Abstract
Raman spectroscopy has been proven to be useful for the component content measurement of polymer blends. However, the soft modeling methods commonly used in quantitative analysis of Raman spectroscopy require a large number of training samples, resulting in a waste of materials and time. This work adopted a modified indirect hard modeling (IHM) method to measure the component content of polymer blends based on Raman spectroscopy. The Raman spectra of polypropylene (PP)/polystyrene (PS) blends with different component content were collected and resolved into the sum of multiple Voigt peak functions. For a large number of peak parameters, the two-dimensional correlation spectroscopy was used to screen out the characteristic Voigt peaks highly correlated with component content to reduce the parameter dimensions and build the parameterized spectral models. The spectral model of the blend was expressed as the weighted sum of the pure component spectral models, during which the parameters of the pure component models were adjusted within a range. According to the relationship between the weight and content of the pure component, a linear regression model for component content prediction was established. The coefficient of determination (R2)/root mean squared error of the IHM component content prediction model was 0.9931/0.4367 wt%. Besides, two popular soft modeling methods, partial least squares and artificial neural network, were compared with the IHM method, which showed that the IHM model had higher prediction accuracy with fewer training samples.
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