Noninvasive Monitoring of Blood Glucose with Raman Spectroscopy

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.

Piezoelectric Peptide Nanotube Substrate Sensors Activated through Sound Wave Energy

The use of sustainable and safe materials is increasingly in demand for the creation of photonic-based technology. Piezoelectric peptide nanotubes make up a class of safe and sustainable materials. We show that these materials can generate piezoelectric charge through the deformation of oriented molecular dipoles when the tube length is flexed through the application of sound energy. Through the combination of peptide nanotubes with plasmon active nanomaterials, harvesting of low-frequency acoustic sound waves was achieved. This effect was applied to boost surface-enhanced Raman scattering signal detection of analytes, including glucose. This work demonstrates the potential of utilizing sound to boost sensing by using piezoelectric materials.

Technological Advances of Wearable Device for Continuous Monitoring of In Vivo Glucose

Managing diabetes is a chronic challenge today, requiring monitoring and timely insulin injections to maintain stable blood glucose levels. Traditional clinical testing relies on fingertip or venous blood collection, which has facilitated the emergence of continuous glucose monitoring (CGM) technology to address data limitations. Continuous glucose monitoring technology is recognized for tracking long-term blood glucose fluctuations, and its development, particularly in wearable devices, has given rise to compact and portable continuous glucose monitoring devices, which facilitates the measurement of blood glucose and adjustment of medication. This review introduces the development of wearable CGM-based technologies, including noninvasive methods using body fluids and invasive methods using implantable electrodes. The advantages and disadvantages of these approaches are discussed as well as the use of microneedle arrays in minimally invasive CGM. Microneedle arrays allow for painless transdermal puncture and are expected to facilitate the development of wearable CGM devices. Finally, we discuss the challenges and opportunities and look forward to the biomedical applications and future directions of wearable CGM-based technologies in biological research.

An integrated plant glucose monitoring system based on microneedle-enabled electrochemical sensor

Real-time monitoring of glucose concentration changes in plants and access to plant physiological information timely are of great significance to the development of precision agriculture. Here, we innovatively present an electrochemical sensing device that combines microneedle sensors and 3D printing technology to achieve real-time monitoring of glucose in plants in a minimally invasive manner. The device consists of two components: the inner part features a highly efficient sensing interface based on platinum wire (MPt-Au-Nafion-GOx-Pu), while the outer part consists of polymer microneedles formed by 3D printing. Additionally, the polymer hollow microneedle features a slender tip diameter of only 300 μm, minimizing plant damage during the detection procedure. The device shows good detection performance, with a limit of detection (LOD) of 33.3 μM and a detection sensitivity of 17 nA/μM·cm2. It can detect glucose concentrations in the range of 100 μM to 100 mM, providing a unique solution for timely agronomic management of crops tool. By performing 12 h real-time monitoring and salt stress treat on tomato and aloe vera, the results verified the feasibility of integrated device applied to real-time glucose detection in plants.

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.

Dipole moment as the underlying mechanism for enhancing the immobilization of glucose oxidase by ferrocene-chitosan for superior specificity non-invasive glucose sensing

Non-invasive methods for sensing glucose levels are highly desirable due to the comfortableness, simplicity, and lack of infection risk. However, the insufficient accuracy and ease of interference limit their practical medical applications. Here, we develop a non-invasive salivary glucose biosensor based on a ferrocene-chitosan (Fc-Chit) modified carbon nanotube (CNT) electrode through a simple drop-casting method. Compared with previous studies that relied mainly on trial and error for evaluation, this is the first time that dipole moment was proposed to optimize the electron-mediated Fc-Chit, demonstrating sturdy immobilization of glucose oxidase (GOx) on the electrode and improving the electron transfer process. Thus, the superior sensing sensitivity of the biosensor can achieve 119.97 μA mM-1 cm-2 in phosphate buffered saline (PBS) solution over a wide sensing range of 20-800 μM. Additionally, the biosensor exhibited high stability (retaining 95.0% after three weeks) and high specificity toward glucose in the presence of various interferents, attributed to the specific sites enabling GOx to be sturdily immobilized on the electrode. The results not only provide a facile solution for accurate and regular screening of blood glucose levels via saliva tests but also pave the way for designing enzymatic biosensors with specific enzyme immobilization through fundamental quantum calculations.

Correction for Extrinsic Background in Raman Hyperspectral Images

Raman hyperspectral microscopy is a valuable tool in biological and biomedical imaging. Because Raman scattering is often weak in comparison to other phenomena, prevalent spectral fluctuations and contaminations have brought advancements in analytical and chemometric methods for Raman spectra. These chemometric advances have been key contributors to the applicability of Raman imaging to biological systems. As studies increase in scale, spectral contamination from extrinsic background, intensity from sources such as the optical components that are extrinsic to the sample of interest, has become an emerging issue. Although existing baseline correction schemes often reduce intrinsic background such as autofluorescence originating from the sample of interest, extrinsic background is not explicitly considered, and these methods often fail to reduce its effects. Here, we show that extrinsic background can significantly affect a classification model using Raman images, yielding misleadingly high accuracies in the distinction of benign and malignant samples of follicular thyroid cell lines. To mitigate its effects, we develop extrinsic background correction (EBC) and demonstrate its use in combination with existing methods on Raman hyperspectral images. EBC isolates regions containing the smallest amounts of sample materials that retain extrinsic contributions that are specific to the device or environment. We perform classification both with and without the use of EBC, and we find that EBC retains biological characteristics in the spectra while significantly reducing extrinsic background. As the methodology used in EBC is not specific to Raman spectra, correction of extrinsic effects in other types of hyperspectral and grayscale images is also possible.

