Noninvasive glucose monitoring using mid-infrared absorption spectroscopy based on a few wavenumbers

Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening

Infrared spectroscopy is a powerful technique for probing the molecular profiles of complex biofluids, offering a promising avenue for high-throughput in vitro diagnostics. While several studies showcased its potential in detecting health conditions, a large-scale analysis of a naturally heterogeneous potential patient population has not been attempted. Using a population-based cohort, here we analyze 5,184 blood plasma samples from 3,169 individuals using Fourier transform infrared (FTIR) spectroscopy. Applying a multi-task classification to distinguish between dyslipidemia, hypertension, prediabetes, type 2 diabetes, and healthy states, we find that the approach can accurately single out healthy individuals and characterize chronic multimorbid states. We further identify the capacity to forecast the development of metabolic syndrome years in advance of onset. Dataset-independent testing confirms the robustness of infrared signatures against variations in sample handling, storage time, and measurement regimes. This study provides the framework that establishes infrared molecular fingerprinting as an efficient modality for populational health diagnostics.

In Vitro Glucose Measurement from NIR and MIR Spectroscopy: Comprehensive Benchmark of Machine Learning and Filtering Chemometrics

The quantitative analysis of glucose using spectroscopy is a topic of great significance and interest in science and industry. One conundrum in this area is deploying appropriate preprocessing and regression tools. To contribute to addressing this challenge, in this study, we conducted a comprehensive and novel comparative analysis of various machine learning and preprocessing filtering techniques applied to near-infrared, mid-infrared, and a combination of near-infrared and mid-infrared spectroscopy for glucose assay. Our objective was to evaluate the effectiveness of these techniques in accurately predicting glucose levels and to determine which approach was most optimal. Our investigation involved the acquisition of spectral data from samples of glucose solutions using the three aforementioned spectroscopy techniques. The data was subjected to several preprocessing filtering methods, including convolutional moving average, Savitzky-Golay, multiplicative scatter correction, and normalisation. We then applied representative machine learning algorithms from three categories: linear modelling, traditional nonlinear modelling, and artificial neural networks. The evaluation results revealed that linear models exhibited higher predictive accuracy than nonlinear models, whereas artificial neural network models demonstrated comparable performance. Additionally, the comparative analysis of various filtering methods demonstrated that the convolutional moving average and Savitzky-Golay filters yielded the most precise outcomes overall. In conclusion, our study provides valuable insights into the efficacy of different machine learning techniques for glucose measurement and highlights the importance of applying appropriate filtering methods in enhancing predictive accuracy. These findings have important implications for the development of new and improved glucose quantification technologies.

Non-invasive blood glucose estimation method based on the phase delay between oxy- and deoxyhemoglobin using visible and near-infrared spectroscopy

Significance

Many researchers have attempted to estimate blood glucose levels (BGLs) noninvasively using near-infrared (NIR) spectroscopy. However, the optical absorption change induced by blood glucose is weak in the NIR region and often masked by interference from other components such as water and hemoglobin.

Aim

Instead of using direct optical absorption by glucose, this study proposes an index calculated from oxy- and deoxyhemoglobin signals that shows a good correlation with BGLs while using conventional visible and NIR spectroscopy.

Approach

The metabolic index, which is based on tissue oxygen consumption, was derived through analytical methods and further verified and reproduced in a series of glucose challenge experiments. Blood glucose estimation units were prototyped by utilizing commercially available smart devices.

Results

Our experimental results showed that the phase delay between the oxy- and deoxyhemoglobin signals in near-infrared spectroscopy correlates with BGL measured by a conventional continuous glucose monitor. The proposed method was also confirmed to work well with visible spectroscopy systems based on smartphone cameras. The proposed method also demonstrated excellent repeatability in results from a total of 19 oral challenge tests.

Conclusions

This study demonstrated the feasibility of non-invasive glucose monitoring using existing photoplethysmography sensors for pulse oximeters and smartwatches. Evaluating the proposed method in diabetic or unhealthy individuals may serve to further increase its practicality.

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.

Sparse wavelengths data in mid-infrared spectroscopy: Modelling approaches and channel sampling

Infrared instruments with smaller and cost-effective components such as bandpass filters, single channel detectors, and laser-based light sources are being developed to provide cheaper and faster analysis of biological samples. Such instruments often provide measurements in form of sparse data, which include a collection of single-frequency channels or a collection of channels covering very narrow spectral ranges, called here multi-frequency channels. To keep costs low, the number of channels needs to be kept at a minimum. However, modelling and preprocessing of sparse data needs enough channels to perform the task. The aim of this study therefore was to understand the effect of channels sampling on data modelling results and find optimal modelling algorithm for different type of sparse data. The sparse data was simulated using Fourier Transform Infrared spectra of milk and fungi. Regression models were established to predict fatty acid composition by partial least squares regression (PLSR), multiple linear regression (MLR) and random forest (RF) methods. We observe that PLSR algorithm is very well suited for sparse data such as multi-frequency channels: excellent calibration models were obtained with only three channels comprising three wavenumbers each. The results were comparable to results obtained with full spectra. MLR and RF in turn provided similarly good results using data with single-frequency channels requiring nine channels in total.

