Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose.

ABSTRACT: Sample-to-sample variability has proven to be a major challenge in achieving calibration transfer in quantitative biological Raman spectroscopy. Multiple morphological and optical parameters, such as tissue absorption and scattering, physiological glucose dynamics and skin heterogeneity, vary significantly in a human population introducing nonanalyte specific features into the calibration model. In this paper, we show that fluctuations of such parameters in human subjects introduce curved (nonlinear) effects in the relationship between the concentrations of the analyte of interest and the mixture Raman spectra. To account for these curved effects, we propose the use of support vector machines (SVM) as a nonlinear regression method over conventional linear regression techniques such as part

SUBMITTER: Barman I 

PROVIDER: S-EPMC3057474 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature