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.