Phantom glucose calibration models from simulated noninvasive human near-infrared spectra

Selectivity and Sensitivity of Near-Infrared Spectroscopic Sensing of β-Hydroxybutyrate, Glucose, and Urea in Ternary Aqueous Solutions

The next-generation artificial pancreas is under development with the goal to enhance tight glycemic control for people with type 1 diabetes. Such technology requires the integration of a chemical sensing unit combined with an insulin infusion device controlled by an algorithm capable of autonomous operation. The potential of near-infrared spectroscopic sensing to serve as the chemical sensing unit is explored by demonstrating the ability to quantify multiple metabolic biomarkers from a single near-infrared spectrum. Independent measurements of β-hydroxy-butyrate, glucose, and urea are presented based on analysis of near-infrared spectra collected over the combination spectral range of 5000-4000 cm^-1 for a set of 50 ternary aqueous standard solutions. Spectra are characterized by a 1 μAU root-mean-square (RMS) noise for 100% lines with a resolution of 4 cm^-1 and an optical path length of 1 mm. Calibration models created by the net analyte signal (NAS) and the partial least squares (PLS) methods provide selective measurements for each analyte with standard errors of prediction in the upper micromolar concentration range. The NAS method is used to determine both the selectivity and sensitivity for each analyte and their values are consistent with these standard errors of prediction. The NAS method is also used to characterize the background spectral variance associated with instrumental and environmental variations associated with buffer spectra collected over a multiday period.

Label-Free Raman Spectroscopy Reveals Signatures of Radiation Resistance in the Tumor Microenvironment

Delay in the assessment of tumor response to radiotherapy continues to pose a major challenge to quality of life for patients with nonresponsive tumors. Here, we exploited label-free Raman spectroscopic mapping to elucidate radiation-induced biomolecular changes in tumors and uncovered latent microenvironmental differences between treatment-resistant and -sensitive tumors. We used isogenic radiation-resistant and -sensitive A549 human lung cancer cells and human head and neck squamous cell carcinoma (HNSCC) cell lines (UM-SCC-47 and UM-SCC-22B, respectively) to grow tumor xenografts in athymic nude mice and demonstrated the molecular specificity and quantitative nature of Raman spectroscopic tissue assessments. Raman spectra obtained from untreated and treated tumors were subjected to chemometric analysis using multivariate curve resolution-alternating least squares (MCR-ALS) and support vector machine (SVM) to quantify biomolecular differences in the tumor microenvironment. The Raman measurements revealed significant and reliable differences in lipid and collagen content postradiation in the tumor microenvironment, with consistently greater changes observed in the radiation-sensitive tumors. In addition to accurately evaluating tumor response to therapy, the combination of Raman spectral markers potentially offers a route to predicting response in untreated tumors prior to commencing treatment. Combined with its noninvasive nature, our findings provide a rationale for in vivo studies using Raman spectroscopy, with the ultimate goal of clinical translation for patient stratification and guiding adaptation of radiotherapy during the course of treatment. SIGNIFICANCE: These findings highlight the sensitivity of label-free Raman spectroscopy to changes induced by radiotherapy and indicate the potential to predict radiation resistance prior to commencing therapy.

Nocturnal Hypoglycemic Alarm Based on Near-Infrared Spectroscopy: In Vivo Studies with a Rat Animal Model

A noninvasive method for detecting episodes of nocturnal hypoglycemia is demonstrated with in vivo measurements made with a rat animal model. Employing spectra collected from the near-infrared combination region of 4000-5000 cm^-1, piecewise linear discriminant analysis (PLDA) is used to classify spectra into alarm and nonalarm data classes on the basis of whether or not they correspond to glucose concentrations below a user-defined hypoglycemic threshold. A reference spectrum and corresponding glucose concentration are acquired at the start of the monitoring period, and spectra are then collected continuously and converted to absorbance units relative to the initial reference spectrum. The resulting differential spectra correspond to differential glucose concentrations that reflect the differences in concentration between each spectrum and the reference. Given an alarm threshold (e.g., 3.0 mM), a database of calibration differential spectra can be partitioned into two groups containing spectra above and below the threshold. A classification model is then computed with PLDA. The resulting model can be applied to the differential spectra collected during the monitoring period in order to identify spectra whose corresponding glucose concentrations lie in the hypoglycemic range. In this work, the alarm algorithm was tested in two single-day studies performed with anesthetized rats. Glucose concentrations spanned the range of 1.6 to 13.5 mM (29 to 244 mg/dL). For both rats, the alarm algorithm performed well. On average, 87.5% of alarm events were correctly detected, and the occurrence of false alarms was 7.2%. False alarms were restricted to times when the glucose concentrations were very close to the alarm threshold rather than at random times, thus demonstrating the potential of the approach for practical use.

Preliminary Clinical Validation of a Differential Correction Method for Improving Measurement Accuracy in Noninvasive Measurement of Blood Glucose Using Near-Infrared Spectroscopy

One of the main challenges in the noninvasive sensing of blood glucose by near-infrared (NIR) spectroscopy is the background variations from light source drift, sweating, and temperature change at the human-machine interface. In this paper, a differential correction method based on the spectra from the floating-reference position and measuring position is proposed to eliminate these spectral variations from background interferences. Its effectiveness was validated by in vitro and in vivo experiments in which the diffuse reflectance of intralipid solutions and human skin was collected at the source distances of 0.6 mm and 2 mm by the custom-built system with six super-luminescent emitting diodes (SLEDs) light source. The results showed that, for the in vitro experiments of intralipid solutions, the coefficients of variations of diffuse reflectance decreased by 20.5% under all the six wavelengths after differential correction. For the in vivo experiments of oral glucose tolerance tests (OGTTs), partial least squares (PLS) regression models between glucose concentrations and the diffuse reflectance from palm skin were built, and the root mean square error of cross validation (RMSECV) decreased by 38.0% on average after the differential correction. Further, the spectra of the oral water tolerance tests (OWTTs) were collected for correlation with glucose concentration in OGTTs, and their correlation coefficients (R) decreased by 35.0% on average after the differential correction. Therefore, this differential correction method based on the spectra from the floating-reference position and measuring position can weaken the influence of background variations on the NIR spectroscopy and has promising potential in in vivo detection, especially for noninvasive blood glucose measurement.