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.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

Dual quantum cascade lasers for noninvasive glucose detection using photoacoustic spectroscopy

The combination of mid-infrared and photoacoustic spectroscopy has shown promising developments as a substitute for invasive glucose detection technology. A dual single-wavelength quantum cascade laser system has been developed using photoacoustic spectroscopy for noninvasive glucose monitoring. Biomedical skin phantoms with similar properties to human skin have been prepared with blood components at different glucose concentrations as test models for the setup. The detection sensitivity of the system has been improved to ± 12.5 mg/dL in the hyperglycemia blood glucose ranges. An ensemble machine learning classifier has been developed to predict the glucose level in the presence of blood components. The model, which was trained with 72,360 unprocessed datasets, achieved a 96.7% prediction accuracy with 100% of the predicted data located in zones A and B of Clarke's error grid analysis. These findings fulfill both the US Food and Drug Administration and Health Canada requirements for glucose monitors.

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

Undiagnosed type 2 diabetes (T2D) remains a major public health concern. The global estimation of undiagnosed diabetes is about 46%, being this situation more critical in developing countries. Therefore, we proposed a non-invasive method to quantify glycated hemoglobin (HbA1c) and glucose in vivo. We developed a technique based on Raman spectroscopy, RReliefF as a feature selection method, and regression based on feed-forward artificial neural networks (FFNN). The spectra were obtained from the forearm, wrist, and index finger of 46 individuals. The use of FFNN allowed us to achieve an error in the predictive model of 0.69% for HbA1c and 30.12 mg/dL for glucose. Patients were classified according to HbA1c values into three categories: healthy, prediabetes, and T2D. The proposed method obtained a specificity and sensitivity of 87.50% and 80.77%, respectively. This work demonstrates the benefit of using artificial neural networks and feature selection techniques to enhance Raman spectra processing to determine glycated hemoglobin and glucose in patients with undiagnosed T2D.

A regression-based machine learning approach for pH and glucose detection with redox-sensitive colorimetric paper sensors

Colorimetric paper sensors are used in various fields due to their convenience and intuitive manner. However, these sensors present low accuracy in practical use because it is difficult to distinguish color changes for a minute amount of analyte with the naked eye. Herein, we demonstrate that a machine learning (ML)-based paper sensor platform accurately determines the color changes. We fabricated a colorimetric paper sensor by adsorbing polyaniline nanoparticles (PAni-NPs), whose color changes from blue to green when the ambient pH decreases. Adding glucose oxidase (GOx) to the paper sensor enables colorimetric glucose detection. Target analytes (10 μL) were aliquoted onto the paper sensors, and their images were taken with a smartphone under the same conditions in a darkroom. The red-green-blue (RGB) data from the images were extracted and used to train and test three regression models: support vector regression (SVR), decision tree regression (DTR), and random forest regression (RFR). Of the three regression models, RFR performed the best at estimating pH levels (R² = 0.957) ranging from pH 2 to 10 and glucose concentrations (R² = 0.922) ranging from 0 to 10 mg mL^-1.

Micro-Raman Analysis of Sperm Cells on Glass Slide: Potential Label-Free Assessment of Sperm DNA toward Clinical Applications

Routine assessment of sperm DNA integrity involves the time-consuming and complex process of staining sperm chromatin. Here, we report a Raman spectroscopy method combined with extended multiplicative signal correction (EMSC) for the extraction of characteristic fingerprints of DNA-intact and DNA-damaged sperm cells directly on glass slides. Raman results of sperm cell DNA integrity on glass substrates were validated one-to-one with clinical sperm cell staining. Although the overall Raman spectral pattern showed considerable similarity between DNA-damaged and DNA-intact sperm cells, differences in specific Raman spectral responses were observed. We then employed and compared multivariate statistical analysis based on principal component analysis-linear discriminant analysis (PCA-LDA) and partial least-squares-discriminant analysis (PLS-DA), and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. In comparison, the PLS-DA model showed relatively better results in terms of diagnostic sensitivity, specificity, and the classification rate between the sperm DNA damaged group and the DNA intact group. Our results demonstrate the potential of Raman based label-free DNA assessment of sperm cell on glass substrates as a simple method toward clinical applications.

Is Raman the best strategy towards the development of non-invasive continuous glucose monitoring devices for diabetes management?

Diabetes has no well-established cure; thus, its management is critical for avoiding severe health complications involving multiple organs. This requires frequent glycaemia monitoring, and the gold standards for this are fingerstick tests. During the last decades, several blood-withdrawal-free platforms have been being studied to replace this test and to improve significantly the quality of life of people with diabetes (PWD). Devices estimating glycaemia level targeting blood or biofluids such as tears, saliva, breath and sweat, are gaining attention; however, most are not reliable, user-friendly and/or cheap. Given the complexity of the topic and the rise of diabetes, a careful analysis is essential to track scientific and industrial progresses in developing diabetes management systems. Here, we summarize the emerging blood glucose level (BGL) measurement methods and report some examples of devices which have been under development in the last decades, discussing the reasons for them not reaching the market or not being really non-invasive and continuous. After discussing more in depth the history of Raman spectroscopy-based researches and devices for BGL measurements, we will examine if this technique could have the potential for the development of a user-friendly, miniaturized, non-invasive and continuous blood glucose-monitoring device, which can operate reliably, without inter-patient variability, over sustained periods.

