Sammon's mapping regression for the quantitative analysis of glucose from both mid infrared and near infrared spectra.

This paper proposes a novel regression method based on Sammon's mapping dimensionality reduction technique for the quantification of glucose from both near infrared and mid infrared spectra. The proposed regression model was validated to determine the concentration of glucose from the spectra of aqueous mixtures consisting of human serum albumin and glucose in phosphate buffer solution from both near infrared (NIR) and mid infrared (MIR) regions. The performance of the proposed prediction model has been analysed with traditional regression methods principal component regression (PCR) and partial least squares regression (PLSR) models. The results indicate that the proposed model yields improved prediction performance compared to PCR and PLSR methods. In detail, the proposed Sammon's mapping regression (SMR) model provides better prediction ability by reducing the root mean square error of prediction (RMSEP) from 35.74 mg dL-1 for PCR and 31.39 mg dL-1 for PLSR to 21.89 mg dL-1 for the proposed regression model in the MIR region and the RMSEP has been reduced from 38.15 mg dL-1 for the PCR model and 37.5 mg dL-1 for the PLSR model to 29.74 mg dL-1 for the SMR model in the NIR region.

Hilbert Huang Transformation with filtering as a pre-treatment method for the quantitative study of glucose in near infrared spectra.

In this paper, a novel pre-treatment technique Hilbert Huang Transformation with filtering (HHTF) that is coupling of the Hilbert Huang Transformation and the digital filtering is proposed for the measurement of glucose from near infrared spectroscopy. HHTF comprises of the Empirical Mode Decomposition (EMD) and the Hilbert Spectral Analysis. In Hilbert spectral analysis, Butterworth filtering was used to eliminate the noise present in the Intrinsic Mode Functions (IMFs). The traditional Partial Least squares Regression (PLSR) has been used as the regression method. The proposed HHTF with the PLSR method has been assessed to determine the concentration of glucose from near infrared spectra of two distinct compositions that are prepared by mixing triacetin, urea and glucose in a phosphate buffer solution (PBS) and another composition of glucose and human serum albumin in a PBS. The efficiency of the proposed method has been compared with the standard normal variate and the 1st derivative preprocessing methods and is shown to outperform both.

Coupling Scatter Correction with bandpass filtering for preprocessing in the quantitative analysis of glucose from near infrared spectra.

This paper proposes a novel pre-processing method based on combining bandpass filtering with scatter correction techniques Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) to enhance the prediction capability of the linear regression models Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR) in near infrared (NIR) spectroscopy. The method is implemented into a calibration model, evaluated and then validated for the prediction of the glucose concentration from NIR spectra of an aqueous mixture of human serum albumin and glucose in a solution of distilled water and phosphate buffer. The results obtained demonstrate improved prediction performance for both PCR and PLSR. Compared to the efficient feature weighting pre-processing (RRelief), the proposed method is shown to yield better prediction reducing the Root Mean Square Error Prediction RMSEP.

Savitzky-Golay coupled with digital bandpass filtering as a pre-processing technique in the quantitative analysis of glucose from near infrared spectra.

This paper proposes a novel pre-processing method based on combining bandpass with Savitzky-Golay filtering to further improve the prediction performance of the linear calibration models Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR) in near infrared spectroscopy. The proposed method is compared to the highly efficient RReliefF pre-processing technique for further evaluation. The developed calibration models have been validated to predict the glucose concentration from near infrared spectra of a mixture of glucose and human serum albumin in a phosphate buffer solution. The results show that the proposed technique improves the prediction performance of both the PCR and PLSR models and achieve better results than the RReliefF technique.

Improving the prediction performance of PLSR using RReliefF and FSD for the quantitative analysis of glucose in Near Infrared spectra.

This paper proposes a novel pre-processing method, Fourier Self Deconvoluted RReliefF (FSDR) that is based on combining Fourier Self Deconvolution (FSD) with the Regressional Relief-F (RReliefF) processing to improve the prediction performance of the Partial Least Squares Regression (PLSR) model in Near Infrared (NIR) spectroscopy. The FSD is used to eliminate both the baseline variations and high frequency noise from the raw spectra and the RReliefF is applied as a feature weighting algorithm. The proposed FSDR-PLSR technique is validated for the determination of glucose from NIR spectra of a mixture composed of triacetin, urea and glucose in a phosphate buffer solution where the individual component concentrations are selected to be within their physiological range in blood. The results obtained confirm that the proposed pre-processing technique improved the prediction performance of the PLSR model.