Noninvasive Monitoring of Blood Glucose with Raman Spectroscopy

The successful development of a noninvasive blood glucose sensor that can operate reliably over sustained periods of time has been a much sought after but elusive goal in diabetes management. Since diabetes has no well-established cure, control of elevated glucose levels is critical for avoiding severe secondary health complications in multiple organs including the retina, kidney and vasculature. While fingerstick testing continues to be the mainstay of blood glucose detection, advances in electrochemical sensing-based minimally invasive approaches have opened the door for alternate methods that would considerably improve the quality of life for people with diabetes. In the quest for better sensing approaches, optical technologies have surfaced as attractive candidates as researchers have sought to exploit the endogenous contrast of glucose, notably its absorption, scattering, and polarization properties. Vibrational spectroscopy, especially spontaneous Raman scattering, has exhibited substantial promise due to its exquisite molecular specificity and minimal interference of water in the spectral profiles acquired from the blood-tissue matrix. Yet, it has hitherto been challenging to leverage the Raman scattering signatures of glucose for prediction in all but the most basic studies and under the least demanding conditions. In this Account, we discuss the newly developed array of methodologies that address the key challenges in measuring blood glucose accurately using Raman spectroscopy and unlock new prospects for translation to sustained noninvasive measurements in people with diabetes. Owing to the weak intensity of spontaneous Raman scattering, recent research has focused on enhancement of signals from the blood constituents by designing novel excitation-collection geometries and tissue modulation methods while our attempts have led to the incorporation of nonimaging optical elements. Additionally, invoking mass transfer modeling into chemometric algorithms has not only addressed the physiological lag between the actual blood glucose and the measured interstitial fluid glucose values but also offered a powerful tool for predictive measurements of hypoglycemia. This framework has recently been extended to provide longitudinal tracking of glucose concentration without necessitating extensive a priori concentration information. These findings are advanced by the results of recent glucose tolerance studies in human subjects, which also hint at the need for designing nonlinear calibration models that can account for subject-to-subject variations in skin heterogeneity and hematocrit levels. Together, the emerging evidence underscores the promise of a blood withdrawal-free optical platform-featuring a combination of high-throughput Raman spectroscopic instrumentation and data analysis of subtle variations in spectral expression-for diabetes screening in the clinic and, ultimately, for personalized monitoring.

Label-Free Raman Spectroscopy Detects Stromal Adaptations in Premetastatic Lungs Primed by Breast Cancer

Recent advances in animal modeling, imaging technology, and functional genomics have permitted precise molecular observations of the metastatic process. However, a comprehensive understanding of the premetastatic niche remains elusive, owing to the limited tools that can map subtle differences in molecular mediators in organ-specific microenvironments. Here, we report the ability to detect premetastatic changes in the lung microenvironment, in response to primary breast tumors, using a combination of metastatic mouse models, Raman spectroscopy, and multivariate analysis of consistent patterns in molecular expression. We used tdTomato fluorescent protein expressing MDA-MB-231 and MCF-7 cells of high and low metastatic potential, respectively, to grow orthotopic xenografts in athymic nude mice and allow spontaneous dissemination from the primary mammary fat pad tumor. Label-free Raman spectroscopic mapping was used to record the molecular content of premetastatic lungs. These measurements show reliable distinctions in vibrational features, characteristic of the collageneous stroma and its cross-linkers as well as proteoglycans, which uniquely identify the metastatic potential of the primary tumor by recapitulating the compositional changes in the lungs. Consistent with histological assessment and gene expression analysis, our study suggests that remodeling of the extracellular matrix components may present promising markers for objective recognition of the premetastatic niche, independent of conventional clinical information. Cancer Res; 77(2); 247-56. ©2016 AACR.

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

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

Emerging trends in optical sensing of glycemic markers for diabetes monitoring

In the past decade, considerable attention has been focused on the measurement of glycemic markers, such as glycated hemoglobin and glycated albumin, that provide retrospective indices of average glucose levels in the bloodstream. While these biomarkers have been regularly used to monitor long-term glucose control in established diabetics, they have also gained traction in diabetic screening. Detection of such glycemic markers is challenging, especially in a point-of-care setting, due to the stringent requirements for sensitivity and robustness. A number of non-separation based measurement strategies were recently proposed, including photonic tools that are well suited to reagent-free marker quantitation. Here, we critically review these methods while focusing on vibrational spectroscopic methods, which offer highly specific molecular fingerprinting capability. We examine the underlying principles and the utility of these approaches as reagentless assays capable of multiplexed detection of glycemic markers and also the challenges in their eventual use in the clinic.

Spectroscopic approach for dynamic bioanalyte tracking with minimal concentration information

Vibrational spectroscopy has emerged as a promising tool for non-invasive, multiplexed measurement of blood constituents - an outstanding problem in biophotonics. Here, we propose a novel analytical framework that enables spectroscopy-based longitudinal tracking of chemical concentration without necessitating extensive a priori concentration information. The principal idea is to employ a concentration space transformation acquired from the spectral information, where these estimates are used together with the concentration profiles generated from the system kinetic model. Using blood glucose monitoring by Raman spectroscopy as an illustrative example, we demonstrate the efficacy of the proposed approach as compared to conventional calibration methods. Specifically, our approach exhibits a 35% reduction in error over partial least squares regression when applied to a dataset acquired from human subjects undergoing glucose tolerance tests. This method offers a new route at screening gestational diabetes and opens doors for continuous process monitoring without sample perturbation at intermediate time points.

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

In this paper, the effects of two-dimensional correlation spectroscopy (2DCOS) on chance correlations in the spectral data, generated from the correlations between glucose concentration and some undesirable experimental factors, such as instrument drift, sample temperature variations, and interferent compositions in the sample matrix, are investigated. The aim is to evaluate the validity of the spectral data set, instead of assessing the calibration models, and then to provide a complementary procedure for better verifying or rejecting the data set. It includes tracing back to the source of the chance correlation on the chemical basis, selecting appropriate preprocessing methods before building multivariate calibration models, and therefore may avoid invalid models. The utility of the proposed analysis is demonstrated with a series of aqueous solutions using near-infrared spectra over the overtone band of glucose. Results show that, spectral variations from chance correlations induced by those experimental factors can be determined by the 2DCOS method, which develops avenues for prospectively accurate prediction in clinical application of this technology.