Nanozyme-based colorimetric biosensor with a systemic quantification algorithm for noninvasive glucose monitoring

Diabetes mellitus accompanies an abnormally high glucose level in the bloodstream. Early diagnosis and proper glycemic management of blood glucose are essential to prevent further progression and complications. Biosensor-based colorimetric detection has progressed and shown potential in portable and inexpensive daily assessment of glucose levels because of its simplicity, low-cost, and convenient operation without sophisticated instrumentation. Colorimetric glucose biosensors commonly use natural enzymes that recognize glucose and chromophores that detect enzymatic reaction products. However, many natural enzymes have inherent defects, limiting their extensive application. Recently, nanozyme-based colorimetric detection has drawn attention due to its merits including high sensitivity, stability under strict reaction conditions, flexible structural design with low-cost materials, and adjustable catalytic activities. This review discusses various nanozyme materials, colorimetric analytic methods and mechanisms, recent machine learning based analytic methods, quantification systems, applications and future directions for monitoring and managing diabetes.

Assessing the quality of stored red blood cells using handheld Spatially Offset Raman spectroscopy with multisource correlation analysis

In this work we employ Spatially Offset Raman Spectroscopy (SORS) to non-invasively identify storage-related changes in red blood cell concentrate (RCC) in-situ within standard plastic transfusion bags. To validate the measurements, we set up a parallel study comparing both bioanalytical data (obtained by blood-gas analysis, hematology analysis and spectrophotometric assays), and Raman spectrometry data from the same blood samples. We then employ Multisource Correlation Analysis (MuSCA) to correlate the different types of data in RCC. Our analysis confirmed a strong correlation of glucose, methemoglobin and oxyhemoglobin with their respective bioassay values in RCC units. Finally, by combining MuSCA with k-means clustering, we assessed changes in all Raman wavenumbers during cold storage in both RCC Raman data from the current study and parallel RCC supernatant Raman data previously acquired from the same units. Direct RCC quality monitoring during storage, would help to establish a basis for improved inventory management of blood products in blood banks and hospitals based on analytical data.

Surface-enhanced Raman scattering: An emerging tool for sensing cellular function

Continuous long-term intracellular imaging and multiplexed monitoring of biomolecular changes associated with key cellular processes remains a challenge for the scientific community. Recently, surface-enhanced Raman scattering (SERS) has been demonstrated as a powerful spectroscopic tool in the field of biology owing to its significant advantages. Some of these include the ability to provide molecule-specific information with exquisite sensitivity, working with small volumes of precious samples, real-time monitoring, and optimal optical contrast. More importantly, the availability of a large number of novel Raman reporters with narrower full width at half maximum (FWHM) of spectral peaks/vibrational modes than conventional fluorophores has created a versatile palette of SERS-based probes that allow targeted multiplex sensing surpassing the detection sensitivity of even fluorescent probes. Due to its nondestructive nature, its applicability has been recognized for biological sensing, molecular imaging, and dynamic monitoring of complex intracellular processes. We critically discuss recent developments in this area with a focus on different applications where SERS has been used for obtaining information that remains elusive for conventional imaging methods. Current reports indicate that SERS has made significant inroads in the field of biology and has the potential to be used for in vivo human applications. This article is categorized under: Diagnostic Tools > In Vitro Nanoparticle-Based Sensing Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Diagnostic Tools > Biosensing Diagnostic Tools > In Vivo Nanodiagnostics and Imaging.

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.

A method for obtaining dynamic spectrum based on the proportion of multi-wavelength PPG waveform and applying it to noninvasive detection of human platelet content

Dynamic spectroscopy (DS) theoretically eliminates individual differences and the impact of measurement conditions on accuracy and can achieve high-precision noninvasive blood component analysis. To further improve the extraction quality of DS, this paper proposes a photoplethysmography (PPG) signal waveform proportion extraction method, which realizes the extraction of DS by calculating the proportion coefficient between PPG waveforms. To verify the effectiveness of our method, the transmission spectra of 146 volunteers' fingers and the true value of platelet content (wavelength 600-1100 nm, platelet content range 32 × 10⁹/L-512 × 10⁹/L) were collected. Then the DS was extracted by the single-trail method and this method, and the prediction model is established by PLS. The experimental data showed, compared with the single-trail method, that the modeling effect of the PPG waveform proportion method is significantly improved, the Rc increased 9.61%, the RMSEc decreased 31.56%, the MAPc decreased from 12 to 6%, and the Rp increased 42.92%, the RMSEP reduced 24.39%, and MAPp decreased from 13 to 11%. The results show that the PPG waveform proportion method proposed in this paper has a higher model correlation coefficient and lower prediction error, significantly improves the extraction quality of DS, further improves the accuracy of noninvasive blood component quantitative analysis, and helps to promote the application process of noninvasive detection of blood components.

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.

Non-Invasive Monitoring of Human Health by Photoacoustic Spectroscopy

For certain diseases, the continuous long-term monitoring of the physiological condition is crucial. Therefore, non-invasive monitoring methods have attracted widespread attention in health care. This review aims to discuss the non-invasive monitoring technologies for human health based on photoacoustic spectroscopy. First, the theoretical basis of photoacoustic spectroscopy and related devices are reported. Furthermore, this article introduces the monitoring methods for blood glucose, blood oxygen, lipid, and tumors, including differential continuous-wave photoacoustic spectroscopy, microscopic photoacoustic spectroscopy, mid-infrared photoacoustic detection, wavelength-modulated differential photoacoustic spectroscopy, and others. Finally, we present the limitations and prospects of photoacoustic spectroscopy.

