| Popis: |
This paper presents an application of near-infrared NIR spectroscopy and multivariate calibration for compositional analysis of complex blends of polycarbonate (PC) and three copolymer components (C1, C2, and C3). Each of the copolymers is composed of 2–3 entities (sub-components) consisting of combinations of butadiene, styrene and acrylonitrile. The concentrations of the PC and three copolymer components were varied using a modified D-optimal design with criteria that minimized the inter-component correlations. To minimize non-chemical spectral variations, the acquired NIR spectra were pre-processed using standard normal variate (SNV and 2nd derivative Savitzky-Golay). Spectral range selection was explored in order to identify which optimal spectral regions were required to generate robust partial least-squares (PLS) models for each component. The optimal calibration models for PC, C1, C2 and C3 exhibited RMSEP values of 0.94%, 0.62%, 0.59% and 0.69% respectively. Using a set of external validation samples, the optimized calibration models for PC, C1, C2 and C3 exhibited bias values of −1.07%, 0.28%, −1.21% and −1.00%, and RMSEP of 2.43%, 1.44%, 1.51%, and 2.05%, respectively. Finally, using a set of 3 samples, the optimized model was successfully transferred to a secondary instrument located in a quality control (QC) laboratory. |
Full Text from ScienceDirect