Raman spectroscopy-based sensitive and specific detection of glycated hemoglobin

In recent years, glycated hemoglobin (HbA1c) has been increasingly accepted as a functional metric of mean blood glucose in the treatment of diabetic patients. Importantly, HbA1c provides an alternate measure of total glycemic exposure due to the representation of blood glucose throughout the day, including post-prandially. In this article, we propose and demonstrate the potential of Raman spectroscopy as a novel analytical method for quantitative detection of HbA1c, without using external dyes or reagents. Using the drop coating deposition Raman (DCDR) technique, we observe that the nonenzymatic glycosylation (glycation) of the hemoglobin molecule results in subtle but discernible and highly reproducible changes in the acquired spectra, which enable the accurate determination of glycated and nonglycated hemoglobin using standard chemometric methods. The acquired Raman spectra display excellent reproducibility of spectral characteristics at different locations in the drop and show a linear dependence of the spectral intensity on the analyte concentration. Furthermore, in hemolysate models, the developed multivariate calibration models for HbA1c show a high degree of prediction accuracy and precision--with a limit of detection that is a factor of ~15 smaller than the lowest physiological concentrations encountered in clinical practice. The excellent accuracy and reproducibility achieved in this proof-of-concept study opens substantive avenues for characterization and quantification of the glycosylation status of (therapeutic) proteins, which are widely used for biopharmaceutical development. We also envision that the proposed approach can provide a powerful tool for high-throughput HbA1c sensing in multicomponent mixtures and potentially in hemolysate and whole blood lysate samples.

Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation

We present the first demonstration of glycated albumin detection and quantification using Raman spectroscopy without the addition of reagents. Glycated albumin is an important marker for monitoring the long-term glycemic history of diabetics, especially as its concentrations, in contrast to glycated hemoglobin levels, are unaffected by changes in erythrocyte life times. Clinically, glycated albumin concentrations show a strong correlation with the development of serious diabetes complications including nephropathy and retinopathy. In this article, we propose and evaluate the efficacy of Raman spectroscopy for determination of this important analyte. By utilizing the pre-concentration obtained through drop-coating deposition, we show that glycation of albumin leads to subtle, but consistent, changes in vibrational features, which with the help of multivariate classification techniques can be used to discriminate glycated albumin from the unglycated variant with 100% accuracy. Moreover, we demonstrate that the calibration model developed on the glycated albumin spectral dataset shows high predictive power, even at substantially lower concentrations than those typically encountered in clinical practice. In fact, the limit of detection for glycated albumin measurements is calculated to be approximately four times lower than its minimum physiological concentration. Importantly, in relation to the existing detection methods for glycated albumin, the proposed method is also completely reagent-free, requires barely any sample preparation and has the potential for simultaneous determination of glycated hemoglobin levels as well. Given these key advantages, we believe that the proposed approach can provide a uniquely powerful tool for quantification of glycation status of proteins in biopharmaceutical development as well as for glycemic marker determination in routine clinical diagnostics in the future.

Investigation of the specificity of Raman spectroscopy in non-invasive blood glucose measurements

Although several in vivo blood glucose measurement studies have been performed by different research groups using near-infrared (NIR) absorption and Raman spectroscopic techniques, prospective prediction has proven to be a challenging problem. An important issue in this case is the demonstration of causality of glucose concentration to the spectral information, especially as the intrinsic glucose signal is smaller compared with that of the other analytes in the blood-tissue matrix. Furthermore, time-dependent physiological processes make the relation between glucose concentration and spectral data more complex. In this article, chance correlations in Raman spectroscopy-based calibration model for glucose measurements are investigated for both in vitro (physical tissue models) and in vivo (animal model and human subject) cases. Different spurious glucose concentration profiles are assigned to the Raman spectra acquired from physical tissue models, where the glucose concentration is intentionally held constant. Analogous concentration profiles, in addition to the true concentration profile, are also assigned to the datasets acquired from an animal model during a glucose clamping study as well as a human subject during an oral glucose tolerance test. We demonstrate that the spurious concentration profile-based calibration models are unable to provide prospective predictions, in contrast to those based on actual concentration profiles, especially for the physical tissue models. We also show that chance correlations incorporated by the calibration models are significantly less in Raman as compared to NIR absorption spectroscopy, even for the in vivo studies. Finally, our results suggest that the incorporation of chance correlations for in vivo cases can be largely attributed to the uncontrolled physiological sources of variations. Such uncontrolled physiological variations could either be intrinsic to the subject or stem from changes in the measurement conditions.

Effect of photobleaching on calibration model development in biological Raman spectroscopy

A major challenge in performing quantitative biological studies using Raman spectroscopy lies in overcoming the influence of the dominant sample fluorescence background. Moreover, the prediction accuracy of a calibration model can be severely compromised by the quenching of the endogenous fluorophores due to the introduction of spurious correlations between analyte concentrations and fluorescence levels. Apparently, functional models can be obtained from such correlated samples, which cannot be used successfully for prospective prediction. This work investigates the deleterious effects of photobleaching on prediction accuracy of implicit calibration algorithms, particularly for transcutaneous glucose detection using Raman spectroscopy. Using numerical simulations and experiments on physical tissue models, we show that the prospective prediction error can be substantially larger when the calibration model is developed on a photobleaching correlated dataset compared to an uncorrelated one. Furthermore, we demonstrate that the application of shifted subtracted Raman spectroscopy (SSRS) reduces the prediction errors obtained with photobleaching correlated calibration datasets compared to those obtained with uncorrelated ones.

Detecting vital signs with wearable wireless sensors

The emergence of wireless technologies and advancements in on-body sensor design can enable change in the conventional health-care system, replacing it with wearable health-care systems, centred on the individual. Wearable monitoring systems can provide continuous physiological data, as well as better information regarding the general health of individuals. Thus, such vital-sign monitoring systems will reduce health-care costs by disease prevention and enhance the quality of life with disease management. In this paper, recent progress in non-invasive monitoring technologies for chronic disease management is reviewed. In particular, devices and techniques for monitoring blood pressure, blood glucose levels, cardiac activity and respiratory activity are discussed; in addition, on-body propagation issues for multiple sensors are presented.

Selectivity for glucose, glucose-6-phosphate, and pyruvate in ternary mixtures from the multivariate analysis of near-infrared spectra

Near-infrared spectroscopy offers the potential for direct in situ analysis in complex biological systems. Chemical selectivity is a critical issue for such measurements given the extent of spectral overlap of overtone and combination spectra. In this work, the chemical basis of selectivity is investigated for a set of multivariate calibration models designed to quantify glucose, glucose-6-phosphate, and pyruvate independently in ternary mixtures. Near-infrared spectra are collected over the combination region (4,000-5,000 cm(-1)) for a set of 60 standard solutions maintained at 37 degrees C. These standard solutions are composed of randomized concentrations (0.5-30 mM) of glucose, glucose-6-phosphate, and pyruvate. Individual calibration models are constructed for each solute by using the partial least-squares (PLS) algorithm with optimized spectral range and number of latent variables. The resulting standard errors are 0.90, 0.72, and 0.32 mM for glucose, glucose-6-phosphate, and pyruvate, respectively. A pure component selectivity analysis (PCSA) demonstrates selectivity for each solute in these ternary samples. The concentration of each solute is also predicted for each sample by using a set of net analyte signal (NAS) calibration models. A comparison of the PLS and NAS calibration vectors demonstrates the chemical basis of selectivity for these multivariate methods. Selectivity of each PLS and NAS calibration model originates from the unique spectral features associated with the targeted analyte. Overall, selectivity is demonstrated for each solute with an order of sensitivity of pyruvate > glucose-6-phosphate > glucose.