Raman Spectroscopy and Its Modifications Applied to Biological and Medical Research

Nowadays, there is an interest in biomedical and nanobiotechnological studies, such as studies on carotenoids as antioxidants and studies on molecular markers for cardiovascular, endocrine, and oncological diseases. Moreover, interest in industrial production of microalgal biomass for biofuels and bioproducts has stimulated studies on microalgal physiology and mechanisms of synthesis and accumulation of valuable biomolecules in algal cells. Biomolecules such as neutral lipids and carotenoids are being actively explored by the biotechnology community. Raman spectroscopy (RS) has become an important tool for researchers to understand biological processes at the cellular level in medicine and biotechnology. This review provides a brief analysis of existing studies on the application of RS for investigation of biological, medical, analytical, photosynthetic, and algal research, particularly to understand how the technique can be used for lipids, carotenoids, and cellular research. First, the review article shows the main applications of the modified Raman spectroscopy in medicine and biotechnology. Research works in the field of medicine and biotechnology are analysed in terms of showing the common connections of some studies as caretenoids and lipids. Second, this article summarises some of the recent advances in Raman microspectroscopy applications in areas related to microalgal detection. Strategies based on Raman spectroscopy provide potential for biochemical-composition analysis and imaging of living microalgal cells, in situ and in vivo. Finally, current approaches used in the papers presented show the advantages, perspectives, and other essential specifics of the method applied to plants and other species/objects.

Comprehensive Review on Wearable Sweat-Glucose Sensors for Continuous Glucose Monitoring

The incidence of diabetes is increasing at an alarming rate, and regular glucose monitoring is critical in order to manage diabetes. Currently, glucose in the body is measured by an invasive method of blood sugar testing. Blood glucose (BG) monitoring devices measure the amount of sugar in a small sample of blood, usually drawn from pricking the fingertip, and placed on a disposable test strip. Therefore, there is a need for non-invasive continuous glucose monitoring, which is possible using a sweat sensor-based approach. As sweat sensors have garnered much interest in recent years, this study attempts to summarize recent developments in non-invasive continuous glucose monitoring using sweat sensors based on different approaches with an emphasis on the devices that can potentially be integrated into a wearable platform. Numerous research entities have been developing wearable sensors for continuous blood glucose monitoring, however, there are no commercially viable, non-invasive glucose monitors on the market at the moment. This review article provides the state-of-the-art in sweat glucose monitoring, particularly keeping in sight the prospect of its commercialization. The challenges relating to sweat collection, sweat sample degradation, person to person sweat amount variation, various detection methods, and their glucose detection sensitivity, and also the commercial viability are thoroughly covered.

Real-time Raman analysis of the hydrolysis of formaldehyde oligomers for enhanced collagen fixation

Formaldehyde (FA) is widely applied as a fixative for proteins such as collagen. Current studies have confirmed that the reversible oligomer-to-monomer equilibrium of FA in aqueous solution and the proportion of FA monomer is a significant factor affecting tissue fixation. Since the hydrolysis of FA oligomers is a dynamic process affected jointly by different factors, its real time monitoring has proved to be challenging. In this work, by utilizing the well-established Raman technique as an analytical platform, we identified the factors affecting the hydrolysis of FA oligomers by rationally examining the ν_s (OCO) and ν_as (OCO) modes with varying conditions, such as time, pH, temperature, and FA concentration. The optimized conditions of the highest hydrolysis rate of oligomers into monomers for fixation on collagen and tissues have been found to be relatively low FA concentration (≤5%) in phosphate-buffered saline at pH 9.0 in room temperature. In order to compare the fixation quality of the optimized conditions to that of the conventional conditions used by current medical practices (4% FA concentration in tap water under room temperature), Raman spectroscopy and chemical derivatization methods with o-phthalaldehyde and fluorescent probe FAP-1 have been investigated, and our results revealed that the FA molecules under our optimized conditions have reacted with at least 15% more amino groups within collagen compared to those under the conventional conditions mentioned above. This study provides direct evidence of the FA equilibrium in solution by Raman spectroscopy, which could be applied for the optimal use of FA in medicine, even at an industrial scale.

Glucose biosensors in clinical practice: principles, limits and perspectives of currently used devices

The demand of glucose monitoring devices and even of updated guidelines for the management of diabetic patients is dramatically increasing due to the progressive rise in the prevalence of diabetes mellitus and the need to prevent its complications. Even though the introduction of the first glucose sensor occurred decades ago, important advances both from the technological and clinical point of view have contributed to a substantial improvement in quality healthcare. This review aims to bring together purely technological and clinical aspects of interest in the field of glucose devices by proposing a roadmap in glucose monitoring and management of patients with diabetes. Also, it prospects other biological fluids to be examined as further options in diabetes care, and suggests, throughout the technology innovation process, future directions to improve the follow-up, treatment, and clinical outcomes of patients.