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

BACKGROUND

Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially.

APPROACH

An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association for Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association.

CONTENT

Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (HbA1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of HbA1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed.

SUMMARY

The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

Prediction of type 1 diabetes with machine learning algorithms based on FTIR spectral data in peripheral blood mononuclear cells

The incidence of autoimmunity is increasing, to ensure timely and comprehensive treatment, there must be a diagnostic method or markers that would be available to the general public. Fourier-transform infrared spectroscopy (FTIR) is a relatively inexpensive and accurate method for determining metabolic fingerprint. The metabolism, molecular composition and function of blood cells vary according to individual physiological and pathological conditions. Thus, by obtaining autoimmune disease-specific metabolic fingerprint markers in peripheral blood mononuclear cells (PBMC) and subsequently using machine learning algorithms, it might be possible to create a tool that will allow the diagnosis of autoimmune diseases. In this preliminary study, it was found that the peak shift at 1545 cm-1 could be considered specific for autoimmune disease type 1 diabetes (T1D), while the shifts at 1070 and 1417 cm-1 could be more attributed to the autoimmune condition per se. The prediction of T1D, despite the small number of participants in the study, showed an inverse AUC = 0.33 ± 0.096, n = 15, indicating a stable trend in the prediction of T1D based on FTIR metabolic fingerprint data in the PBMC.

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.

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

BACKGROUND

Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially.

APPROACH

An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association of Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association.

CONTENT

Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (Hb A1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of Hb A1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed.

SUMMARY

The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

Progress and Perspectives of Mid-Infrared Photoacoustic Spectroscopy for Non-Invasive Glucose Detection

The prevalence of diabetes is rapidly increasing worldwide and can lead to a range of severe health complications that have the potential to be life-threatening. Patients need to monitor and control blood glucose levels as it has no cure. The development of non-invasive techniques for the measurement of blood glucose based on photoacoustic spectroscopy (PAS) has advanced tremendously in the last couple of years. Among them, PAS in the mid-infrared (MIR) region shows great promise as it shows the distinct fingerprint region for glucose. However, two problems are generally encountered when it is applied to monitor real samples for in vivo measurements in this MIR spectral range: (i) low penetration depth of MIR light into the human skin, and (ii) the effect of other interfering components in blood, which affects the selectivity of the detection system. This review paper systematically describes the basics of PAS in the MIR region, along with recent developments, technical challenges, and data analysis strategies, and proposes improvements for the detection sensitivity of glucose concentration in human bodies. It also highlights the recent trends of incorporating machine learning (ML) to enhance the detection sensitivity of the overall system. With further optimization of the experimental setup and incorporation of ML, this PAS in the MIR spectral region could be a viable solution for the non-invasive measurement of blood glucose in the near future.

Mid-infrared photoacoustic spectroscopy based on ultrasound detection for blood component analysis

For the non-invasive measurement of biological tissue, a piezoelectric photoacoustic spectroscopy (PZT-PAS) system that detects a single frequency of ultrasound induced by the irradiation of pulse-modulated mid-infrared laser light was developed. PA spectra of the optical phantom and biological samples were obtained, and the relationship between the PA signal intensity and optical absorbance in the fingerprint region (930-1,200 cm^-1) was analyzed to estimate the optical absorbance. The resonance vibration of the induced ultrasound was utilized to further increase the signal strength for biological tissue measurement. Consequently, PA spectrum reflecting the absorption of components in biological tissues was obtained.

Understanding bias in probabilistic analysis in model-based health economic evaluation

Guidelines of economic evaluations suggest that probabilistic analysis (using probability distributions as inputs) provides less biased estimates than deterministic analysis (using point estimates) owing to the non-linear relationship of model inputs and model outputs. However, other factors can also impact the magnitude of bias for model results. We evaluate bias in probabilistic analysis and deterministic analysis through three simulation studies. The simulation studies illustrate that in some cases, compared with deterministic analyses, probabilistic analyses may be associated with greater biases in model inputs (risk ratios and mean cost estimates using the smearing estimator), as well as model outputs (life-years in a Markov model). Point estimates often represent the most likely value of the parameter in the population, given the observed data. When model parameters have wide, asymmetric confidence intervals, model inputs with larger likelihoods (e.g., point estimates) may result in less bias in model outputs (e.g., costs and life-years) than inputs with lower likelihoods (e.g., probability distributions). Further, when the variance of a parameter is large, simulations from probabilistic analyses may yield extreme values that tend to bias the results of some non-linear models. Deterministic analysis can avoid extreme values that probabilistic analysis may encounter. We conclude that there is no definitive answer on which analytical approach (probabilistic or deterministic) is associated with a less-biased estimate in non-linear models. Health economists should consider the bias of probabilistic analysis and select the most suitable approach for their analyses.