On-line near-infrared spectrometer to monitor urea removal in real time during hemodialysis

The ex vivo removal of urea during hemodialysis treatments is monitored in real time with a noninvasive near-infrared spectrometer. The spectrometer uses a temperature-controlled acousto optical tunable filter (AOFT) in conjunction with a thermoelectrically cooled extended wavelength InGaAs detector to provide spectra with a 20 cm(-1) resolution over the combination region (4000-5000 cm(-1)) of the near-infrared spectrum. Spectra are signal averaged over 15 seconds to provide root mean square noise levels of 24 micro-absorbance units for 100% lines generated over the 4600-4500 cm(-1) spectral range. Combination spectra of the spent dialysate stream are collected in real-time as a portion of this stream passes through a sample holder constructed from a 1.1 mm inner diameter tube of Teflon. Real-time spectra are collected during 17 individual dialysis sessions over a period of 10 days. Reference samples were extracted periodically during each session to generate 87 unique samples with corresponding reference concentrations for urea, glucose, lactate, and creatinine. A series of calibration models are generated for urea by using the partial least squares (PLS) algorithm and each model is optimized in terms of number of factors and spectral range. The best calibration model gives a standard error of prediction (SEP) of 0.30 mM based on a random splitting of spectra generated from all 87 reference samples collected across the 17 dialysis sessions. PLS models were also developed by using spectra collected in early sessions to predict urea concentrations from spectra collected in subsequent sessions. SEP values for these prospective models range from 0.37 mM to 0.52 mM. Although higher than when spectra are pooled from all 17 sessions, these prospective SEP values are acceptable for monitoring the hemodialysis process. Selectivity for urea is demonstrated and the selectivity properties of the PLS calibration models are characterized with a pure component selectivity analysis.

Measurement of 2-D SpO2 distribution in skin tissue by multispectral imaging with depth selectivity control

Two-dimensional hemoglobin oxygen saturation measurement is demonstrated by using the combination technique of multispectral imaging and the polarization control. Multispectral images are acquired at the wavelength range from 500 to 680 nm to observe the wavelength-dependent diffusely reflected light from the skin tissue. For eliminating the superficially reflected light from the skin, the skin tissue is illuminated by linearly polarized light and the polarization analyzer whose orientation is perpendicular to the illumination light is inserted in front of an imaging camera. The hemoglobin oxygen saturation levels corresponding to all image pixels are estimated by the partial least squares regression method with respect to each reflection spectrum. Mapping all the estimated values enables the oxygen saturation map across the observed tissue area.

Comparison of multivariate calibration models for glucose, urea, and lactate from near-infrared and Raman spectra

Partial least-squares (PLS) calibration models have been generated from a series of near-infrared (near-IR) and Raman spectra acquired separately from sixty different mixed solutions of glucose, lactate, and urea in aqueous phosphate buffer. Independent PLS models were prepared and compared for glucose, lactate, and urea. Near-IR and Raman spectral features differed substantially for these solutes, with Raman spectra enabling greater distinction with less spectral overlap than features in the near-IR spectra. Despite this, PLS models derived from near-IR spectra outperformed those from Raman spectra. Standard errors of prediction were 0.24, 0.11, and 0.14 mmol L(-1) for glucose, lactate, and urea, respectively, from near-IR spectra and 0.40, 0.42, and 0.36 mmol L(-1) for glucose, lactate, and urea, respectively, from Raman spectra. Differences between instrumental signal-to-noise ratios were responsible for the better performance of the near-IR models. The chemical basis of model selectivity was examined for each model by using a pure component selectivity analysis combined with analysis of the net analyte signal for each solute. This selectivity analysis showed that models based on either near-IR or Raman spectra had excellent selectivity for the targeted analyte. The net analyte signal analysis also revealed that analytical sensitivity was higher for the models generated from near-IR spectra. This is consistent with the lower standard errors of prediction.

Noninvasive near-infrared blood glucose monitoring using a calibration model built by a numerical simulation method: Trial application to patients in an intensive care unit

We have applied a new methodology for noninvasive continuous blood glucose monitoring, proposed in our previous paper, to patients in ICU (intensive care unit), where strict controls of blood glucose levels are required. The new methodology can build calibration models essentially from numerical simulation, while the conventional methodology requires pre-experiments such as sugar tolerance tests, which are impossible to perform on ICU patients in most cases. The in vivo experiments in this study consisted of two stages, the first stage conducted on healthy subjects as preliminary experiments, and the second stage on ICU patients. The prediction performance of the first stage was obtained as a correlation coefficient (r) of 0.71 and standard error of prediction (SEP) of 28.7 mg/dL. Of the 323 total data, 71.5% were in the A zone, 28.5% were in the B zone, and none were in the C, D, and E zones for the Clarke error-grid analysis. The prediction performance of the second stage was obtained as an r of 0.97 and SEP of 27.2 mg/dL. Of the 304 total data, 80.3% were in the A zone, 19.7% were in the B zone, and none were in the C, D, and E zones. These prediction results suggest that the new methodology has the potential to realize a noninvasive blood glucose monitoring system using near-infrared spectroscopy (NIRS) in ICUs. Although the total performance of the present monitoring system has not yet reached a satisfactory level as a stand-alone system, it can be developed as a complementary system to the conventional one used in ICUs for routine blood glucose management, which checks the blood glucose levels of patients every few hours.

Pulse glucometry: A new approach for noninvasive blood glucose measurement using instantaneous differential near-infrared spectrophotometry

We describe a new optical method for noninvasive blood glucose (BGL) measurement. Optical methods are confounded by basal optical properties of tissues, especially water and other biochemical species, and by the very small glucose signal. We address these problems by using fast spectrophotometric analysis in a finger, deriving 100 transmittance spectra per second, to resolve optical spectra (900 to 1700 nm) of blood volume pulsations throughout the cardiac cycle. Difference spectra are calculated from the pulsatile signals, thereby eliminating the effects of bone, other tissues, and nonpulsatile blood. A partial least squares (PLS) model is used with the measured spectral data to predict BGL levels. Using glucose tolerance tests in 27 healthy volunteers, periodic optical measurements were made simultaneously with collection of blood samples for in vitro glucose analysis. Altogether, 603 paired data sets were obtained in all subjects and two-thirds of the data or of the subjects randomly selected were used for the PLS calibration model and the rest for the prediction. Bland-Altman and error-grid analyses of the predicted and measured BGL levels indicated clinically acceptable accuracy. We conclude that the new method, named pulse glucometry, has adequate performance for safe, noninvasive estimation of BGL.