Emerging Theranostic Nanomaterials in Diabetes and Its Complications

Diabetes mellitus (DM) refers to a group of metabolic disorders that are characterized by hyperglycemia. Oral subcutaneously administered antidiabetic drugs such as insulin, glipalamide, and metformin can temporarily balance blood sugar levels, however, long-term administration of these therapies is associated with undesirable side effects on the kidney and liver. In addition, due to overproduction of reactive oxygen species and hyperglycemia-induced macrovascular system damage, diabetics have an increased risk of complications. Fortunately, recent advances in nanomaterials have provided new opportunities for diabetes therapy and diagnosis. This review provides a panoramic overview of the current nanomaterials for the detection of diabetic biomarkers and diabetes treatment. Apart from diabetic sensing mechanisms and antidiabetic activities, the applications of these bioengineered nanoparticles for preventing several diabetic complications are elucidated. This review provides an overall perspective in this field, including current challenges and future trends, which may be helpful in informing the development of novel nanomaterials with new functions and properties for diabetes diagnosis and therapy.

Applications of Advanced Materials for Non-Enzymatic Glucose Monitoring: From Invasive to the Wearable Device

Diabetes mellitus (DM) is a global health problem leading to many complications and disabilities in life adjusting activities and even dead. Monitoring glucose levels is a key factor in diagnosis and management of DM. Conventional glucose sensors consisted of immobilized enzymes, are so susceptible to environmental conditions. In this way, nonenzymatic biosensors have attracted extensive attentions in many clinical diagnostics applications. To date, the finger pricking test is a common enzyme-based glucometer that is an invasive and inconvenient and may lead to infections in the injection sites. So, working on the possibility of cutaneous or subcutaneous insertion of devices as a noninvasive or minimally-invasive systems for continuous glucose controlling approaches through human biofluids (blood, perspiration, tears, saliva, etc.) have stimulated growing interest. This review summarizes recent nonenzymatic and noninvasive biofluids glucose monitoring systems which are highly resilience and stretchable to continuously adapt to body movements during common physical activity. Sensors are based on their constituent materials including carbon-based, metal nanoparticles, polymer, and hydrogel systems are classified for electrochemical, and optical glucose detection. Finally, we address the drawbacks and challenges of enzyme-free sensors which are aroused sustaining research passion to be used in point-of-care medical diagnostics applications.

Advancing Raman spectroscopy from research to clinic: Translational potential and challenges

Raman spectroscopy has emerged as a non-invasive and versatile diagnostic technique due to its ability to provide molecule-specific information with ultrahigh sensitivity at near-physiological conditions. Despite exhibiting substantial potential, its translation from optical bench to clinical settings has been impacted by associated limitations. This perspective discusses recent clinical and biomedical applications of Raman spectroscopy and technological advancements that provide valuable insights and encouragement for resolving some of the most challenging hurdles.

Simultaneous detection of glucose, triglycerides, and total cholesterol in whole blood by Fourier-Transform Raman spectroscopy

In this paper, a reagent-free simultaneous and direct detection method of three analytes in human blood based on Fourier-transform Raman (FT-Raman) spectroscopy with 1064 nm laser radiation was proposed for the first time. A total of 161 human blood samples were characterized by FT-Raman spectroscopy under the excitation laser source of 1064 nm. In order to achieve a robust regression model, the Nonlinear Iterative Partial Least Squares (NIPALS) with orthogonal signal correction (OSC) algorithm and sample set partition based on a joint x-y distance (SPXY) is used to establish multivariate calibration models. The root means square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), correlation coefficients (R²) and ratio of performance to deviation (RPD) were 0.34255 mg/dL, 0.3662 mg/dL, 0.99982 and 56.3524 for glucose, 0.33656 mg/dL, 0.75736 mg/dL, 0.99967 and 34.9169 for total cholesterol (TC), and 0.29956 mg/dL, 0.27469 mg/dL, 0.99998 and 173.5098 for triglycerides (TG), respectively. The analysis results showed that the proposed method could be able to accurately predict the concentration of glucose, TC and TG in blood. This method can instantaneous multi-component detection on whole blood.

Multifunctional Dressing for Wound Diagnosis and Rehabilitation

A wound dressing is a sterile pad or compress that is used in direct contact with a wound to help it heal and prevent further issues or complications. Though wound healing is an intricate dynamic process that involves multiple biomolecular species, conventional wound dressings have a limited ability to provide timely information of abnormal conditions, missing the best time for early treatment. The current perspective presents and discusses the design and development of smart wound dressings that are integrated with multifunctional materials, wearable sensors and drug delivery systems as well as their application ranging from wound monitoring to timely application of therapeutics. The perspective also discusses the ongoing challenges and exciting opportunities associated with the development of wearable sensor-based smart wound dressing and provide critical insights into wound healing monitoring and management.

Noninvasive Glucose Monitor Using Dielectric Spectroscopy

OBJECTIVE

The Alertgy noninvasive continuous glucose monitor (ANICGM) is a novel wristband device that reports glucose levels without entailing skin puncture. This study evaluated the performance of the ANICGM compared to a Food and Drug Administration-approved glucose meter in patients with type 2 diabetes.

METHODS

The ANICGM device measures changes in the electromagnetic field generated by its sensor to produce a dielectric spectrum. The data contained within this spectrum are used in tandem with machine learning algorithms to estimate blood glucose levels. Values from the ANICGM were collected, sent to the Alertgy lab, formatted, and compared with fingerstick blood glucose levels, which were measured using the Accuchek Inform II glucometer. Fifteen patients completed three 120-minute sessions. The mean absolute relative difference (MARD) was calculated.

