Katsuhiko Maruo

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 skins 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.

Noninvasive blood glucose assay using a newly developed near-infrared system

This paper reports in vivo near-infrared (NIR) noninvasive blood glucose assay using dermis tissue spectra. We assume that the glucose content in dermis tissue traces the variations in blood glucose. For dermis spectra measurements, epidermis, especially stratum corneum, acts as an interference in skin tissue. Thus, we have developed a method for the selective measurement of dermis tissue spectra, enabling us to obtain better quality spectra for an accurate blood glucose assay. The selective measurement of the dermis spectra realized by using a newly developed fiber-optic probe that consists of source and detector optical fibers separated by 0.65 mm on a skin surface. The light path in the skin tissue for this geometry has been simulated by a Monte Carlo method. The simulation results show that detected light mainly interrogates dermis tissue. As the absorbance signal of glucose in human tissue is extremely small, the quality of the measured spectra is critical for the reliable assay. The present method for blood glucose assay has been applied to one Type 1 diabetic. The correlation coefficient between the blood glucose content predicted by NIR spectra and those measured by finger-prick was 0.928 and the standard error of prediction was 32.2 mg/dL. These results demonstrate the potential of our methodology for noninvasive NIR blood glucose assay.

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.

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.

Preliminary evaluation of optical glucose sensing in red cell concentrations using near-infrared diffuse-reflectance spectroscopy

Bacterial contamination of blood products is one of the most frequent infectious complications of transfusion. Since glucose levels in blood supplies decrease as bacteria proliferate, it should be possible to detect the presence of bacterial contamination by measuring the glucose concentrations in the blood components. Hence this study is aimed to serve as a preliminary study for the nondestructive measurement of glucose level in transfusion blood. The glucose concentrations in red blood cell (RBC) samples were predicted using near-infrared diffuse-reflectance spectroscopy in the 1350 to 1850 nm wavelength region. Furthermore, the effects of donor, hematocrit level, and temperature variations among the RBC samples were observed. Results showed that the prediction performance of a dataset which contained samples that differed in all three parameters had a standard error of 29.3 mg/dL. Multiplicative scatter correction (MSC) preprocessing method was also found to be effective in minimizing the variations in scattering patterns created by various sample properties. The results suggest that the diffuse-reflectance spectroscopy may provide another avenue for the detection of bacterial contamination in red cell concentrations (RCC) products.

Noninvasive blood glucose monitoring by novel optical-fiber probe

For monitoring the blood glucose level noninvasively from the skin spectra, improvement of signal to noise ratio (S/N) of the glucose signal is critical. This cannot be achieved by the reduction of instrumental noise alone. To reduce the interference from undesired optical signals arising from the stratum corneum and the subcutaneous tissue, we designed a novel optical fiber probe for the skin spectra. The probe consisted of one central optical fiber around which several optical fibers were arranged in circle. The separation of the central optical fiber from each of the surrounding fibers was set at less than 1 mm. This probe was attached to the skin surface vertically when spectral measurements were performed. The measuring light was shone onto the skin surface through the circle fibers and scattered light reaching the central detecting fiber was collected and transmitted to the detection system. The true light path is not defined at present, but light passing through a long path could be neglected with this geometry. When we choose an adequate fiber distance, we can measure the dermis spectra selectively. Glucose intake experiments were performed with volunteers, for whom near-infrared (NIR) spectra were measured at the forearm, from which the blood glucose level was calculated. Partial least square regression (PLSR) analysis was carried out and we found good correlation between the optically estimated values of the glucose level and directly measured values of blood samples. The correlation coefficient characteristic had a positive peak at around 1600 nm, a typical of the glucose spectrum. In conclusion, our system using the novel optical fiber probe detected the changes in the glucose in the human skin tissue quantitatively and noninvasively.

Multi-Objective Genetic Algorithm-Based Sample Selection for Partial Least Squares Model Building with Applications to Near-Infrared Spectroscopic Data:

In this study, multi-objective genetic algorithms (GAs) are introduced to partial least squares (PLS) model building. This method aims to improve the performance and robustness of the PLS model by removing samples with systematic errors, including outliers, from the original data. Multi-objective GA optimizes the combination of these samples to be removed. Training and validation sets were used to reduce the undesirable effects of over-fitting on the training set by multi-objective GA. The reduction of the over-fitting leads to accurate and robust PLS models. To clearly visualize the factors of the systematic errors, an index defined with the original PLS model and a specific Pareto-optimal solution is also introduced. This method is applied to three kinds of near-infrared (NIR) spectra to build PLS models. The results demonstrate that multi-objective GA significantly improves the performance of the PLS models. They also show that the sample selection by multi-objective GA enhances the ability of the PLS models to detect samples with systematic errors.

Moving window cross validation: a new cross validation method for the selection of a rational number of components in a partial least squares calibration model

A new cross validation method called moving window cross validation (MWCV) is proposed in this study, as a novel method for selecting the rational number of components for building an efficient calibration model in analytical chemistry. This method works with an innovative pattern to split a validation set by a number of given windows that move synchronously along proper subsets of all the samples. Calculations for the mean value of all mean squares error in cross validations (MSECVs) for all splitting forms are made for different numbers of components, and then the optimal number of components for the model can be selected. Performance of MWCV is compared with that of two cross validation methods, leave-one-out cross validation (LOOCV) and Monte Carlo cross validation (MCCV), for partial least squares (PLS) models developed on one simulated data set and two real near-infrared (NIR) spectral data sets. The results reveal that MWCV can avoid a tendency to over-fit the data. Selection of the optimal number of components can be easily made by MWCV because it yields a global minimum in root MSECV at the optimal number of components. Changes in the window size and window number of MWCV do not greatly influence the selection of the number of components. MWCV is demonstrated to be an effective, simple and accurate cross validation method.

Influence of Blood Glucose Level on the Scattering Coefficient of the Skin in Near-Infrared Spectroscopy

Various non-invasive glucose monitoring methods using near-infrared spectroscopy have been investigated although no method has been successful so far. Our previous study has proposed a new promising method utilizing numerically generated absorbance spectra instead of the experimentally acquired absorbance spectra. The method suggests that the correct estimation of the optical properties is very important for numerically generating the absorbance spectra. The purpose of this study is to measure the change in the optical properties of the skin with the change in the blood glucose level in vivo. By measuring the reflectances of light incident on the skin surface at two distances from the incident point, the optical properties of the skin can be estimated. The estimation is a kind of the inverse problem based on the simulation of light propagation in the skin. Phantom experiments have verified the method and in vivo experiments are to be performed.Copyright