Prediction of glucose in whole blood by near-infrared spectroscopy: influence of wavelength region, preprocessing, and hemoglobin concentration

Measurement accuracy for predicting glucose in whole blood was studied based on near-infrared spectroscopy. Optimal wavelength regions, preprocessing, and the influence of hemoglobin were examined using partial least-squares regression. Spectra between 1100 and 2400 nm were measured from 98 whole blood samples. In order to study the influence of hemoglobin, which is the most dominant component in blood, 98 samples were arranged such that glucose and hemoglobin concentrations were distributed in their physiological ranges. Samples were grouped into three depending on hemoglobin level. The results showed that glucose prediction was influenced by hemoglobin concentrations in the calibration model. It was necessary for samples used in the calibration model to represent the entire range of hemoglobin level. The cross-validation errors were the smallest when the wavelength regions of 1390 to 1888 nm and 2044 to 2393 nm were used. However, prediction accuracy was not very dependent on preprocessing methods in this optimal region. The standard error of glucose prediction was 25.5 mgdL and the coefficient of variation in prediction was 11.2%.

Multispectral polarization imaging for observing blood oxygen saturation in skin tissue

We propose a new technique that combines two-dimensional (2D) multispectral imaging and polarization gating for observing the blood oxygen saturation (SpO2) level in human skin tissue. The spectral decomposition of the skin tissue image provides the principal information on blood oxygenation. The polarization gating selects the measurement depth according to the relative orientation of the two polarizers that are placed on a camera and a light source. The combination of these two methods yields multispectral images of the superficial and deep layers of the skin tissue separately. In order to evaluate the blood oxygen, we focus on the multispectral images of the deep site. The SpO2 levels at each image pixel are calculated by means of the partial least squares regression with respect to each reflectance spectrum. The reassignment of the predicted responses retrieves an image whose pixel values represent the relative SpO2 levels. A demonstration experiment for acquiring the multispectral polarization images is performed in the spectral range of 500 to 680 nm, and the SpO2 distributions are obtained.

New methodology to obtain a calibration model for noninvasive near-infrared blood glucose monitoring

This paper reports new methodology to obtain a calibration model for noninvasive blood glucose monitoring using diffuse reflectance near-infrared (NIR) spectroscopy. Conventional studies of noninvasive blood glucose monitoring with NIR spectroscopy use a calibration model developed by in vivo experimental data sets. In order to create a calibration model, we have used a numerical simulation of light propagation in skin tissue to obtain simulated NIR diffuse reflectance spectra. The numerical simulation method enables us to design parameters affecting the prediction of blood glucose levels and their variation ranges for a data set to create a calibration model using multivariate analysis without any in vivo experiments in advance. By designing the parameters and their variation ranges appropriately, we can prevent a calibration model from chance temporal correlations that are often observed in conventional studies using NIR spectroscopy. The calibration model (regression coefficient vector) obtained by the numerical simulation has a characteristic positive peak at the wavelength around 1600 nm. This characteristic feature of the regression coefficient vector is very similar to those obtained by our previous in vitro and in vivo experimental studies. This positive peak at around 1600 nm also corresponds to the characteristic absorption band of glucose. The present study has reinforced that the characteristic absorbance of glucose at around 1600 nm is useful to predict the blood glucose level by diffuse reflectance NIR spectroscopy. We have validated this new calibration methodology using in vivo experiments. As a result, we obtained a coefficient of determination, r2, of 0.87 and a standard error of prediction (SEP) of 12.3 mg/dL between the predicted blood glucose levels and the reference blood glucose levels for all the experiments we have conducted. These results of in vivo experiments indicate that if the parameters and their vibration ranges are appropriately taken into account in a numerical simulation, the new calibration methodology provides us with a very good calibration model that can predict blood glucose levels with small errors without conducting any experiments in advance to create a calibration model for each individual patient. This new calibration methodology using numerical simulation has promising potential for NIR spectroscopy, especially for noninvasive blood glucose monitoring.

An implantable subcutaneous glucose sensor array in ketosis-prone rats: closed loop glycemic control

A closed loop system of diabetes control would minimize hyperglycemia and hypoglycemia. We therefore implanted and tested a subcutaneous amperometric glucose sensor array in alloxan-diabetic rats. Each array employed four sensing units, the outputs of which were processed in real time to yield a unified signal. We utilized a gain-scheduled insulin control algorithm which rapidly reduced insulin delivery as glucose concentration declined. Such a system was generally effective in controlling glycemia and the degree of lag between blood glucose and the sensor signal was usually 3-8 min. After prolonged implantation, this lag was sometimes longer, which led to impairment of sensor accuracy. Using a prospective two-point calibration method, sensor accuracy and closed loop control were good. A revised algorithm yielded better glycemic control than the initial algorithm did. Future research needs to further improve calibration methods and reduce foreign body fibrosis in order to avoid a time-related increase in lag duration.

Raman spectroscopy for noninvasive glucose measurements

We report the first successful study of the use of Raman spectroscopy for quantitative, noninvasive ("transcutaneous") measurement of blood analytes, using glucose as an example. As an initial evaluation of the ability of Raman spectroscopy to measure glucose transcutaneously, we studied 17 healthy human subjects whose blood glucose levels were elevated over a period of 2-3 h using a standard glucose tolerance test protocol. During the test, 461 Raman spectra were collected transcutaneously along with glucose reference values provided by standard capillary blood analysis. A partial least squares calibration was created from the data from each subject and validated using leave-one-out cross validation. The mean absolute errors for each subject were 7.8%+/-1.8% (mean+/-std) with R2 values of 0.83+/-0.10. We provide spectral evidence that the glucose spectrum is an important part of the calibrations by analysis of the calibration regression vectors.

