Selective removal of interference signals for near-infrared spectra of biomedical samples by using region orthogonal signal correction

Construction of an efficacious model for a nondestructive identification of traditional Chinese medicines Liuwei Dihuang pills from different manufacturers using near-infrared spectroscopy and moving window partial least-squares discriminant analysis

This paper reports on the construction of an efficacious model for a non-invasive identification of traditional Chinese medicines, Liuwei Dihuang pills from different manufacturers, on the basis of near-infrared spectra (NIRS) coupled with moving window partial least-squares discriminant analysis (MWPLSDA). Considering the continuity of near-infrared spectral measurements, MWPLSDA is used to identify continuous and highly classification-related information intervals, a simple, yet effective classification model that can be developed for identifying accurate 150 Liuwei Dihuang pills from five different manufacturers. Meanwhile, the method is compared with some traditional pattern-recognition methods including principal component analysis (PCA), linear discriminant analysis (LDA) and partial least-squares discriminant analysis (PLSDA). The obtained results show that the method not only can reduce the operation time, but also significantly improves the classification accuracy. Hence, the nondestructive method can be expected to be promising for more practical applications on quality control and the discrimination of traditional Chinese medicine.

Net analyte signal-based simultaneous determination of dyes in environmental samples using moving window partial least squares regression with UV-vis spectroscopy

The multivariate calibration methods-moving window selection partial least squares regression (MWPLSR) and net analyte signal (NAS)-were employed for simultaneous determination of a mixture of C.I. Disperse Blue 183, C.I. Disperse Blue 79, C.I. Disperse Red 82, C.I. Disperse Red 65, C.I. Disperse Yellow 211 and C.I. Disperse Orange 25 by UV-vis spectrophotometry. The absorption spectra of the six disperse dyes were recorded between 320 and 680 nm. A modified changeable size moving window partial least squares (CSMWPLS) and searching combination moving window partial least squares (SCMWPLS) were proposed to search for an optimized spectral interval and an optimized combination of spectral regions from informative regions obtained by MWPLSR. Different wavelength regions were selected by taking into account different spectral parameters including the starting wavelength, the ending wavelength and wavelength interval. It was found that wavelength selection improved the performance of the corresponding net analyte signal-partial least squares (NAS-PLS) model, in terms of root mean square error (RMSE), compared with the results obtained using whole spectra or direct combination of informative regions for each dye. The importance of calibration design was also investigated by calculating the prediction and validation errors. The influence of using independent validation sets were emphasized. The proposed calibration method gave better results in combination and informative spectral regions for determination of the six disperse dyes without prior separation.

Determination of Glucose in Plasma by Dry Film-based Fourier Transformed-Infrared Spectroscopy Coupled with Boosting Support Vector Regression

In the present study, a dry film-based Fourier transformed-infrared (FT-IR) spectroscopic technique, coupled with boosting support vector regression (BSVR), was employed for a blood glucose assay. Potassium thiocyanate (KSCN) was taken in the dry-film method as an internal standard to compensate for any film thickness variation. This technique circumvents interference from water absorption, and requires only 5 microl of a sample. Moving window partial least-squares regression (MWPLSR) was used for wavenumber interval selection before multivariate modeling. By using the BSVR modeling technique, glucose in plasma could be determined over a 0.4 - 20 mmol/l concentration range with satisfactory accuracy. The performance of the BSVR methodology was compared with that of conventional support vector regression (SVR) as well as partial-least squares (PLS). The results demonstrated that BSVR is an effective multivariate calibration tool, providing better performance than conventional PLS and SVR.

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.

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.