Prediction of Polyethylene Density by Near-Infrared Spectroscopy Combined with Neural Network Analysis

Abstract

A rapid and intact method has been developed for predicting polyethylene density by near-infrared spectroscopy combined with neural network analysis. Near-infrared spectra in the region of 1.1-2.2 μm wavelength were measured using pellets or powders of twenty-three kinds of polyethylene (PE) with different densities (0.898-0.962 g cm-3). The spectra were used for training a back-propagation neural network after normalized and second-derivative treatments to predict PE density. Although only a small number of spectral data were used for training, a leave-one-out test of neural network analysis has demonstrated good results. In comparison, principal component regression (PCR) analysis and partial least-squares (PLS) regression analysis were applied. The correlation coefficients (R) were calculated to be 1.000, 0.968 and 0.983 for neural network, PCR and PLS analysis, respectively. The root mean square errors of prediction were found to be 0.00026, 0.0043 and 0.0031 g cm-3, respectively. It is found that near-infrared spectroscopy combined with neural network analysis is useful for the efficient and accurate determination of PE density.