Non-invasive blood glucose measurement of 95% certainty by pressure regulated Mid-IR

Optimization of mid-infrared noninvasive blood-glucose prediction model by support vector regression coupled with different spectral features

Mid-infrared spectral analysis of glucose in subcutaneous interstitial fluid has been widely employed as a noninvasive alternative to the standard blood-glucose detection requiring blood-sampling via skin-puncturing, but improving the confidence level of such a replacement remains highly desirable. Here, we show that with an innovative metric of attributes in measurements and data-management, a high accuracy in correlating the test results of our improved spectral analysis to those of the standard detection is accomplished. First, our comparative laser speckle contrast imaging of subcutaneous interstitial fluid in fingertips, thenar and hypothenar reveal that spectral measurements from hypothenar, with an attenuated total reflection Fourier transform infrared spectrometer, give much stronger signals than the stereotype measurements from fingertips. Second, we demonstrate that discriminative selection of the spectral locations and ranges, to minimize spectral interference and maximize signal-to-noise, are critically important. The optimal band is pinned at that between 1000 ± 3 cm-1 and1040 ± 3 cm-1. Third, we propose an individual exclusive prediction model by adopting the support vector regression analysis of the spectral data from four subjects. The average predicted coefficient of determination, root mean square error and mean absolute error of four subjects are 0.97, 0.21 mmol/L, 0.17 mmol/L, respectively, and the average probability of being in Zone A of the Clark error grid is 100.00 %. Additionally, we demonstrate with the Bland and Altman plot that our proposed model has the highest consistency with portable blood glucose meter detection method.

Noninvasive Glucose Sensing In Vivo

Blood glucose monitoring is an essential aspect of disease management for individuals with diabetes. Unfortunately, traditional methods require collecting a blood sample and thus are invasive and inconvenient. Recent developments in minimally invasive continuous glucose monitors have provided a more convenient alternative for people with diabetes to track their glucose levels 24/7. Despite this progress, many challenges remain to establish a noninvasive monitoring technique that works accurately and reliably in the wild. This review encompasses the current advancements in noninvasive glucose sensing technology in vivo, delves into the common challenges faced by these systems, and offers an insightful outlook on existing and future solutions.

Microfluidic microwave biosensor based on biomimetic materials for the quantitative detection of glucose

This paper presents a microwave microfluidic biosensor for monitoring blood glucose levels. The glucose sensor is a triple ring microstrip patch antenna integrated with a biomimetic microfluidic device capable of measuring a fixed volume of glucose solution. The sensor was utilized to detect 50–500 mg/dL glucose solutions. The interaction of the glucose solution with the electromagnetic field on the patch's surface influences both the resonance frequency and the magnitude of reflection coefficient. The results indicate that the microfluidic device can reduce experimental error and enhance the correlation between glucose concentration, resonant frequency, and reflection coefficient. Finally, the microfluidic sensor had a sensitivity of 0.25 MHz/(mg/dL), a detection limit as low as 7.7 mg/dL, and correlation coefficients of resonance frequency and reflection coefficient with a glucose concentration of 0.996 and 0.984, respectively. The experiment on the sensor's stability verifies the sensor's excellent stability and rapid response (~ 150 ms). Consequently, the device can be used to differentiate the concentration of glucose solutions, as well as to detect blood glucose levels at an early stage.

Glucose Level Sensing Using Single Asymmetric Split Ring Resonator

In this article, a biosensor composed of a single metamaterial asymmetric resonator is specifically designed for sensing the glucose level of 1 µL of solution. The resonator has two gaps, and one of them ends with a semicircle shape on which the glucose solution is placed. This design helps in confining the drops of glucose solutions in a specific area where the field is maximally confined in order to enhance the electromagnetic wave-matter interaction. Six samples of glucose solutions with concentrations that cover hypoglycemia, normal and hyperglycemia conditions that vary from around 41 to 312 mg/dL were prepared and examined by this biosensor. The resonance frequency redshift was used as a measure of the changes in the glucose level of the solutions. Without glucose solution, an excellent agreement between the measured and simulated transmission amplitude was observed. The increase in glucose concentrations exhibited clear and noticeable redshifts in the resonance frequency. This biosensor revealed a 0.9997 coefficient of determination, which implies an excellent prediction fitting model. More importantly, a sensitivity of 438 kHz/(mg/dL) was observed over the range of concentrations of the aqueous solution.

Non-Invasive Glucose Monitoring Using Optical Sensor and Machine Learning Techniques for Diabetes Applications

Diabetes is a major public health challenge affecting more than 451 million people. Physiological and experimental factors influence the accuracy of non-invasive glucose monitoring, and these need to be overcome before replacing the finger prick method. Also, the suitable employment of machine learning techniques can significantly improve the accuracy of glucose predictions. One aim of this study is to use light sources with multiple wavelengths to enhance the sensitivity and selectivity of glucose detection in an aqueous solution. Multiple wavelength measurements have the potential to compensate for errors associated with inter- and intra-individual differences in blood and tissue components. In this study, the transmission measurements of a custom built optical sensor are examined using 18 different wavelengths between 410 and 940 nm. Results show a high correlation value (0.98) between glucose concentration and transmission intensity for four wavelengths (485, 645, 860 and 940 nm). Five machine learning methods are investigated for glucose predictions. When regression methods are used, 9% of glucose predictions fall outside the correct range (normal, hypoglycemic or hyperglycemic). The prediction accuracy is improved by applying classification methods on sets of data arranged into 21 classes. Data within each class corresponds to a discrete 10 mg/dL glucose range. Classification based models outperform regression, and among them, the support vector machine is the most successful with F1-score of 99%. Additionally, Clarke error grid shows that 99.75% of glucose readings fall within the clinically acceptable zones. This is an important step towards critical diagnosis during an emergency patient situation.

Achieving Nonenzymatic Blood Glucose Sensing by Uprooting Saturation

The saturation of nonenzymatic blood glucose sensors at lower than normal blood glucose levels has blocked their practical applications. The mechanistic understanding of the saturation, however, has long been under debate. Employing cyclic voltammetry, amperometry, and FTIR with various electrolytes of varying concentrations, we were able to uproot the saturation cause. It was found to be related to the hydroxide ion concentration, which must be 11 times greater than that of the glucose concentration, contrary to the prior understanding. Together with the satisfactory sensitivity at high pH, nonenzymatic blood glucose sensing has finally been achieved, eliminating the usual problem of electrochemical current saturation as well as the need for enzyme found in the present technology.