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

Abstract

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.