Near-infrared spectroscopic measurement of physiological glucose levels in variable matrices of protein and triglycerides

Niche preclinical and clinical applications of photoacoustic imaging with endogenous contrast

In the past decade, photoacoustic (PA) imaging has attracted a great deal of popularity as an emergent diagnostic technology owing to its successful demonstration in both preclinical and clinical arenas by various academic and industrial research groups. Such steady growth of PA imaging can mainly be attributed to its salient features, including being non-ionizing, cost-effective, easily deployable, and having sufficient axial, lateral, and temporal resolutions for resolving various tissue characteristics and assessing the therapeutic efficacy. In addition, PA imaging can easily be integrated with the ultrasound imaging systems, the combination of which confers the ability to co-register and cross-reference various features in the structural, functional, and molecular imaging regimes. PA imaging relies on either an endogenous source of contrast (e.g., hemoglobin) or those of an exogenous nature such as nano-sized tunable optical absorbers or dyes that may boost imaging contrast beyond that provided by the endogenous sources. In this review, we discuss the applications of PA imaging with endogenous contrast as they pertain to clinically relevant niches, including tissue characterization, cancer diagnostics/therapies (termed as theranostics), cardiovascular applications, and surgical applications. We believe that PA imaging's role as a facile indicator of several disease-relevant states will continue to expand and evolve as it is adopted by an increasing number of research laboratories and clinics worldwide.

In situ insight into the self-assembly evolution of ABA-type block copolymers in water during the gelation process using infrared spectroscopy and near-infrared spectroscopy

As a kind of thermo-responsive hydrogel, amphiphilic block copolymers are widely investigated. However, the molecular mechanism of their structural change during the gelation process is still limited. Here, a well-controlled triblock copolymer poly(N,N-dimethylacrylamide)-b-poly(diacetone acrylamide)-b-poly(N,N-dimethylacrylamide) (PDMAA-b-PDAAM-b-PDMAA) was synthesized. Its optical microrheology results suggest a gelation temperature range from 42 to 50 °C, showing a transition from viscosity to elasticity. The morphological transition from spheres to worms occurs. Temperature-dependent IR spectra through two-dimensional correlation spectroscopy (2D-COS) and the Gaussian fitting technique were analyzed to obtain the transition information of the molecular structure within the triblock copolymer. The N-way principal component analysis (NPCA) on the temperature-dependent NIR spectra was performed to understand the molecular interaction between water and the copolymer. The intramolecular hydrogen bonds within the hydrophobic PDAAM block tend to dissociate with temperature, resulting in improved hydration and a relative volume increase of the PDAAM block. The dissociation of intermolecular hydrogen bonds within the PDAAM block was the driving force for the morphological transition. Moreover, the hydrophilic PDMAA block dehydrates with temperature, and three stages can be found. The dehydration rate of the second stage with temperature from 42 to 50 °C was obviously higher than those in the lower (first stage) and higher (third stage) temperature ranges.

Quantification of triglyceride levels in fresh human blood by terahertz time-domain spectroscopy

We conducted a pilot clinical study to investigate ex vivo fresh human blood from 93 patients with coronary heart disease (CHD). The results indicated that terahertz (THz) time-domain spectroscopy (TDS) can be used to quantify triglyceride (TG) levels in human blood. Based on the TG concentrations and corresponding THz absorption coefficients, the Pearson correlation analysis demonstrated that the THz absorption coefficients have a significant negative linear correlation with TG concentration. Comparisons between the THz measurements at 0.2 THz and an automatic biochemical analyzer were performed using an additional 20 blood samples, and the results confirmed that the relative error was less than 15%. Our ex vivo human blood study indicates that the THz technique can be used to assess blood TG levels in clinical diagnostic practice.

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.

Near-infrared spectroscopy for medical applications: Current status and future perspectives

The near-infrared radiation (NIR) window, also known as the "optical window" or "therapeutic window", is the range of wavelengths that has the maximum depth of penetration in tissue. Indeed, because NIR is minimally absorbed by water and hemoglobin, spectra readings can be easily collected from the body surface. Recent reports have shown the potential of NIR spectroscopy in various medical applications, including functional analysis of the brain and other tissues, as well as an analytical tool for diagnosing diseases. The broad applicability of NIR spectroscopy facilitates the diagnosis and therapy of diseases as well as elucidating their pathophysiology. This review introduces recent advances and describes new studies in NIR to demonstrate potential clinical applications of NIR spectroscopy.