Glucose emission spectra through mid-infrared passive spectroscopic imaging of the wrist for non-invasive glucose sensing

Non-invasive blood glucose sensing can be achieved using mid-infrared spectroscopy, although no practical device based on this method has yet been developed. Here, we propose mid-infrared passive spectroscopic imaging for glucose measurements from a distance. Spectroscopic imaging of thermal radiation from the human body enabled, for the first time in the world, the detection of glucose-induced luminescence from a distance. In addition, glucose emission spectra of the wrist acquired at regular intervals up to 60 min showed that there was a strong correlation between the glucose emission intensity and blood glucose level measured using an invasive sensor. Thus, the new technology proposed here is expected to be applied to real-time monitoring of diabetic patients to detect hypoglycemic attacks during sleep and to detect hyperglycemia in a population. Moreover, this technology could lead to innovations that would make it possible to remotely measure a variety of substances.

High-quality microresonators in the longwave infrared based on native germanium

The longwave infrared (LWIR) region of the spectrum spans 8 to 14 μm and enables high-performance sensing and imaging for detection, ranging, and monitoring. Chip-scale LWIR photonics has enormous potential for real-time environmental monitoring, explosive detection, and biomedicine. However, realizing technologies such as precision sensors and broadband frequency combs requires ultra low-loss and low-dispersion components, which have so far remained elusive in this regime. Here, we use native germanium to demonstrate the first high-quality microresonators in the LWIR. These microresonators are coupled to partially-suspended Ge waveguides on a separate glass chip, allowing for the first unambiguous measurements of isolated linewidths. At 8 μm, we measured losses of 0.5 dB/cm and intrinsic quality (Q) factors of 2.5 × 10⁵, nearly two orders of magnitude higher than prior LWIR resonators. Our work portends the development of novel sensing and nonlinear photonics in the LWIR regime.

Developing longwave infrared technology hide intrinsic challenges but at the same time is important to develop sensing and imaging for detection, ranging, and monitoring systems. Here the authors demonstrate the fabrication of high-quality microresonators in the LWIR with the simple use of native germanium.

Development of a Noninvasive Blood Glucose Monitoring System Prototype: Pilot Study

Background

Diabetes mellitus is a severe disease characterized by high blood glucose levels resulting from dysregulation of the hormone insulin. Diabetes is managed through physical activity and dietary modification and requires careful monitoring of blood glucose concentration. Blood glucose concentration is typically monitored throughout the day by analyzing a sample of blood drawn from a finger prick using a commercially available glucometer. However, this process is invasive and painful, and leads to a risk of infection. Therefore, there is an urgent need for noninvasive, inexpensive, novel platforms for continuous blood sugar monitoring.

Objective

Our study aimed to describe a pilot test to test the accuracy of a noninvasive glucose monitoring prototype that uses laser technology based on near-infrared spectroscopy.

Methods

Our system is based on Raspberry Pi, a portable camera (Raspberry Pi camera), and a visible light laser. The Raspberry Pi camera captures a set of images when a visible light laser passes through skin tissue. The glucose concentration is estimated by an artificial neural network model using the absorption and scattering of light in the skin tissue. This prototype was developed using TensorFlow, Keras, and Python code. A pilot study was run with 8 volunteers that used the prototype on their fingers and ears. Blood glucose values obtained by the prototype were compared with commercially available glucometers to estimate accuracy.

Results

When using images from the finger, the accuracy of the prototype is 79%. Taken from the ear, the accuracy is attenuated to 62%. Though the current data set is limited, these results are encouraging. However, three main limitations need to be addressed in future studies of the prototype: (1) increase the size of the database to improve the robustness of the artificial neural network model; (2) analyze the impact of external factors such as skin color, skin thickness, and ambient temperature in the current prototype; and (3) improve the prototype enclosure to make it suitable for easy finger and ear placement.

Conclusions

Our pilot study demonstrates that blood glucose concentration can be estimated using a small hardware prototype that uses infrared images of human tissue. Although more studies need to be conducted to overcome limitations, this pilot study shows that an affordable device can be used to avoid the use of blood and multiple finger pricks for blood glucose monitoring in the diabetic population.

Noninvasive Blood Glucose Monitoring Systems Using Near-Infrared Technology—A Review

The past few decades have seen ongoing development of continuous glucose monitoring (CGM) systems that are noninvasive and accurately measure blood glucose levels. The conventional finger-prick method, though accurate, is not feasible for use multiple times a day, as it is painful and test strips are expensive. Although minimally invasive and noninvasive CGM systems have been introduced into the market, they are expensive and require finger-prick calibrations. As the diabetes trend is high in low- and middle-income countries, a cost-effective and easy-to-use noninvasive glucose monitoring device is the need of the hour. This review paper briefly discusses the noninvasive glucose measuring technologies and their related research work. The technologies discussed are optical, transdermal, and enzymatic. The paper focuses on Near Infrared (NIR) technology and NIR Photoplethysmography (PPG) for blood glucose prediction. Feature extraction from PPG signals and glucose prediction with machine learning methods are discussed. The review concludes with key points and insights for future development of PPG NIR-based blood glucose monitoring systems.

Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied.

A Single Wavelength Mid-Infrared Photoacoustic Spectroscopy for Noninvasive Glucose Detection Using Machine Learning

According to the International Diabetes Federation, 530 million people worldwide have diabetes, with more than 6.7 million reported deaths in 2021. Monitoring blood glucose levels is essential for individuals with diabetes, and developing noninvasive monitors has been a long-standing aspiration in diabetes management. The ideal method for monitoring diabetes is to obtain the glucose concentration level with a fast, accurate, and pain-free measurement that does not require blood drawing or a surgical operation. Multiple noninvasive glucose detection techniques have been developed, including bio-impedance spectroscopy, electromagnetic sensing, and metabolic heat conformation. Nevertheless, reliability and consistency challenges were reported for these methods due to ambient temperature and environmental condition sensitivity. Among all the noninvasive glucose detection techniques, optical spectroscopy has rapidly advanced. A photoacoustic system has been developed using a single wavelength quantum cascade laser, lasing at a glucose fingerprint of 1080 cm-1 for noninvasive glucose monitoring. The system has been examined using artificial skin phantoms, covering the normal and hyperglycemia blood glucose ranges. The detection sensitivity of the system has been improved to ±25 mg/dL using a single wavelength for the entire range of blood glucose. Machine learning has been employed to detect glucose levels using photoacoustic spectroscopy in skin samples. Ensemble machine learning models have been developed to measure glucose concentration using classification techniques. The model has achieved a 90.4% prediction accuracy with 100% of the predicted data located in zones A and B of Clarke's error grid analysis. This finding fulfills the US Food and Drug Administration requirements for glucose monitors.

Skin Patchable Sensor Surveillance for Continuous Glucose Monitoring

Diabetes mellitus is a physiological and metabolic disorder affecting millions of people worldwide, associated with global morbidity, mortality, and financial expenses. Long-term complications can be avoided by frequent, continuous self-monitoring of blood glucose. Therefore, this review summarizes the current state-of-art glycemic control regimes involving measurement approaches and basic concepts. Following an introduction to the significance of continuous glucose sensing, we have tracked the evolution of glucose monitoring devices from minimally invasive to non-invasive methods to present an overview of the spectrum of continuous glucose monitoring (CGM) technologies. The conveniences, accuracy, and cost-effectiveness of the real-time CGM systems (rt-CGMs) are the factors considered for discussion. Transdermal biosensing and drug delivery routes have recently emerged as an innovative approach to substitute hypodermal needles. This work reviews skin-patchable glucose monitoring sensors for the first time, providing specifics of all the major findings in the past 6 years. Skin patch sensors and their progressive form, i.e., microneedle (MN) array sensory and delivery systems, are elaborated, covering self-powered, enzymatic, and non-enzymatic devices. The critical aspects reviewed are material design and assembly techniques focusing on flexibility, sensitivity, selectivity, biocompatibility, and user-end comfort. The review highlights the advantages of patchable MNs' multi-sensor technology designed to maintain precise blood glucose levels and administer diabetes drugs or insulin through a "sense and act" feedback loop. Subsequently, the limitations and potential challenges encountered from the MN array as rt-CGMs are listed. Furthermore, the current statuses of working prototype glucose-responsive "closed-loop" insulin delivery systems are discussed. Finally, the expected future developments and outlooks in clinical applications are discussed.

Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm⁻¹, followed by peak normalization at 850 cm⁻¹ and preprocessing by MSC.

A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring

The prevalence of diabetes is increasing globally. More than 690 million cases of diabetes are expected worldwide by 2045. Continuous blood glucose monitoring is essential to control the disease and avoid long-term complications. Diabetics suffer on a daily basis with the traditional glucose monitors currently in use, which are invasive, painful, and cost-intensive. Therefore, the demand for non-invasive, painless, economical, and reliable approaches to monitor glucose levels is increasing. Since the last decades, many glucose sensing technologies have been developed. Researchers and scientists have been working on the enhancement of these technologies to achieve better results. This paper provides an updated review of some of the pioneering non-invasive optical techniques for monitoring blood glucose levels that have been proposed in the last six years, including a summary of state-of-the-art error analysis and validation techniques.

Preventing dataset shift from breaking machine-learning biomarkers

Machine learning brings the hope of finding new biomarkers extracted from cohorts with rich biomedical measurements. A good biomarker is one that gives reliable detection of the corresponding condition. However, biomarkers are often extracted from a cohort that differs from the target population. Such a mismatch, known as a dataset shift, can undermine the application of the biomarker to new individuals. Dataset shifts are frequent in biomedical research, e.g.,  because of recruitment biases. When a dataset shift occurs, standard machine-learning techniques do not suffice to extract and validate biomarkers. This article provides an overview of when and how dataset shifts break machine-learning-extracted biomarkers, as well as detection and correction strategies.

