Prediction of Ethylene Content in Melt-State Random and Block Polypropylene by Near-Infrared Spectroscopy and Chemometrics: Influence of a Change in Sample Temperature and its Compensation Method

This paper reports on the influence of a change in sample temperature, and a method for its compensation, for the prediction of ethylene (C₂) content in melt-state random polypropylene (RPP) and block polypropylene (BPP) by near-infrared (NIR) spectroscopy and chemometrics. Near-infrared (NIR) spectra of RPP in the melt and solid states were measured by a Fourier transform near-infrared (FT-NIR) on-line monitoring system and an FT-NIR laboratory system. There are some significant differences between the solid- and melt-state RPP spectra. Moreover, we investigated the predicted values of the C₂ content from the RPP or BPP spectra measured at 190 °C and 250 °C using the calibration model for the C₂ content developed using the RPP or BPP spectra measured at 230 °C. The errors in the predicted values of the C₂ content depend on the pretreatment methods for each calibration model. It was found that multiplicative signal correction (MSC) is very effective in compensating for the influence of the change of temperature for the RPP or BPP samples on the predicted C₂ content. From the suggestion of principal component analysis (PCA) and difference spectrum analysis, we propose a new compensation method for the temperature change that uses the difference spectra between two spectra sets measured at different temperatures. We achieved good results using the difference spectra between the RPP/BPP spectra sets measured at 190 °C and 250 °C after correction and the calibration model developed with the spectra measured at 230 °C. The comparison between the method using MSC and the proposed method showed that the predicted error in the latter is slightly better than those in the former.