RESULTS

MARD values were compared between study days 2 and 3. The MARD for day 2 was 18.5% (95% CI, 12.8-42.2%), and the MARD for day 3 was 15.3% (95% CI, 12.3-18.4%). The difference in the MARD between days 2 and 3 was not statistically significant (P = .210).

CONCLUSION

The resulting MARDs suggest that further investigation into the use of dielectric spectroscopy for glucose monitoring should be explored.

Vibrational Spectroscopy for Detection of Diabetes: A Review

Type II diabetes mellitus (T2DM) is a metabolic disorder that is characterized by chronically elevated glucose caused by insulin resistance. Although T2DM is manageable through insulin therapy, the disorder itself is a risk factor for much more dangerous diseases including cardiovascular disease, kidney disease, retinopathy, Alzheimer's disease, and more. T2DM affects 450 million people worldwide and is attributed to causing over four million deaths each year. Current methods for detecting diabetes typically involve testing a person's glycated hemoglobin levels as well as blood sugar levels randomly or after fasting. However, these methods can be problematic due to an individual's levels differing on a day-to-day basis or being affected by diet or environment, and due to the lack of sensitivity and reliability within the tests themselves. Vibrational spectroscopic methods have been pursued as a novel method for detecting diabetes accurately and early in a minimally invasive manner. This review summarizes recent research, since 2015, which has used infrared or Raman spectroscopy for the purpose of developing a fast and accurate method for diagnosing diabetes. Based on critical evaluation of the reviewed work, vibrational spectroscopy has the potential to improve and revolutionize the way diabetes is diagnosed, thereby allowing for faster and more effective treatment of the disorder.

The Impact of Recent Developments in Electrochemical POC Sensor for Blood Sugar Care

Rapid glucose testing is very important in the care of diabetes. Monitoring of blood glucose is the most critical indicator of disease control in diabetic patients. The invention and popularity of electrochemical sensors have made glucose detection fast and inexpensive. The first generation of glucose sensors had limitations in terms of sensitivity and selectivity. In order to overcome these problems, scientists have used a range of new materials to produce new glucose electrochemical sensors with higher sensitivity, selectivity and lower cost. A variety of different electrochemical sensors including enzymatic electrochemical sensors and enzyme-free electrochemical sensors have been extensively investigated. We discussed the development process of electrochemical glucose sensors in this review. We focused on describing the benefits of carbon materials in nanomaterials, specially graphene for sensors. In addition, we discussed the limitations of the sensors and challenges in future research.

A Versatile Multichannel Instrument for Measurement of Ratiometric Fluorescence Intensity and Phosphorescence Lifetime

Optical biosensing is being actively investigated for minimally-invasive monitoring of key biomarkers both in vitro and in vivo. However, typical benchtop instruments are not portable and are not well suited to high-throughput, real-time analysis. This paper presents a versatile multichannel instrument for measurement of emission intensity and lifetime values arising from luminescent biosensor materials. A detailed design description of the opto-electronic hardware as well as the control software is provided, elaborating a flexible, user-configurable system that may be customized or duplicated for a wide range of applications. This article presents experimental measurements that prove the in vitro and in vivo functionality of the system. Such tools may be adopted for many research and development purposes, including evaluation of new biosensor materials, and may also serve as prototypes for future miniaturized handheld or wearable devices.

Low dimensional materials for glucose sensing

Biosensors are essential components for effective healthcare management. Since biological processes occur on molecular scales, nanomaterials and nanosensors intrinsically provide the most appropriate landscapes for developing biosensors. Low-dimensional materials have the advantage of offering high surface areas, increased reactivity and unique physicochemical properties for efficient and selective biosensing. So far, nanomaterials and nanodevices have offered significant prospects for glucose sensing. Targeted glucose biosensing using such low-dimensional materials enables much more effective monitoring of blood glucose levels, thus providing significantly better predictive diabetes diagnostics and management. In this review, recent advances in using low dimensional materials for sensing glucose are summarized. Sensing fundamentals are discussed, as well as invasive, minimally-invasive and non-invasive sensing methods. The effects of morphological characteristics and size-dependent properties of low dimensional materials are explored for glucose sensing, and the key performance parameters such as selectivity, stability and sensitivity are also discussed. Finally, the challenges and future opportunities that low dimensional materials can offer for glucose sensing are outlined.

Optical Glucose Sensor Using Pressure Sensitive Paint

A glucose sensor is used as an essential tool for diagnosing and treating diabetic patients and controlling processes during cell culture. Since the development of an electrochemical-based glucose sensor, an optical glucose sensor has been devised to overcome its shortcomings, but this also poses a problem because it requires a complicated manufacturing process. This study aimed to develop an optical glucose sensor film that could be fabricated with a simple process using commercial pressure sensitive paints. The sensor manufacturing technology developed in this work could simplify the complex production process of the existing electrochemical or optical glucose sensors. In addition, a photometric method for glucose concentration analysis was developed using the color image of the sensor. By developing this sensor and analysis technology, the basis for glucose measurement was established that enables two-dimensional, online, and continuous measurement. The proposed sensor showed good linearity at 0–4 mM glucose in an aqueous sample solution, its limit of detection was 0.37 mM, and the response time was 2 min.