Non-invasive glucose measurement technologies: an update from 1999 to the dawn of the new millennium

There are three main issues in non-invasive (NI) glucose measurements: namely, specificity, compartmentalization of glucose values, and calibration. There has been progress in the use of near-infrared and mid-infrared spectroscopy. Recently new glucose measurement methods have been developed, exploiting the effect of glucose on erythrocyte scattering, new photoacoustic phenomenon, optical coherence tomography, thermo-optical studies on human skin, Raman spectroscopy studies, fluorescence measurements, and use of photonic crystals. In addition to optical methods, in vivo electrical impedance results have been reported. Some of these methods measure intrinsic properties of glucose; others deal with its effect on tissue or blood properties. Recent studies on skin from individuals with diabetes and its response to stimuli, skin thermo-optical response, peripheral blood flow, and red blood cell rheology in diabetes shed new light on physical and physiological changes resulting from the disease that can affect NI glucose measurements. There have been advances in understanding compartmentalization of glucose values by targeting certain regions of human tissue. Calibration of NI measurements and devices is still an open question. More studies are needed to understand the specific glucose signals and signals that are due to the effect of glucose on blood and tissue properties. These studies should be performed under normal physiological conditions and in the presence of other co-morbidities.

Non-invasive monitoring of gingival crevicular fluid for estimation of blood glucose level

Development of a non-invasive method for measuring the blood glucose level is an urgent necessity, and putting such a method into practical use will enable some of the physical and mental stress that patients with diabetes have to endure to be removed. To realise a non-invasive blood glucose monitor, the gingival crevicular fluid (GCF) was measured. A GCF-collecting device was developed that was designed to be disposable, biocompatible and small enough to be inserted in the gingival crevice for collection of a sub-microlitre sample of GCF. Also, a high-sensitivity glucose testing tape incorporated in the device was developed. Red laser light in a portable optical device measured the colour density of the testing tape. Standard glucose solutions were used to investigate the measurement accuracy of the GCF glucose monitor and showed a correlation coefficient of R = 0.99 (n = 20) between the optical density and the glucose levels. The GCF glucose monitor was evaluated on healthy Swedish and Japanese adults (n = 10) and both GCF glucose levels (GCFLs) and blood glucose levels (BGLs) were measured in conjunction with meal loads. The GCFLs were about 1/10-1/560 lower than the BGLs. No difference in the range of GCFLs between the Swedish and the Japanese subjects was observed. Therefore it was concluded that physique, body mass index and life-style, such as dietary habit, did not significantly influence the GCFLs. Further, the correlation coefficients of all the subjects were 0.70 and 0.88 with each group. It was suggested that GCF could be used as a method of non-invasive blood glucose measurement.

Online Measurement of Urea Concentration in Spent Dialysate during Hemodialysis

Background: We describe online optical measurements of urea in the effluent dialysate line during regular hemodialysis treatment of several patients. Monitoring urea removal can provide valuable information about dialysis efficiency.

Methods: Spectral measurements were performed with a Fourier-transform infrared spectrometer equipped with a flow-through cell. Spectra were recorded across the 5000–4000 cm−1 (2.0–2.5 μm) wavelength range at 1-min intervals. Savitzky–Golay filtering was used to remove baseline variations attributable to the temperature dependence of the water absorption spectrum. Urea concentrations were extracted from the filtered spectra by use of partial least-squares regression and the net analyte signal of urea.

Results: Urea concentrations predicted by partial least-squares regression matched concentrations obtained from standard chemical assays with a root mean square error of 0.30 mmol/L (0.84 mg/dL urea nitrogen) over an observed concentration range of 0–11 mmol/L. The root mean square error obtained with the net analyte signal of urea was 0.43 mmol/L with a calibration based only on a set of pure-component spectra. The error decreased to 0.23 mmol/L when a slope and offset correction were used.

Conclusions: Urea concentrations can be continuously monitored during hemodialysis by near-infrared spectroscopy. Calibrations based on the net analyte signal of urea are particularly appealing because they do not require a training step, as do statistical multivariate calibration procedures such as partial least-squares regression.

Near-infrared spectroscopic measurement of urea in dialysate samples collected during hemodialysis treatments

Single-beam spectra were collected over the combination region of the near-infrared spectrum for 80 samples collected from 15 people over a two-week period. Partial least-squares (PLS) regression was used to generate an optimized calibration model for urea. PLS calibration models accurately measure urea in the spent dialysate matrix. Prediction errors are on the order of 0.15 mM, which is sufficient for the clinical assessment of the dialysis process. In addition, the feasibility of a global calibration model is demonstrated by generating a calibration model from samples and spectra obtained from 12 people to predict the level of urea in samples collected from 3 different people. In this case, the standard error of prediction is 0.09 mM. Spectra were modified in order to systematically examine the impact of resolution and noise. Little impact is observed by altering the spectral resolution from 4 to 32 cm-1. Spectral noise, however, plays an important role in the accuracy of these calibration models. Increasing the magnitude of the spectral noise increases the prediction errors and increases the width of the spectral range necessary for extracting the analytical information. The utility of the method is demonstrated by analyzing dialysate samples collected during actual dialysis treatments. In addition, the necessary resolution and spectral quality necessary for reliable on-line urea monitoring is identified. These findings indicate that a dedicated, on-line urea spectrometer must posses a resolution of 16 cm-1 coupled with a sample thickness of 1.5 mm and spectral noise levels on the order of 25 micro-absorbance units when measured as the root-mean-square (RMS) noise of 100% lines.

In vivo noninvasive measurement of blood glucose by near-infrared diffuse-reflectance spectroscopy

This paper reports in situ noninvasive blood glucose monitoring by use of near-infrared (NIR) diffuse-reflectance spectroscopy. The NIR spectra of the human forearm were measured in vivo by using a pair of source and detector optical fibers separated by a distance of 0.65 mm on the skin surface. This optical geometry enables the selective measurement of dermis tissue spectra due to the skin's optical properties and reduces the interference noise arising from the stratum corneum. Oral glucose intake experiments were performed with six subjects (including a single subject with type I diabetes) whose NIR skin spectra were measured at the forearm. Partial least-squares regression (PLSR) analysis was carried out and calibration equations were obtained with each subject individually. Without exception among the six subjects, the regression coefficient vectors of their calibration models were similar to each other and had a positive peak at around 1600 nm, corresponding to the characteristic absorption peak of glucose. This result indicates that there is every possibility of glucose detection in skin tissue using our measurement system. We also found that there was a good correlation between the optically predicted values and the directly measured values of blood samples with individual subjects. The potential of noninvasive blood glucose monitoring using our methodology was demonstrated by the present study.

Determination of glucose concentration in a scattering medium based on selected wavelengths by use of an overtone absorption band

A method and device for measuring glucose concentration in a scattering medium have been developed. A spectral range of 800-1800 nm is considered for wavelength selection because of its deeper penetration into biological tissue and the presence of a glucose absorption band. An algorithm based on selected wavelengths is proposed to minimize interference from other components. The optimal distance between the light source and the detector for diffuse reflectance measurement minimizes the influence of medium scattering. The proposed algorithm and measuring device are tested with a solution containing milk with added glucose. Glucose concentrations between 0 and 2000 mg/dl are determined with a correlation coefficient of 0.977. We also investigate the influence of concentration variations of other substances such as water, hemoglobin, albumin, and cholesterol when they are mixed in a scattering medium.

