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Potential of a Newly Developed High-Speed Near-Infrared (NIR) Camera (Compovision) in Polymer Industrial Analyses: Monitoring Crystallinity and Crystal Evolution of Polylactic Acid (PLA) and Concentration of PLA in PLA/Poly-(R)-3-Hydroxybutyrate (PHB) Blends
Volume 67, Number 12 (Dec. 2013) Page 1441-1446
DAITARO ISHIKAWA, TAKASHI NISHII, FUMIAKI MIZUNO, HARUMI SATO, SERGEI G. KAZARIAN, and YUKIHIRO OZAKI*
This study was carried out to evaluate a new high-speed hyperspectral near-infrared (NIR) camera named Compovision. Quantitative analyses of the crystallinity and crystal evolution of biodegradable polymer, polylactic acid (PLA), and its concentration in PLA/poly-(R)-3-hydroxybutyrate (PHB) blends were investigated using near-infrared (NIR) imaging. This NIR camera can measure two-dimensional NIR spectral data in the 1000–2350 nm region obtaining images with wide field of view of 150 × 250 mm2 (approximately 100 000 pixels) at high speeds (in less than 5 s). PLA with differing crystallinities between 0 and 50% blended samples with PHB in ratios of 80/20, 60/40, 40/60, 20/80, and pure films of 100% PLA and PHB were prepared. Compovision was used to collect respective NIR spectra in the 1000–2350 nm region and investigate the crystallinity of PLA and its concentration in the blends. The partial least squares (PLS) regression models for the crystallinity of PLA were developed using absorbance, second derivative, and standard normal variate (SNV) spectra from the most informative region of the spectra, between 1600 and 2000 nm. The predicted results of PLS models achieved using the absorbance and second derivative spectra were fairly good with a root mean square error (RMSE) of less than 6.1% and a determination of coefficient (R2) of more than 0.88 for PLS factor 1. The results obtained using the SNV spectra yielded the best prediction with the smallest RMSE of 2.93% and the highest R2 of 0.976. Moreover, PLS models developed for estimating the concentration of PLA in the blend polymers using SNV spectra gave good predicted results where the RMSE was 4.94% and R2 was 0.98. The SNV-based models provided the best-predicted results, since it can reduce the effects of the spectral changes induced by the inhomogeneity and the thickness of the samples. Wide area crystal evolution of PLA on a plate where a temperature slope of 70–105 °C had occurred was also monitored using NIR imaging. An SNV-based image gave an obvious contrast of the crystallinity around the crystal growth area according to slight temperature change. Moreover, it clarified the inhomogeneity of crystal evolution over the significant wide area. These results have proved that the newly developed hyperspectral NIR camera, Compovision, can be successfully used to study polymers for industrial processes, such as monitoring the crystallinity of PLA and the different composition of PLA/PHB blends.
Index Headings: High-speed NIR camera; Crystallinity; NIR imaging; Standard normalized variate; Crystal evolution; Polymer blends; Polylactic acid; PLS regression.