Ratiometric Fluorescent Biosensors for Glucose and Lactate Using an Oxygen-Sensing Membrane

In this study, ratiometric fluorescent glucose and lactate biosensors were developed using a ratiometric fluorescent oxygen-sensing membrane immobilized with glucose oxidase (GOD) or lactate oxidase (LOX). Herein, the ratiometric fluorescent oxygen-sensing membrane was fabricated with the ratio of two emission wavelengths of platinum meso-tetra (pentafluorophenyl) porphyrin (PtP) doped in polystyrene particles and coumarin 6 (C6) captured into silica particles. The operation mechanism of the sensing membranes was based on (i) the fluorescence quenching effect of the PtP dye by oxygen molecules, and (ii) the consumption of oxygen levels in the glucose or lactate oxidation reactions under the catalysis of GOD or LOX. The ratiometric fluorescent glucose-sensing membrane showed high sensitivity to glucose in the range of 0.1–2 mM, with a limit of detection (LOD) of 0.031 mM, whereas the ratiometric fluorescent lactate-sensing membrane showed the linear detection range of 0.1–0.8 mM, with an LOD of 0.06 mM. These sensing membranes also showed good selectivity, fast reversibility, and stability over long-term use. They were applied to detect glucose and lactate in artificial human serum, and they provided reliable measurement results.

Utilizing pulse dynamics for non-invasive Raman spectroscopy of blood analytes

Non-invasive measurement methods offer great benefits in the field of medical diagnostics with molecular-specific techniques such as Raman spectroscopy which is increasingly being used for quantitative measurements of tissue biochemistry in vivo. However, some important challenges still remain for label-free optical spectroscopy to be incorporated into the clinical laboratory for routine testing. In particular, non-analyte-specific variations in tissue properties introduce significant variability of the spectra, thereby preventing reliable calibration. For measurements of blood analytes such as glucose, we propose to decrease the interference from individual tissue characteristics by exploiting the known dynamics of the blood-tissue matrix. We reason that by leveraging the natural blood pulse rhythm, the signals from the blood analytes can be enhanced while those from the static components can be effectively suppressed. Here, time-resolved measurements with subsequent pulse frequency estimation and phase-sensitive detection are proposed to recover the Raman spectra correlated with the dynamic changes at blood-pulse frequency. Pilot in vivo study results are presented to establish the benefits as well as outline the challenges of the proposed method in terms of instrumentation and signal processing.

Cigarette smoke-induced changes in the murine vocal folds: a Raman spectroscopic observation

Raman spectroscopic methods are being projected as novel tools to study the early invisible molecular level changes in a label-free manner. In the present study, we have used Raman spectroscopy to explore the earliest biochemical changes in murine vocal folds in response to time-bound cigarette smoke exposure. Mice were exposed to cigarette smoke for 2 or 4-weeks through a customized smoke inhalation system. The larynx was collected and initial evaluations using standard methods of analysis such as histopathology and immunofluorescence was performed. Concurrent unstained sections were used for Raman imaging. Two common pathological features of vocal fold disorders including alterations in collagen content and epithelial hypercellularity, or hyperplasia, were observed. The mean spectra, principal component analysis, and Raman mapping also revealed differences in the collagen content and hypercellularity in the smoke exposed tissues. The differences in 2-week exposed tissues were found to be more prominent as compared to 4-week. This was attributed to adaptive responses and the already reported biphasic effects, which suggest that collagen synthesis is significantly reduced at higher cigarette smoke concentrations. Overall findings of the study are supportive of the prospective application of Raman imaging in monitoring changes due to cigarette smoke in the vocal folds.

Non-invasive monitoring of red blood cells during cold storage using handheld Raman spectroscopy

BACKGROUND

The current best practices allow for the red blood cells (RBCs) to be stored for prolonged periods in blood banks worldwide. However, due to the individual-related variability in donated blood and RBCs continual degradation within transfusion bags, the quality of stored blood varies considerably. There is currently no method for assessing the blood product quality without compromising the sterility of the unit. This study demonstrates the feasibility of monitoring storage lesion of RBCs in situ while maintaining sterility using an optical approach.

STUDY DESIGN AND METHODS

A handheld spatially offset Raman spectroscopy (RS) device was employed to non-invasively monitor hemolysis and metabolic changes in 12 red cell concentrate (RCC) units within standard sealed transfusion bags over 7 weeks of cold storage. The donated blood was analyzed in parallel by biochemical (chemical analysis, spectrophotometry, hematology analysis) and RS measurements, which were then correlated through multisource correlation analysis.

RESULTS

Raman bands of lactate (857 cm^-1 ), glucose (787 cm^-1 ), and hemolysis (1003 cm^-1 ) were found to correlate strongly with bioanalytical data over the length of storage, with correlation values 0.98 (95% confidence interval [CI]: 0.86-1.00; p = .0001), 0.95 (95% CI: 0.71-0.99; p = .0008) and 0.97 (95% CI: 0.79-1.00; p = .0004) respectively.

DISCUSSION

This study demonstrates the potential of collecting information on the clinical quality of blood units without breaching the sterility using Raman technology. This could significantly benefit quality control of RCC units, patient safety and inventory management in blood banks and hospitals.

A meta-analysis of studies examining associations between resonance Raman spectroscopy-assessed skin carotenoids and plasma carotenoids among adults and children

CONTEXT

No meta-analyses appeared to have been conducted to examine overall correlations between resonance Raman spectroscopy (RRS)-assessed skin carotenoids and plasma/serum carotenoids.

OBJECTIVE

To review the available literature and quantify the association between RRS-assessed skin carotenoids and plasma/serum carotenoids via a meta-analysis of observational studies.