Real-time in situ monitoring of freely suspended and immobilized cell cultures based on mid-infrared spectroscopic measurements

Glucose and lactate profiles in Chinese hamster ovary cell cultures were accurately monitored in real time and in situ during three bioreactor batch cultures lasting 11,15, and 15 days performed within a 60-day period. Monitoring was accomplished using in situ-collected mid-infrared spectra analyzed with a priori one-time established partial least-squares regression models. The robustness of the technique was demonstrated by application of these models without modification after 2.3 years. Neither recalibration nor instrument maintenance was required during the 2.3-year period, except for the daily filling of liquid nitrogen for detector cooling during operation. The lactate calibration model yielded accurate absolute concentration estimations during each of the batch cultures with standard errors of estimate from 1 to 3 mM. The a priori-established glucose calibration model yielded concentration estimations with an off-set, which was constant throughout a culture. Adjustment of the off-set before inoculation resulted in accurate concentration estimations with Standard errors of estimate of approximately 1 mM for each of the bioreactor cultures. Sensitivity in detecting differences of 0.5 mM and selectivity against variation of one metabolite while the other was kept constant was demonstrated during standard additions of either glucose or lactate. The sensor system proved to be reliable, simple, accurate, sterile, and capable of long-term automatic operation and is considered to be mature enough to be routinely applied for in situ (on-line) cell culture monitoring.

Nondestructive near-infrared spectroscopic measurement of multiple analytes in undiluted samples of serum-based cell culture media

An adaptive calibration procedure is used to build selective multivariate calibration models for the measurement of glucose, lactate, glutamine, and ammonia in undiluted serum-based cell culture media. This adaptive procedure removes metabolism-induced covariance between these analytes in a series of calibration samples collected during the cultivation of PC-3 human prostate cancer cells. Partial least-squares calibration models are generated from single-beam near-infrared (NIR) spectra collected over the 4800- to 4200-cm(-1) combination spectral range. Calibration models were generated with both the full spectral range and optimized spectral ranges. In both cases, the number of model factors was optimized and model validity was determined by comparing analyte concentrations predicted from a series of independent and unaltered samples that were obtained during a subsequent cultivation of the PC-3 cells. Similar analytical performance was achieved with fewer model factors when the optimized spectral range was used. The lowest standard errors of prediction were 0.82, 0.94, 0.55, and 0.76 mM for glucose, lactate, glutamine, and ammonia, respectively. Different spectral ranges were optimal for each analyte and the optimized spectral range coincided with the distinguishing spectral features of the analyte. The results of this study demonstrate that NIR spectroscopy can be used effectively in the off-line measurement of important nutrients (glucose and glutamine) and byproducts (lactate and ammonia) in a serum-based animal cell culture medium.

Near-Infrared Spectroscopy for Measuring Urea in Hemodialysis Fluids

Background: Near-infrared spectroscopy is proposed as a method for providing real-time urea concentrations during hemodialysis treatments. The feasibility of such noninvasive urea measurements is evaluated in undiluted dialysate fluid.

Methods: Near-infrared spectra were collected from calibration solutions of urea prepared in dialysate fluid. Spectra were collected over three distinct spectral regions, and partial least-squares calibration models were optimized and compared for each. Selectivity for urea was demonstrated with two-component samples composed of urea and glucose in the dialysate matrix. The clinical significance of this approach was assessed by measuring urea in real hemodialysate samples.

Results: Urea absorptions within the combination and short-wavelength, near-infrared spectral regions provided sufficient spectral information for sound calibration models in the dialysate matrix. The combination spectral region had SEs of calibration (SEC) and prediction (SEP) of 0.38 mmol/L and 0.26 mmol/L, respectively, over the 4720–4600 cm−1 spectral range with 5 partial least-square factors. A second calibration model was established over the combination region from a series of solutions prepared with independently variable concentrations of urea and glucose. The best calibration model for urea in the presence of variable glucose concentrations had a SEC of 0.6 mmol/L and a SEP of 0.4 mmol/L for a 5-factor model over the 4600–4350 cm−1 spectral range. There was no significant decrease in SEP when the 4720–4600 cm−1 calibration model was used to measure urea in real samples collected during actual hemodialysis.

Conclusions: Urea can be determined with sufficient sensitivity and selectivity for clinical measurements within the matrix of the hemodialysis fluid.

Measurement of potency and lipids in monensin fermentation broth by near-infrared spectroscopy

A transmission near-infrared (NIR) spectroscopic method for quantification of potency and lipids in monensin fermentation broth was developed and validated. Two multiple linear regression calibration curves were established for a set of 100 fermentation samples, correlating the appropriate absorption bands in the NIR spectrum to the laboratory reference methods; high-performance liquid chromatography for potency, and chloroform extraction for lipids. During method development, potency was found to be well correlated to NIR absorbances specific for monensin. While acceptable, correlation of NIR absorbances characteristic of oil to the chloroform lipid method was weaker due to a greater amount of relative variation in the lipid measurements. Following establishment of the optimal calibration curves, the NIR method for potency and lipids was validated for selectivity, accuracy, precision, and robustness. In order to investigate long-term drift in the measurement system, samples were tested both by the NIR and the reference methods over a 7-month period. The differences between results from the two measurements were calculated and statistically analyzed.

The GlucoWatch biographer: a frequent automatic and noninvasive glucose monitor

The GlucoWatch (Cygnus, Inc, Redwood City, CA, USA) biographer provides automatic, frequent and noninvasive blood glucose measurements for up to 12 h. The device extracts glucose through intact skin where it is measured by an amperometric biosensor. Clinical trials in a variety of environments have shown that the biographer provides accurate and precise glucose measurements when compared with serial fingerstick blood glucose measurements. Mean difference between these measurements was 0.26 mmol/L in the home environment (r = 0.80). Over 94% of biographer readings were in the clinically acceptable A+B region of the Clarke Error Grid. A slight positive bias is observed for the biographer readings at low glucose levels. Biographer precision, as measured by coefficient of variation (CV)%, is approximately 10%. The low glucose alert function of the biographer was able to detect up to 75% of hypoglycaemic episodes with a low false alert level. Skin irritation, characterized by erythema and oedema was either nonexistent or mild in over 87% of subjects and resolved in virtually all subjects without treatment in several days. The GlucoWatch biographer has been shown to be a safe and effective method to track glucose level trends and patterns, which should enable improved glycaemic control for many patients.