Non-invasive Monitoring of Three Glucose Ranges Based On ECG By Using DBSCAN-CNN

Autonomic nervous system (ANS) can maintain homeostasis through the coordination of different organs including heart. The change of blood glucose (BG) level can stimulate the ANS, which will lead to the variation of Electrocardiogram (ECG). Considering that the monitoring of different BG ranges is significant for diabetes care, in this paper, an ECG-based technique was proposed to achieve non-invasive monitoring with three BG ranges: low glucose level, moderate glucose level, and high glucose level. For this purpose, multiple experiments that included fasting tests and oral glucose tolerance tests were conducted, and the ECG signals from 21 adults were recorded continuously. Furthermore, an approach of fusing density-based spatial clustering of applications with noise and convolution neural networks (DBSCAN-CNN) was presented for ECG preprocessing of outliers and classification of BG ranges based ECG. Also, ECG's important information, which was related to different BG ranges, was graphically visualized. The result showed that the percentages of accurate classification were 87.94% in low glucose level, 69.36% in moderate glucose level, and 86.39% in high glucose level. Moreover, the visualization results revealed that the highlights of ECG for the different BG ranges were different. In addition, the sensitivity of prediabetes/diabetes screening based on ECG was up to 98.48%, and the specificity was 76.75%. Therefore, we conclude that the proposed approach for BG range monitoring and prediabetes/diabetes screening has potentials in practical applications.

Clinic-on-a-Needle Array toward Future Minimally Invasive Wearable Artificial Pancreas Applications

In order to reduce medical facility overload due to the rise of the elderly population, modern lifestyle diseases, or pandemics, the medical industry is currently developing point-of-care and home medical device systems. Diabetes is an incurable and lifetime disease, accountable for a significant mortality and socio-economic public health burden. Thus, tight glucose control in diabetic patients, which can prevent the onset of its late complications, is of enormous importance. Despite recent advances, the current best achievable management of glucose control is still inadequate, due to several key limitations in the system components, mainly related to the reliability of sensing components, both temporally and chemically, and the integration of sensing and delivery components in a single wearable platform, which is yet to be achieved. Thus, advanced closed-loop artificial pancreas systems able to modulate insulin delivery according to the measured sensor glucose levels, independently of patient supervision, represent a key requirement of development efforts. Here, we demonstrate a minimally invasive, transdermal, multiplex, and versatile continuous metabolites monitoring system in the subcutaneous interstitial fluid space based on a chemically modified SiNW-FET nanosensor array on microneedle elements. Using this technology, ISF-borne metabolites require no extraction and are measured directly and continuously by the nanosensors. Due to their chemical sensing mechanism, the nanosensor response is only influenced by the specific metabolite of interest, and no response is observed in the presence of potential exogenous and endogenous interferents known to seriously affect the response of current electrochemical glucose detection approaches. The 2D architecture of this platform, using a single SOI substrate as a top-down multipurpose material, resulted in a standard fabricated chip with 3D functionality. After proving the ability of the system to act as a selective multimetabolites sensor, we have implemented our platform to reach our main goal for in vivo continuous glucose monitoring of healthy human subjects. Furthermore, minor adjustments to the fabrication technique allow the on-chip integration of microinjection needle elements, which can ideally be used as a drug delivery system. Preliminary experiments on a mice animal model successfully demonstrated the single-chip capability to both monitor glucose levels as well as deliver insulin. By that, we hope to provide in the future a cost-effective and reliable wearable personalized clinical tool for patients and a strong tool for research, which will be able to perform direct monitoring of clinical biomarkers in the ISF as well as synchronized transdermal drug delivery by this single-chip multifunctional platform.

Mid-infrared ridge waveguides fabricated in CaF2 crystal by the technique of O5+ ion irradiation

The fabrication of ridge waveguides in calcium fluoride (CaF₂) crystal working at the mid-infrared wavelength was studied. First, the planar waveguide was fabricated by using O⁵⁺ ion irradiation, and then the ridge waveguide structure was manufactured by precise diamond blade dicing. The propagation loss was measured by end-face coupling arrangement, and then annealing treatment was implemented to optimize the waveguide performance, and the propagation loss was finally reduced to 0.5 dB/cm. We measured the Raman spectra of the waveguide and substrate to observe the damage to the material lattice caused by O⁵⁺ ion irradiation technology.