Using experimental data designs and multivariate modeling to assess the effect of glycated serum protein concentration on glucose prediction from near-infrared spectra of human serum

Near-infrared (NIR) spectra of human blood serum consist of overlapping strong absorption bands of water and serum proteins, which affect the ability of multivariate calibration models to predict glucose. Furthermore, serum proteins such as albumin and globulins undergo a glycation reaction by forming covalent bonds with freely available glucose molecules in the serum. In diabetic individuals with poor glucose control, more and more serum protein molecules react with glucose, resulting in a high glycated protein concentration. The glucose molecules covalently bonded to serum proteins might contribute to the overall glucose signal acquired by NIR spectroscopy. This might affect the prediction ability of multivariate calibration models such as partial least squares regression (PLSR). In this study, we investigated the effect of total protein concentration and the glycated protein concentration in blood serum on the prediction ability of PLSR calibration models. Serum samples were subjected to ultra-filtration, and the PLSR model was built using NIR spectra of filtered serum solutions. Prediction performance was found to improve by 39-42% in absence of serum protein molecules. Various experimental data set designs were generated by carefully varying the glycated serum protein concentration in calibration and test sets of PLSR models. This investigation revealed that the impact of varying glycated protein concentration on the root mean square error of prediction was not drastic. To test the statistical significance of the prediction results, a multiple linear regression model was built. The glycated serum protein concentration was found to be statistically insignificant (p = 0.86) in predicting glucose concentration. Overall, it was concluded that the glycated serum proteins do not affect the glucose prediction accuracy of PLSR models using NIR spectra of human serum.

Improvement of spectral calibration for food analysis through multi-model fusion

Near-infrared (NIR) spectroscopy will present a more promising tool for quantitative analysis if the predictive ability of the calibration model is further improved. To achieve this goal, a new ensemble calibration method based on uninformative variable elimination (UVE)-partial least square (PLS) is proposed, which is named as ensemble PLS (EPLS), meaning a fusion of multiple PLS models. In this method, different calibration sets are first generated by bootstrap and different PLS models are obtained. Then, the UVE is used to shrink the original variable space into a specific subspace. By repeating this process, a fixed number of candidates PLS member models are obtained. Finally, a smaller part of candidate models are integrated to produce an ensemble model. In order to verify the performance of EPLS, three NIR spectral datasets from food industry were used for illustration. Both full-spectrum PLS and UVEPLS of single models were used as reference. It was found that the proposed method could lead to lower RMSEP (root mean square error of prediction) value than PLS and UVEPLS and such an improvement is statistically significant according to a paired t-test. The results showed that the method is of value to enhance the predictive ability of PLS-based calibration involving complex NIR matrices in food analysis.

Note: Multivariate system spectroscopic model using Lorentz oscillators and partial least squares regression analysis

Multivariate system spectroscopic model plays important role in understanding chemometrics of ensemble under study. Here in this manuscript we discuss various approaches of modeling of spectroscopic system and demonstrate how Lorentz oscillator can be used to model any general spectroscopic system. Chemometric studies require customized templates design for the corresponding variants participating in ensemble, which generates the characteristic matrix of the ensemble under study. The typical biological system that resembles human blood tissue consisting of five major constituents i.e., alanine, urea, lactate, glucose, ascorbate; has been tested on the model. The model was validated using three approaches, namely, root mean square error (RMSE) analysis in the range of ±5% confidence interval, clerk gird error plot, and RMSE versus percent noise level study. Also the model was tested across various template sizes (consisting of samples ranging from 10 up to 1000) to ascertain the validity of partial least squares regression. The model has potential in understanding the chemometrics of proteomics pathways.

Digital filtering and model updating methods for improving the robustness of near-infrared multivariate calibrations

