An optical method recently proposed for non-invasive in vivo blood glucose concentration (BGL) measurement, named "pulse glucometry", was implemented with three multivariate regression methods, Principal Component Regression (PCR), Partial Least Squares Regression (PLS) and Support Vector Machines Regression (SVMsR), as well as with a classification method, Support Vector Machines Classification (SVMsC), for carrying out calibration. A very fast spectrophotometer provided instantaneously and simultaneously the total transmitted radiation spectrum (Iλ) and the cardiac-related pulsatile component (ΔIIλ) superimposed on IIλ in human fingertips over a wavelength range from 900 to 1700 nm with resolution of 8 nm in 100 Hz sampling. From a family of IIλs including information relating to various BGL values, differential optical densities (ΔODIλs (=log(1+ΔIIλ/IIλ)) were calculated and normalized by theΔODIλ values at 1100 nm. Finally, the 2nd derivatives of the normalized ΔODIλs served as regressors. Subsequently, calibration models from regressors and regressands (the corresponding measured BGL or classified BGL values) were constructed with PCR, PLS, SVMsR and SVMsC. Each regression model showed a relatively good result by evaluating a 5-fold cross validation using total 100 data-sets: Clarke error grid analysis indicated a good correlation in each model compared with the measured BGL values, and the SVMsR calibration provided the best plot distributions. Good regression models were obtained by these three methods. This study suggests that "pulse glucometry" can produce clinically acceptable results when implemented with regression and classification type calibrations, and, through rapid BGL assessment, promises to offer a more practical, easier and more convenient way for diabetes screening and health care in normal subjects than is possible with existing methods.