Enhancing the Accuracy of Non-Invasive Glucose Sensing in Aqueous Solutions Using Combined Millimeter Wave and Near Infrared Transmission

We reported measurement results relating to non-invasive glucose sensing using a novel multiwavelength approach that combines radio frequency and near infrared signals in transmission through aqueous glucose-loaded solutions. Data were collected simultaneously in the 37–39 GHz and 900–1800 nm electromagnetic bands. We successfully detected changes in the glucose solutions with varying glucose concentrations between 80 and 5000 mg/dl. The measurements showed for the first time that, compared to single modality systems, greater accuracy on glucose level prediction can be achieved when combining transmission data from these distinct electromagnetic bands, boosted by machine learning algorithms.

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.

Non-Invasive Blood Glucose Monitoring Technology: A Review

In recent years, with the rise of global diabetes, a growing number of subjects are suffering from pain and infections caused by the invasive nature of mainstream commercial glucose meters. Non-invasive blood glucose monitoring technology has become an international research topic and a new method which could bring relief to a vast number of patients. This paper reviews the research progress and major challenges of non-invasive blood glucose detection technology in recent years, and divides it into three categories: optics, microwave and electrochemistry, based on the detection principle. The technology covers medical, materials, optics, electromagnetic wave, chemistry, biology, computational science and other related fields. The advantages and limitations of non-invasive and invasive technologies as well as electrochemistry and optics in non-invasives are compared horizontally in this paper. In addition, the current research achievements and limitations of non-invasive electrochemical glucose sensing systems in continuous monitoring, point-of-care and clinical settings are highlighted, so as to discuss the development tendency in future research. With the rapid development of wearable technology and transdermal biosensors, non-invasive blood glucose monitoring will become more efficient, affordable, robust, and more competitive on the market.

Identification and Quantitative Determination of Lactate Using Optical Spectroscopy-Towards a Noninvasive Tool for Early Recognition of Sepsis

Uninterrupted monitoring of serum lactate levels is a prerequisite in the critical care of patients prone to sepsis, cardiogenic shock, cardiac arrest, or severe lung disease. Yet there exists no device to continuously measure blood lactate in clinical practice. Optical spectroscopy together with multivariate analysis is proposed as a viable noninvasive tool for estimation of lactate in blood. As an initial step towards this goal, we inspected the plausibility of predicting the concentration of sodium lactate (NaLac) from the UV/visible, near-infrared (NIR), and mid-infrared (MIR) spectra of 37 isotonic phosphate-buffered saline (PBS) samples containing NaLac ranging from 0 to 20 mmol/L. UV/visible (300–800 nm) and NIR (800–2600 nm) spectra of PBS samples were collected using the PerkinElmer Lambda 1050 dual-beam spectrophotometer, while MIR (4000–500 cm⁻¹) spectra were collected using the Spectrum two FTIR spectrometer. Absorption bands in the spectra of all three regions were identified and functional groups were assigned. The concentration of lactate in samples was predicted using the Partial Least-Squares (PLS) regression analysis and leave-one-out cross-validation. The regression analysis showed a correlation coefficient (R²) of 0.926, 0.977, and 0.992 for UV/visible, NIR, and MIR spectra, respectively, between the predicted and reference samples. The RMSECV of UV/visible, NIR, and MIR spectra was 1.59, 0.89, and 0.49 mmol/L, respectively. The results indicate that optical spectroscopy together with multivariate models can achieve a superior technique in assessing lactate concentrations.

Surface Enhanced Raman Spectroscopy Based Biosensor with a Microneedle Array for Minimally Invasive In Vivo Glucose Measurements

To monitor blood glucose levels reliably, diabetic patients usually have to undergo frequent fingerstick tests to draw out fresh blood, which is painful and inconvenient with the potential risk of cross contamination especially when the lancet is reused or not properly sterilized. This work reports a novel surface-enhanced Raman spectroscopy (SERS) sensor for the in situ intradermal detection of glucose based on a low-cost poly(methyl methacrylate) microneedle (PMMA MN) array. After incorporating 1-decanethiol (1-DT) onto the silver-coated array surface, the sensor was calibrated in the range of 0-20 mM in skin phantoms then tested for the in vivo quantification of glucose in a mouse model of streptozocin (STZ)-induced type I diabetes. The results showed that the functional poly(methyl methacrylate) microneedle (F-PMMA MN) array was able to directly measure glucose in the interstitial fluid (ISF) in a few minutes and retain its structural integrity without swelling. The Clarke error grid analysis of measured data indicated that 93% of the data points lie in zones A and B. Moreover, the MN array exhibited minimal invasiveness to the skin as the skin recovered well without any noticeable adverse reaction in 10 min after measurements. With further improvement and proper validation, this polymeric MN array-based SERS biosensor has the potential to be used in painless glucose monitoring of diabetic patients in the future.