Fourier transform near-infrared (NIR) transmission spectra are used for quantitative analysis of glucose for 17 sets of prediction data sampled as much as six months outside the timeframe of the corresponding calibration data. Aqueous samples containing physiological levels of glucose in a matrix of bovine serum albumin and triacetin are used to simulate clinical samples such as blood plasma. Background spectra of a single analyte-free matrix sample acquired during the instrumental warm-up period on the prediction day are used for calibration updating and for determining the optimal frequency response of a preprocessing infinite impulse response time-domain digital filter. By tuning the filter and the calibration model to the specific instrumental response associated with the prediction day, the calibration model is given enhanced ability to operate over time. This methodology is demonstrated in conjunction with partial least squares calibration models built with a spectral range of 4700-4300 cm(-1). By using a subset of the background spectra to evaluate the prediction performance of the updated model, projections can be made regarding the success of subsequent glucose predictions. If a threshold standard error of prediction (SEP) of 1.5 mM is used to establish successful model performance with the glucose samples, the corresponding threshold for the SEP of the background spectra is found to be 1.3 mM. For calibration updating in conjunction with digital filtering, SEP values of all 17 prediction sets collected over 3-178 days displaced from the calibration data are below 1.5 mM. In addition, the diagnostic based on the background spectra correctly assesses the prediction performance in 16 of the 17 cases.

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.

Optical coherence tomography-based continuous noninvasive glucose monitoring in patients with diabetes

OBJECTIVE

The objective of this feasibility study was to test an optical coherence tomography (OCT)-based system for noninvasive, accurate, and continuous monitoring of blood (as opposed to interstitial) glucose concentration in subjects with diabetes. OCT uses low coherence light with precise depth focusing ability to measure changes in the microvasculature structure for glucose detection.

RESEARCH DESIGN AND METHODS

Thirty-three subjects with diabetes had blood glucose concentrations evaluated using simultaneous capillary and OCT-based device (Sentris-100, GlucoLight, Bethlehem, PA) measurements. Subjects received a 50-g carbohydrate load at the start of the study period. Two glucose values with 60 mg separation were used to calibrate the OCT system. Six subjects were excluded (two because of hardware and/or software failures at study initiation, two because of sensor connection problems, and two others because of problems with disposable device positioning on the skin). Of the 27 subjects who completed the study, 12 had type 1 diabetes, and 15 had type 2 diabetes. Statistical analysis included plotting of the Clarke error grid, comparison to International Organization for Standardization (ISO) standards for glucose monitoring systems, and calculation of the Pearson correlation coefficient.

RESULTS

Based on a total of 236 matched points, Clarke error grid analysis indicated 83% in zone A, 16% in zone B, and <1% in zones C and D. ISO standards comparison showed 83% of OCT estimates were within 20% of the reference value for blood glucoses >75 mg/dL. No conclusions can be made about blood glucose estimates in the <75 mg/dL range since no low reference values occurred. The Pearson correlation coefficient comparing OCT versus capillary blood glucose was 0.85 (95% confidence interval 0.81-0.88, P < 0.0001). Mean relative absolute deviation was 11.5%, and median relative absolute deviation was 8.2%.

CONCLUSIONS

This study demonstrated that the OCT system shows acceptable accuracy in the prediction of blood glucose levels in subjects with both type 1 and type 2 diabetes. Future efforts will evaluate the accuracy of the system in the hypoglycemic range.

Blank augmentation protocol for improving the robustness of multivariate calibrations

An updating procedure is described for improving the robustness of multivariate calibration models based on near-infrared spectroscopy. Employing a single blank sample containing no analyte, repeated spectra are acquired during the instrumental warm-up period. These spectra are used to capture the instrumental profile on the analysis day in a way that can be used to update a previously computed calibration model. By augmenting the original spectra of the calibration samples with a group of spectra collected from the blank sample, an updated model can be computed that incorporates any instrumental drift that has occurred. This protocol is evaluated in the context of an analysis of physiological levels of glucose in a simulated biological matrix designed to mimic blood plasma. Employing data of calibration and prediction samples acquired over approximately six months, procedures are studied for implementing the algorithm in conjunction with calibration models based on partial least squares (PLS) regression. Over the range of 1-20 mM glucose, the final algorithm achieves a standard error of prediction (SEP) of 0.79 mM when the augmented PLS model is applied to data collected 176 days after the collection of the calibration spectra. Without updating, the original PLS model produces a seriously degraded SEP of 13.4 mM.

Identification of pure component spectra by independent component analysis in glucose prediction based on mid-infrared spectroscopy

We present a method for glucose prediction from mid-IR spectra by independent component analysis (ICA). This method is able to identify pure, or individual, absorption spectra of constituent components from the mixture spectra without a priori knowledge of the mixture. This method was tested with a two-component system consisting of an aqueous solution of both glucose and sucrose, which exhibit distinct but closely overlapped spectra. ICA combined with principal component analysis was able to identify a spectrum for each component, the correct number of components, and the concentrations of the components in the mixture. This method does not need a calibration process and is advantageous in noninvasive glucose monitoring since expensive and time-consuming clinical tests for data calibration are not required.

