the same person. The large resulting data set revealed the distribution of the informative wavelength. The blood glucose values were efficiently estimated by a simple linear regression with clinically acceptable accuracies. The result suggested the potential for constructing a personalized low-invasive blood glucose sensor using short-wavelength NIR spectroscopy.

In vitro glucose measurement using tunable mid-infrared laser spectroscopy combined with fiber-optic sensor

Because mid-infrared (mid-IR) spectroscopy is not a promising method to noninvasively measure glucose in vivo, a method for minimally invasive high-precision glucose determination in vivo by mid-IR laser spectroscopy combined with a tunable laser source and small fiber-optic attenuated total reflection (ATR) sensor is introduced. The potential of this method was evaluated in vitro. This research presents a mid-infrared tunable laser with a broad emission spectrum band of 9.19 to 9.77[Formula: see text](1024~1088 cm(-1)) and proposes a method to control and stabilize the laser emission wavelength and power. Moreover, several fiber-optic ATR sensors were fabricated and investigated to determine glucose in combination with the tunable laser source, and the effective sensing optical length of these sensors was determined for the first time. In addition, the sensitivity of this system was four times that of a Fourier transform infrared (FT-IR) spectrometer. The noise-equivalent concentration (NEC) of this laser measurement system was as low as 3.8 mg/dL, which is among the most precise glucose measurements using mid-infrared spectroscopy. Furthermore, a partial least-squares regression and Clarke error grid were used to quantify the predictability and evaluate the prediction accuracy of glucose concentration in the range of 5 to 500 mg/dL (physiologically relevant range: 30~400 mg/dL). The experimental results were clinically acceptable. The high sensitivity, tunable laser source, low NEC and small fiber-optic ATR sensor demonstrate an encouraging step in the work towards precisely monitoring glucose levels in vivo.