A Compact Mid-Infrared Spectroscopy System for Healthcare Applications Based on a Wavelength-Swept, Pulsed Quantum Cascade Laser

A mid-infrared spectroscopic system using a high-speed wavelength-swept and pulsed quantum cascade laser (QCL) for healthcare applications such as blood glucose measurement is proposed. We developed an attenuated total reflection measurement system comprising the QCL with a micro-electromechanical system (MEMS)-scanning grating, hollow optical fibers, and InAsSb detector and tested its feasibility for healthcare applications. A continuous spectrum was obtained by integrating comb-shaped spectra, the timing of which was slightly shifted. As this method does not require complex calculations, absorption spectra are obtained in real-time. We found that the signal-to-noise ratio of the obtained spectrum had been improved by increasing the number of spectra that were integrated into the spectrum calculation. Accordingly, we succeeded in measuring the absorption spectrum of a 0.1% aqueous glucose solution. Furthermore, the absorption spectra of human lips were measured, and it was shown that estimation of blood glucose levels were possible using a model equation derived using a partial least squares regression analysis of the measured absorption spectra. The spectroscopic system based on the QCL with MEMS-scanning grating has the advantages of compactness and low cost over conventional Fourier transform infrared-based systems and common spectroscopic systems with a tunable QCL that has a relatively large, movable grating.

Noninvasive, wearable, and tunable electromagnetic multisensing system for continuous glucose monitoring, mimicking vasculature anatomy

Painless, needle-free, and continuous glucose monitoring sensors are needed to enhance the life quality of diabetic patients. To that extent, we propose a first-of-its-kind, highly sensitive, noninvasive continuous glycemic monitoring wearable multisensor system. The proposed sensors are validated on serum, animal tissues, and animal models of diabetes and in a clinical setting. The noninvasive measurement results during human trials reported high correlation (>0.9) between the system's physical parameters and blood glucose levels, without any time lag. The accurate real-time responses of the sensors are attributed to their unique vasculature anatomy-inspired tunable electromagnetic topologies. These wearable apparels wirelessly sense hypo- to hyperglycemic variations with high fidelity. These components are designed to simultaneously target multiple body locations, which opens the door for the development of a closed-loop artificial pancreas.

Biomolecular and bioanalytical applications of infrared spectroscopy - A review

Infrared (IR; or mid-infrared, MIR; 4000-400 cm^-1; 2500-25,000 nm) spectroscopy has become one of the most powerful and versatile tools at the disposal of modern bioscience. Because of its high molecular specificity, applicability to wide variety of samples, rapid measurement and non-invasivity, IR spectroscopy forms a potent approach to elucidate qualitative and quantitative information from various kinds of biological material. For these reasons, it became an established bioanalytical technique with diverse applications. This work aims to be a comprehensive and critical review of the recent accomplishments in the field of biomolecular and bioanalytical IR spectroscopy. That progress is presented on a wider background, with fundamental characteristics, the basic principles of the technique outlined, and its scientific capability directly compared with other methods being used in similar fields (e.g. near-infrared, Raman, fluorescence). The article aims to present a complete examination of the topic, as it touches the background phenomena, instrumentation, spectra processing and data analytical methods, spectra interpretation and related information. To suit this goal, the article includes a tutorial information essential to obtain a thorough perspective of bio-related applications of the reviewed methodologies. The importance of the fundamental factors to the final performance and applicability of IR spectroscopy in various areas of bioscience is explained. This information is interpreted in critical way, with aim to gain deep understanding why IR spectroscopy finds extraordinarily intensive use in this remarkably diverse and dynamic field of research and utility. The major focus is placed on the diversity of the applications in which IR biospectroscopy has been established so far and those onto which it is expanding nowadays. This includes qualitative and quantitative analytical spectroscopy, spectral imaging, medical diagnosis, monitoring of biophysical processes, and studies of physicochemical properties and dynamics of biomolecules. The application potential of IR spectroscopy in light of the current accomplishments and the future prospects is critically evaluated and its significance in the progress of bioscience is comprehensively presented.

Noninvasive blood glucose detection using a quantum cascade laser

A Quantum Cascade Laser (QCL) was invented in the late 90s as a promising mid-infrared light source and it has contributed to the fields of industry, military, medicine, and biology. The room temperature operation, watt-level output power, compact size, and wide tuning capability of this laser advanced the field of noninvasive blood glucose detection with the use of transmission, absorption, and photoacoustic spectroscopy. This review provides a complete overview of the recent progress and technical details of these spectroscopy techniques, using QCL as an infrared light source for detecting blood glucose concentrations in diabetic patients.

Review of Non-invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone

Annual deaths in the U.S. attributed to diabetes are expected to increase from 280,210 in 2015 to 385,840 in 2030. The increase in the number of people affected by diabetes has made it one of the major public health challenges around the world. Better management of diabetes has the potential to decrease yearly medical costs and deaths associated with the disease. Non-invasive methods are in high demand to take the place of the traditional finger prick method as they can facilitate continuous glucose monitoring. Research groups have been trying for decades to develop functional commercial non-invasive glucose measurement devices. The challenges associated with non-invasive glucose monitoring are the many factors that contribute to inaccurate readings. We identify and address the experimental and physiological challenges and provide recommendations to pave the way for a systematic pathway to a solution. We have reviewed and categorized non-invasive glucose measurement methods based on: (1) the intrinsic properties of glucose, (2) blood/tissue properties and (3) breath acetone analysis. This approach highlights potential critical commonalities among the challenges that act as barriers to future progress. The focus here is on the pertinent physiological aspects, remaining challenges, recent advancements and the sensors that have reached acceptable clinical accuracy.