DATA SOURCES

To identify relevant publications, we searched the PubMed, Embase, CINAHL, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, ProQuest, and Scopus databases in April 2020 for items combining 3 concepts: Raman spectroscopy, skin, and plasma or serum.

DATA EXTRACTION

Criteria for inclusion were publication in a peer-reviewed journal between 1990 and 2020, available in English language, and results reported as a baseline Pearson correlation coefficient. In teams of 2, the researchers independently reviewed titles and abstracts of 2212 nonduplicate papers with initial screening yielding 62 papers for full-text review, of which 15 were deemed eligible for inclusion.

DATA ANALYSIS

A random-effects model in R (version 4.0.0) "meta" package was used to analyze the correlation between RRS-assessed skin and plasma/serum carotenoids. A subgroup analysis was conducted for studies involving adults and children, respectively.

CONCLUSIONS

The 15 studies included 1155 individuals: 963 adults and 192 children. One study included children and adults. The random-effects model yielded an overall correlation of 0.68 (95%CI, 0.61-0.74; I2 = 74%; P < 0.01). The results were similar when grouped by adults and children. Among 963 adults, the correlation in the random-effects model was 0.69 (95%CI, 0.61-0.75; I2 = 78%; P < 0.01). Among 192 children, the correlation in the random-effects model was 0.66 (95%CI, 0.52- 0.77; I2 = 55%; P = 0.06). Overall, there was a positive, statistically significant correlation between RRS-assessed skin carotenoids and plasma/serum carotenoids in a pooled meta-analysis of 15 studies.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO (record number 178835).

Combination of pathological and spectroscopic characterization to promote diagnosis of retinal pigment epithelium-Bruch's membrane complex in a diabetic rat model

Diabetic retinopathy (DR) is a common condition of diabetes, and approaches to detecting early DR using the unique characteristics of the retinal pigment epithelium-Bruch's membrane complex (RBC) have increasingly attracted attention. A diabetic model was established in Sprague-Dawley rats via streptozocin (STZ) injection for 1 (DM1) and 6 months (DM6), confirmed by weekly blood glucose measurement. Serum and retinal tissue-based advanced glycation endproducts (AGE) levels significantly elevated in diabetic rats, and RBC was evaluated by transmission electron microscopy and Raman spectroscopy. The results showed that whole Raman spectra and all marked band intensities could respectively achieve almost equal and accurate discrimination of all animal groups, along with the determination of important molecules from the band data. Further quantitative analyses indicated series of metabolic disturbance due to hyperglycemia were involved while the body self-regulation mechanism still played a role with different effects during the disease progression. Given this, Raman spectroscopy can reliably distinguish the early characterization of DR in addition to providing intrinsic key molecules that is sensitive to identify the early disease progression.

Silver-Coated Disordered Silicon Nanowires Provide Highly Sensitive Label-Free Glycated Albumin Detection through Molecular Trapping and Plasmonic Hotspot Formation

Glycated albumin (GA) is rapidly emerging as a robust biomarker for screening and monitoring of diabetes. To facilitate its rapid, point-of-care measurements, a label-free surface-enhanced Raman spectroscopy (SERS) sensing platform is reported that leverages the specificity of molecular vibrations and signal amplification on silver-coated silicon nanowires (Ag/SiNWs) for highly sensitive and reproducible quantification of GA. The simulations and experimental measurements demonstrate that the disordered orientation of the nanowires coupled with the wicking of the analyte molecules during the process of solvent evaporation facilitates molecular trapping at the generated plasmonic hotspots. Highly sensitive detection of glycated albumin is shown with the ability to visually detect spectral features at as low as 500 × 10^-9 m, significantly below the physiological range of GA in body fluids. Combined with chemometric regression models, the spectral data recorded on the Ag/SiNWs also allow accurate prediction of glycated concentration in mixtures of glycated and non-glycated albumin in proportions that reflect those in the bloodstream.

Evaluation of Opportunities and Limitations of Mid-Infrared Skin Spectroscopy for Noninvasive Blood Glucose Monitoring

BACKGROUND

A wide range of optical techniques has recently been presented for the development of noninvasive methods for blood glucose sensing based on multivariate skin spectrum analysis, and most recent studies are reviewed in short by us. The vibrational spectral fingerprints of glucose, as especially found in the mid-infrared or Raman spectrum, have been suggested for achieving largest selectivity for the development of noninvasive blood glucose methods.

METHODS

Here, the different aspects on integral skin measurements are presented, which are much dependent on the absorption characteristics of water as the main skin constituent. In particular, different mid-infrared measurement techniques as realized recently are discussed. The limitations of the use of the attenuated total reflection technique in particular are elaborated, and confounding skin or saliva spectral features are illustrated and discussed in the light of recently published works, claiming that the attenuated total reflection technique can be utilized for noninvasive measurements.

RESULTS

It will be shown that the penetration depth of the infrared radiation with wavelengths around 10 µm is the essential parameter, which can be modulated by different measurement techniques as with photothermal or diffuse reflection. However, the law of physics is limiting the option of using the attenuated total reflection technique with waveguides from diamond or similar optical materials.

CONCLUSIONS

There are confounding features from mucosa, stratum corneum, or saliva, which have been misinterpreted for glucose measurements. Results of an earlier study with multivariate evaluation based on glucose fingerprint features are again referred to as a negative experimental proof.