Near-infrared spectroscopic measurements of blood analytes using multi-layer perceptron neural networks

Near-infrared (NIR) spectroscopy is being applied to the solution of problems in many areas of biomedical and pharmaceutical research. In this paper we investigate the use of NIR spectroscopy as an analytical tool to quantify concentrations of urea, creatinine, glucose and oxyhemoglobin (HbO2). Measurements have been made in vitro with a portable spectrometer developed in our labs that consists of a two beam interferometer operating in the range of 800-2300 nm. For the data analysis a pattern recognition philosophy was used with a preprocessing stage and a multi-layer perceptron (MLP) neural network for the measurement stage. Results show that the interferogram signatures of the above compounds are sufficiently strong in that spectral range. Measurements of three different concentrations were possible with mean squared error (MSE) of the order of 10(-6).

Experimental and theoretical aspects of glucose measurement using a microcantilever modified by enzyme-containing polyacrylamide

We report a glucose oxidase-containing polyacrylamide hydrogel-coated microcantilever sensor for the measurement of glucose. This enzymatic reaction of glucose results in swelling of the hydrogel due to formation of charged ions (gluconate molecules and protons). The microcantilever undergoes reversible and reproducible bending deflection upon exposure to solutions containing various glucose concentrations due to swelling or shrinking of the hydrogels. The microcantilever deflections increase when the glucose concentrations increase. A theoretical model has been built to correlate volume changes of the gel with microcantilever bending. The calculated data matched with the experimental results very well. Such hydrogel-coated microcantilevers could potentially be used to prepare microcantilever-based chemical and biological sensors when other enzymes are immobilized in the hydrogel.

A new hybrid strategy for constructing a robust calibration model for near-infrared spectral analysis

A new hybrid algorithm is proposed for construction of a high-quality calibration model for near-infrared (NIR) spectra that is robust against both spectral interference (including background and noise) and multiple outliers. The algorithm is a combination of continuous wavelet transform (CWT) and a modified iterative reweighted PLS (mIRPLS) procedure. In the proposed algorithm the spectral interference is filtered by CWT at the first stage then mIRPLS is proposed to detect the multiple outliers in the CWT domain. Compared with the original IRPLS method, mIRPLS does not need to adjust variable parameters to achieve optimum calibration results, which makes it very convenient to perform in practice. The final PLS model is constructed robustly because both the spectral interference and multiple outliers are eliminated. In order to validate the effectiveness and universality of the algorithm, it was applied to two different sets of NIR spectra. The results indicate that the proposed strategy can greatly enhance the robustness and predictive ability of NIR spectral analysis.

Design of a mechanical-tunable filter spectrometer for noninvasive glucose measurement

The development of an accurate and reliable noninvasive near-infrared (NIR) glucose sensor hinges on the success in addressing the sensitivity and the specificity problems associated with the weak glucose signals and the overlapping NIR spectra. Spectroscopic hardware parameters most relevant to noninvasive blood glucose measurement are discussed, which include the optical throughput, integration time, spectral range, and the spectral resolution. We propose a unique spectroscopic system using a continuously rotating interference filter, which produces a signal-to-noise ratio of the order of 10(5) and is estimated to be the minimum required for successful in vivo glucose sensing. Using a classical least-squares algorithm and a spectral range between 2180 and 2312 nm, we extracted clinically relevant glucose concentrations in multicomponent solutions containing bovine serum albumin, triacetin, lactate, and urea.

Specificity of noninvasive blood glucose sensing using optical coherence tomography technique: a pilot study

Noninvasive monitoring of blood glucose concentration in diabetic patients would significantly reduce complications and mortality associated with this disease. In this paper, we experimentally and theoretically studied specificity of noninvasive blood glucose monitoring with the optical coherence tomography (OCT) technique. OCT images and signals were obtained from skin of Yucatan micropigs and New Zealand rabbits. Obtained results demonstrate that: (1) several body osmolytes may change the refractive index mismatch between the interstitial fluid (ISF) and scattering centres in tissue, however the effect of the glucose is approximately one to two orders of magnitude higher; (2) an increase of the ISF glucose concentration in the physiological range (3-30 mM) may decrease the scattering coefficient by 0.22% mM(-1) due to cell volume change; (3) stability of the OCT signal slope is dependent on tissue heterogeneity and motion artefacts; and (4) moderate skin temperature fluctuations (+/- 1 degree C) do not decrease accuracy and specificity of the OCT-based glucose sensor, however substantial skin heating or cooling (several degrees C) significantly change the OCT signal slope. These results suggest that the OCT technique may provide blood glucose concentration monitoring with sufficient specificity under normal physiological conditions.