Infrared Spectroscopy with a Fiber-Coupled Quantum Cascade Laser for Attenuated Total Reflection Measurements Towards Biomedical Applications

The development of rapid and accurate biomedical laser spectroscopy systems in the mid-infrared has been enabled by the commercial availability of external-cavity quantum cascade lasers (EC-QCLs). EC-QCLs are a preferable alternative to benchtop instruments such as Fourier transform infrared spectrometers for sensor development as they are small and have high spectral power density. They also allow for the investigation of multiple analytes due to their broad tuneability and through the use of multivariate analysis. This article presents an in vitro investigation with two fiber-coupled measurement setups based on attenuated total reflection spectroscopy and direct transmission spectroscopy for sensing. A pulsed EC-QCL (1200–900 cm−1) was used for measurements of glucose and albumin in aqueous solutions, with lactate and urea as interferents. This analyte composition was chosen as an example of a complex aqueous solution with relevance for biomedical sensors. Glucose concentrations were determined in both setup types with root-mean-square error of cross-validation (RMSECV) of less than 20 mg/dL using partial least-squares (PLS) regression. These results demonstrate accurate analyte measurements, and are promising for further development of fiber-coupled, miniaturised in vivo sensors based on mid-infrared spectroscopy.

Applicability of FTIR-ATR Method to Measure Carbonyls in Blood Plasma after Physical and Mental Stress

Introduction

Oxidative stress is a state of imbalance between the production of reactive oxygen species and antioxidant defenses. It results in the oxidation of all cellular elements and, to a large extent, proteins, causing inter alia the formation of carbonyl groups in their structures. The study focused on assessment of changes in the plasma protein-bound carbonyls in police horses after combat training and after rest and the applicability of infrared spectroscopy with a Fourier transform, utilizing the attenuated total reflectance (FTIR-ATR) in detecting plasma protein oxidation.

Methods

We evaluated the influence of both the different concentrations of hydrogen peroxide and combat training on protein carbonylation in horse blood plasma. The oxidation of plasma proteins was assessed using a spectrophotometric method based on the carbonyl groups derivatization with 2,4-dinitrophenylhydrazine (DNPH). The measured values were correlated with the carbonyl groups concentrations determined by means of the FTIR-ATR method.

Results

The linear correlation between the DNPH and FTIR-ATR methods was shown. The concentration of plasma protein-bound carbonyls significantly deceased in police horses after one-day rest when compared to the values measured directly after the combat training (a drop by 23%, p<0.05 and 29%, p<0.01 measured by DNPH and FTIR-ATR methods, respectively). These results were consistent with the proteins phosphorylation analysis.

Conclusion

The FTIR-ATR method may be applied to measure the level of plasma proteins peroxidation.

Noninvasive Electromagnetic Wave Sensing of Glucose

Diabetic patients need long-term and frequent glucose monitoring to assist in insulin intake. The current finger-prick devices are painful and costly, which places noninvasive glucose sensors in high demand. In this review paper, we list several advanced electromagnetic (EM)-wave-based technologies for noninvasive glucose measurement, including infrared (IR) spectroscopy, photoacoustic (PA) spectroscopy, Raman spectroscopy, fluorescence, optical coherence tomography (OCT), Terahertz (THz) spectroscopy, and microwave sensing. The development of each method is discussed regarding the fundamental principle, system setup, and experimental results. Despite the promising achievements that have been previously reported, no established product has obtained FDA approval or survived a marketing test. The limitations of, and prospects for, these techniques are presented at the end of this review.

Parametric standing wave generation of a shallow reflection plane in a nonrigid sample for use in a noninvasive blood glucose monitor

When monitoring a moist sample using mid-infrared spectroscopy, its thickness must be <100  μm to avoid light absorption from the water. Therefore, we propose an ultrasonic-assisted mid-infrared spectroscopic imaging method that can generate a reflection plane at a depth of 100  μm from the surface of the sample by creating an ultrasonic standing wave. A frequency of 10 MHz is required to obtain an optical path length of 100  μm in biological samples. However, because biological samples generally have high compressibility, attenuation of ultrasonic waves at this frequency is significant. We use agar as a biological phantom and observe that a reflection plane is generated inside by ultrasonic standing waves using optical coherence tomography. It is found that when the sample is vibrated with an 800-kHz ultrasonic wave, a reflection plane is generated at a depth shallower than the theoretically predicted value. We believe that the reflection plane is generated by parametric standing waves, which are based on parametric effect. We detect the waveform distortion using an acoustic emission sensor and confirm the higher harmonics that generate the observed reflection plane using a fast Fourier transform.