Novel control system for blood glucose using a model predictive method

We developed a novel blood glucose control system, using a model predictive method, to achieve optimal control of the blood glucose level in severely diabetic or pancreatectomized patients. This system is designed to predict glucose level changes in advance, considering delayed response time and the administered doses of insulin. This method is also designed to calculate the most appropriate insulin infusion rate by considering differences in individual response to insulin. In this study, we compared our system with a conventional proportional and differential controller (PD controller) to determine whether the new system could regulate the glucose level efficiently in pancreatectomized dogs. The model predictive control method resulted in a significant reduction of mean insulin infusion rate compared with the conventional PD controller (0.71 mU/kg per min vs. 1.81 mU/kg per min, p = 0.0005), when the glucose level in both methods reached the planned target level (100 mg/dl). The new system also tended to have a reduced mean glucose infusion rate for compensating for overshooting of the glucose level compared with the PD controller (0.7 mg/kg per min vs. 1.1 mg/kg per min, p = 0.16). These results indicate that the new system should be a useful tool for regulating the glucose level in severely diabetic patients.

Near-infrared reflectance spectroscopy for noninvasive monitoring of metabolites

An important class of substances in clinical chemistry are metabolites in body fluids, which are accessible by near-infrared spectroscopy without sample treatment using reagentless, fast and readily automated in vitro assays. Furthermore, noninvasive sensing systems are under development for the determination of blood glucose, especially for diabetic patients or for monitoring in intensive care and surgery. Near-infrared diffuse reflectance spectrometry of skin was employed allowing a certain tissue volume to be integrally probed. For calibration, the partial least-squares (PLS) algorithm was used either based on wide spectral intervals or using special spectral variable selection. Capillary blood glucose reference concentrations were obtained by finger pricking and an automated laboratory method (hexokinase/G6P-DH). Clear evidence is provided for the physical effect, as manifested by the spectral glucose absorptivities, underlying the individual single-person calibration models, which still require improvements in the methodology in the normo- and hypoglycemic concentration range. In extending the potential of noninvasive blood assays by infrared spectroscopy, a novel technique is presented for probing the intravascular fluid space by using fast spectral near-infrared measurements of skin tissue. The pulsatile blood spectrum can be derived from reflectance spectra of oral mucosa by Fourier analysis (near-infrared plethysmography). Future applications and prospects for noninvasive blood assays are discussed.

Noninvasive glucose determination by oscillating thermal gradient spectrometry

BACKGROUND

Several noninvasive measurement approaches for the determination of blood glucose levels have been pursued over the past two decades. There is worldwide recognition that an unobtrusive and noninvasive measurement technique will better enable the patient with diabetes to obtain information for appropriate disease management. Many challenges have so far prevented any noninvasive technology from meeting the requirements.

METHOD

In this article, we describe a novel optical technology that when applied to the human body, provides both the sensitivity and the specificity required for acceptance. For human tissue, specific wavelength bands in the mid- infrared (IR) region offer predominantly single component absorbances and thus, provide the basis for the required specificity of an in vivo determination of glucose. It is highly desirable to utilize these bands for the development of a practical spectroscopic technique. The use of mid-IR absorbance bands requires a methodology that accommodates relatively short optical transmission pathlengths. Thermal gradient spectroscopy is one suitable methodology. We describe the utilization of optical phenomena, which arise during a thermal gradient, in the development of a practical instrument. The prototype apparatus is described and results obtained from aqueous samples and tissue phantom studies are presented. Furthermore, a mathematical derivation is presented in the Appendix, that defines the relationship between the optical signals produced and the properties of the tissue under analysis.

Noninvasive blood glucose measurements by near-infrared transmission spectroscopy across human tongues

Noninvasive blood glucose measurements are characterized in human subjects. A series of first overtone transmission spectra are collected across the tongues of five human subjects with type 1 diabetes. The noninvasive human spectra are collected by an experimental protocol that is designed to minimize chance correlations with blood glucose levels. In one treatment of the data, every fifth sample is used as a blind prediction point to validate model performance. In another rearrangement of the data, the spectra collected over the first 29 days are used to build calibration models that are then used to predict in vivo glycemia from spectra collected over the next 10 days. Of the five data sets (one for each subject), one demonstrates a complete inability to predict blood glucose levels and is deemed void of glucose-specific information. Glucose-specific information is evident in the remaining four data sets, albeit to varying degrees. For all data sets, the ability to measure glucose from spectra collected noninvasively from human subjects depends on spectral quality and reproducibility of the tongue-to-spectrometer interface. The standard error of prediction is 3.4 mM for the best calibration model. The significance of this magnitude of prediction error is discussed relative to the situations where: (1) the model is completely void of glucose-specific information and (2) glucose predictions are limited by spectral signal-to-noise and sample thickness. Overall, glucose-specific information is available from noninvasive first-overtone spectra collected across human tongues. Significant improvements are necessary, however, before clinically useful measurements are possible.

Noninvasive Prediction of Glucose by Near-Infrared Diffuse Reflectance Spectroscopy

Background: Self-monitoring of blood glucose by diabetics is crucial in the reduction of complications related to diabetes. Current monitoring techniques are invasive and painful, and discourage regular use. The aim of this study was to demonstrate the use of near-infrared (NIR) diffuse reflectance over the 1050–2450 nm wavelength range for noninvasive monitoring of blood glucose.

Methods: Two approaches were used to develop calibration models for predicting the concentration of blood glucose. In the first approach, seven diabetic subjects were studied over a 35-day period with random collection of NIR spectra. Corresponding blood samples were collected for analyte analysis during the collection of each NIR spectrum. The second approach involved three nondiabetic subjects and the use of oral glucose tolerance tests (OGTTs) over multiple days to cause fluctuations in blood glucose concentrations. Twenty NIR spectra were collected over the 3.5-h test, with 16 corresponding blood specimens taken for analyte analysis.

Results: Statistically valid calibration models were developed on three of the seven diabetic subjects. The mean standard error of prediction through cross-validation was 1.41 mmol/L (25 mg/dL). The results from the OGTT testing of three nondiabetic subjects yielded a mean standard error of calibration of 1.1 mmol/L (20 mg/dL). Validation of the calibration model with an independent test set produced a mean standard error of prediction equivalent to 1.03 mmol/L (19 mg/dL).

Conclusions: These data provide preliminary evidence and allow cautious optimism that NIR diffuse reflectance spectroscopy using the 1050–2450 nm wavelength range can be used to predict blood glucose concentrations noninvasively. Substantial research is still required to validate whether this technology is a viable tool for long-term home diagnostic use by diabetics.