Noninvasive blood glucose monitoring with optical coherence tomography: a pilot study in human subjects

OBJECTIVE

To study the feasibility of noninvasive blood glucose monitoring using optical coherence tomography (OCT) technique in healthy volunteers.

RESEARCH DESIGN AND METHODS

An OCT system with the wavelength of 1,300 nm was used in 15 healthy subjects in 18 clinical experiments. Standard oral glucose tolerance tests were performed to induce changes in blood glucose concentration. Blood samples were taken from the right arm vein every 5 or 15 min. OCT images were taken every 10-20 s from the left forearm over a total period of 3 h. The slope of the signals was calculated at the depth of 200-600 micro m from the skin surface.

RESULTS

A total of 426 blood samples and 8,437 OCT images and signals were collected and analyzed in these experiments. There was a good correlation between changes in the slope of noninvasively measured OCT signals and blood glucose concentrations throughout the duration of the experiments. The slope of OCT signals changed significantly (up to 2.8% per 10 mg/dl) with variation of plasma glucose values. The good correlation obtained between the OCT signal slope and blood glucose concentration is due to the coherent detection of backscattered photons, which allows measurements of OCT signal from a specific tissue layer without unwanted signal from other tissue layers.

CONCLUSIONS

This pilot study demonstrated the capability of the OCT technique to monitor blood glucose concentration noninvasively in human subjects. Further studies with a larger number of subjects including diabetic subjects are planned to validate these preliminary results.

Noninvasive and minimally-invasive optical monitoring technologies

With recent advancements in micro-fabrication and nano-fabrication techniques as well as advancements in the photonics industry, there is now the potential to develop less invasive portable sensors for monitoring micronutrients and other substances used to assess overall health. There have been many technology innovations in the central laboratory for these substances for overall health status but the primary motivation for the research and development of a portable field instrument has come from a diabetic patient and market-driven desire to minimally invasively or noninvasively monitor glucose concentrations in vivo. Such a sensor system has the potential to significantly improve the quality of life for the estimated 16 million diabetics in this country by making routine glucose measurements less painful and more convenient. In addition, there is a critical need for the development of less invasive portable technologies to assess micronutrient status (iron, vitamin A, iodine and folate), environmental hazards (lead) and for other disease-related substances, such as billirubin for infant jaundice. Currently, over 100 small companies and universities are working to develop improved monitoring devices, primarily for glucose, and optical methods are a big part of these efforts. In this article many of these potentially less invasive and portable optical sensing technologies, which are currently under investigation, will be reviewed including optical absorption spectroscopy, polarimetry, Raman spectroscopy and fluorescence.

Preliminary investigation of near-infrared spectroscopic measurements of urea, creatinine, glucose, protein, and ketone in urine

OBJECTIVE

We investigated the use of near-infrared spectroscopy as an analytical tool to quantify concentrations of urea, creatinine, glucose, ketone, and protein in urine.

DESIGN AND METHODS

FT-IR spectroscopy in conjunction with a polynomial based spectral smoothing method was applied to urine specimens. A partial factorial experimental design was employed to collect spectra using normal and spiked urine samples.

RESULTS

Our results show that the spectral signatures of urea, creatinine, glucose, ketone, and protein in the 1350 to 1800 nm and 2050 to 2375 nm range are sufficiently strong and unique for accurate measurements.

CONCLUSIONS

The accuracy of near infrared for quantifying concentrations of urea and creatinine is only slightly less than our selected reference methods. Glucose, ketone and protein are sufficiently accurate to be useful as a screening tool for wellness. The method successfully accounts for biologic matrix variation. The advantages of near-infrared analysis are (1) no reagents, (2) ease of sample preparation, (3) speed, and (4) the ability to quantify multiple analytes with one spectra.

Data preprocessing and partial least squares regression analysis for reagentless determination of hemoglobin concentrations using conventional and total transmission spectroscopy

Visible-near infrared spectroscopy was successfully used for the determination of total hemoglobin concentration in whole blood. Absorption spectra of whole blood samples, whose hemoglobin concentrations ranged between 6.6 and 17.2 g/dL, were measured from 500 to 800 nm. Two different types of transmission were measured: conventional transmission spectroscopy which collected primarily collimated radiation transmitted through the sample, and total transmission spectroscopy which used an integrating sphere to collect all scattered light as well. Different preprocessing techniques in conjunction with a partial least squares regression calibration model to predict hemoglobin concentrations were applied to the above two types of transmission. Depending on different preprocessing methods, the standard error of predictions ranged from 0.37 to 2.67 g/dL. Mean centering gave the most accurate prediction in our particular data set. Preprocessing methods designed for compensation of the scattering effect produced the worst results contrary to expectations. For univariate analysis, better prediction was achieved by total transmission measurement than by conventional transmission measurement. No significant difference was observed for multivariate analysis on the other hand. Careful selection of the data preprocessing methods and of the multivariate statistical model is required for reagentless determination of hemoglobin concentration in whole blood.

Optical properties of human skin in the near infrared wavelength range of 1000 to 2200 nm

In this paper we present the absorption coefficient mu(a) and the isotropic scattering coefficient mu(s)(') for 22 human skin samples measured using a double integrating sphere apparatus in the wavelength range of 1000-2200 nm. These in vitro results show that values for mua) follow 70% of the absorption coefficient of water and values for mu(s)(') range from 3 to 16 cm(-1). From the measured optical properties, it was found that a 2% Intralipid solution provides a suitable skin tissue phantom.

Optical glucose sensing in biological fluids: an overview

Recent technological advancements in the photonics industry have led to a resurgence of interest in optical glucose sensing and to realistic progress toward the development of an optical glucose sensor. Such a sensor has the potential to significantly improve the quality of life for the estimated 16 million diabetics in this country by making routine glucose measurements more convenient. Currently over 100 small companies and universities are working to develop noninvasive or minimally invasive glucose sensing technologies, and optical methods play a large role in these efforts. This article reviews many of the recent advances in optical glucose sensing including optical absorption spectroscopy, polarimetry, Raman spectroscopy, and fluorescent glucose sensing. In addition a review of calibration and data processing methods useful for optical techniques is presented.

Determination of Glucose in Dried Serum Samples by Fourier-Transform Infrared Spectroscopy

Background: Practical improvements are needed to allow measurement of glucose concentrations by Fourier- transform infrared (FT-IR) spectroscopy. We developed a new method that allows determination of the glucose concentration in dried sera.

Methods: We studied 32 serum samples after fourfold dilution and desiccation before FT-IR analyses on a spectrometer operated at a resolution of 2.0 cm−1. We integrated all spectral windows at the surface of the spectrum in the C—O region. For comparison, glucose was measured in the sera by a glucose oxidase method.

Results: One peak within the spectrum was most specific for glucose (997–1062 cm−1). Its surface integration showed a strong relationship with reference data (r = 0.998; P &lt;0.001). FT-IR analyses of five glucose solutions were performed to determine its specific absorption at the same peak. In this way, glucose concentrations in serum spectra could be measured. For the first time while using FT-IR spectroscopy, no manipulation of spectra nor use of internal standard was necessary to obtain results in high accordance with glucose concentration measured by a conventional (glucose-oxidase) method (Sy|x = 0.25 mmol/L; r = 0.998).

Conclusions: FT-IR spectroscopy appears to be an easy and accurate method to determine glucose concentration and could be widely used to simultaneously identify and quantify several metabolites in biological fluids or tissues.

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.

Spectroscopic and Clinical Aspects of Noninvasive Glucose Measurements

Frequent determination of glucose concentrations in diabetic patients is an important tool for diabetes management. This requires repetitive lancing and finger bleeding. Use of noninvasive (NI) detection techniques offers several advantages, such as the absence of pain and exposure to sharp objects and biohazard materials, the potential for increased frequency of testing, and hence, tighter control of the glucose concentrations, and the potential for a closed-loop system including a monitor and an insulin pump. These potential advantages have led to considerable interest in the commercialization of NI glucose monitoring devices. Review of the scientific, patent, and commercial literature indicates that the spectroscopic basis for NI determination of glucose is not yet well established, and attempts at commercialization may be several steps ahead of our understanding the origin and characteristics of an in vivo glucose-specific or glucose-related signal. Several technologies have potential for leading to viable measuring devices, but most of the data are based on in vitro experimentation. Because of the technical complexity of in vivo glucose measurements, this review aims at discussing the gap between the established need and current technology limitations.