The existing calibration algorithms for Raman spectral quantitative analysis

such as principal component regression

partial least squares and support vector machine need a large number of training samples and the regression model may have unsatisfactory generalization ability. Indirect hard modeling (IHM)is a recently introduced method for spectral quantitative analysis

which is used to analyze spectra with highly overlapping band and nonlinear variation. This method can be used to build a calibration model with high generalization ability only by a few training samples. However

all the spectra of pure-components in the mixture is required in IHM method. Obviously

this condition is difficult to achieve in practical applications. To overcome the limitation of IHM

a novel method based on Voigt function for spectral quantitative analysis

i.e.

direct hard modeling (DHM)

was presented. The novel method did not require the pure-component spectra. By DHM algorithm

the characteristic peaks of every pure component in the spectra of mixture could be fixed

and then a linear calibration model could be built based on the areas of unique peaks and the concentration of pure-components. The DHM algorithm was successfully demonstrated while testing on the quantitative analysis of xylene mixture in a PX unit. Experimental results showed that the DHM algorithm had the advantages of robustness and fewer training samples.