Improvement of the Partial Least Squares Model Performance for Oral Glucose Intake Experiments by Inside Mean Centering and Inside Multiplicative Signal Correction

CHEMOMETRIC ANALYSIS OF DIFFUSE REFLECTANCE SPECTRAL DATA USING SINGULAR VALUE DECOMPOSITION FOR BLOOD GLUCOSE DETECTION

Diabetes mellitus is a metabolic disorder that affects the production or usage of insulin by the body. Diabetes prevails in the body as a long-term condition which causes several other disorders if left unnoticed. Proper control of Diabetes needs continuous monitoring. The current measurement technique is invasive in nature and requires the withdrawal of blood from the body. Periodic quantification of blood glucose leads to pain and discomfort for the subject. This paper presents a non-invasive glucose measuring system using near-infrared diffuse reflectance spectroscopy (DRS). This work attempts to determine the blood glucose value from the diffuse reflected spectra in the NIR region. The study is executed with the spectral signatures of 33 diabetic subjects collected non-invasively using diffuse reflectance spectrometer from a diabetic centre. Blood glucose level of the same subjects are also recorded using the clinical method. The spectral information is subjected to standard normal variate (SNV) preprocessing method to remove baseline drift and then dimension reduction using singular value decomposition (SVD) is applied to the preprocessed data. The extracted singular values when compared with the clinically measured blood glucose is found to have a proportional relationship. The proposed study using singular value decomposition paves us the way for estimating the blood glucose value non-invasively with the obtained set of clinical blood glucose and the corresponding singular value table as a standard reference set.

Calibration maintenance and transfer using Tikhonov regularization approaches

Maintaining multivariate calibrations is essential and involves keeping models developed on an instrument applicable to predicting new samples over time. Sometimes a primary instrument model is needed to predict samples measured on secondary instruments. This situation is referred to as calibration transfer. This paper reports on using a Tikhonov regularization (TR) based method in both cases. A distinction of the TR design for calibration maintenance and transfer is a defined weighting scheme for a small set of new (transfer or standardization) samples augmented to the full set of calibration samples. Because straight application of basic TR theory is not always possible with calibration maintenance and transfer, this paper develops a generic solution to always enable application of TR. Harmonious (bias/variance tradeoff) and parsimonious (effective rank) considerations for TR are compared with the same TR format applied to partial least squares (PLS), showing that both approaches are viable solutions to the calibration maintenance and transfer problems.

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.