Fumiaki Mizuno

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:

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 (R 2 ) 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 R 2 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 R 2 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.

Development of a High-Speed Monitoring near Infrared Hyperspectral Camera (Compovision) for Wide Area Imaging and its Applications

The demand for high-speed, wide-area near infrared imaging I t is well-known that near infrared (NIR) imaging technology and the methodology associated with it provide insightful evaluations about the surface homogeneity, morphology and component distribution of various industrial materials such as pharmaceuticals, polymers, biological materials like biomedical samples and agricultural products. In particular, NIR spectroscopy is suitable for non-destructive and in situ analysis of bulk materials with the added bonus of physical stability making it resistant to environmental impact. Hence, it is often more robust compared to some other vibrational spectroscopic techniques, such as Raman and infrared (IR) spectroscopy. With regard to the methodology for the mapping and monitoring of various products, procedures applied depend strongly upon the performance of the instrument used to implement the task. Although positive features such as high sensitivity, high wavelength and spatial resolution are essential qualifications, issues concerning high-speed measurements and portability are also primary concerns in the development of the instrument. In the past decade, many research groups have developed novel NIR imaging devices. In particular, a highly portable NIR imaging device for practical pharmaceutical process monitoring has been provided by our group. Our research group has also developed a novel NIR imaging device, called Compovision, (Sumitomo Electric Industries Ltd, Figure 1) for rapid widearea monitoring. The novel features of Compovision include high-speed, wide-area monitoring over a broad NIR wavelength region, enabled by a newlydeveloped indium gallium arsenic (InGaAs) detector. A linear moveable stage, the primary characteristic of Compovision, rapidly measures two-dimensional spectra. Thus, NIR data in the 1000–2350 nm region of a 150 mm × 200 mm area (approximately 100,000 pixels) can be measured within 2–5 s. In most cases, the measurement area obtained by commercial NIR imaging devices is approximately 10 × 10 mm and requires much longer measurement times. From the utility of these aforementioned benefits, Compovision is a potentially powerful instrument for industrial process monitoring. In general, although the quantitative accuracy of sample spectra obtained by high-speed NIR devices such as Compovision is good, the relatively low spectral resolution could prove to be a major constraint for the overall performance of these devices. Nevertheless, a wide-area NIR image that can be rapidly obtained is very attractive for practical applications in process monitoring. Therefore, quantitative methods combined with appropriate chemometric techniques must be employed to extract practical information from NIR data and thus improve results. Multiple linear regression (MLR), principal component regression (PCR) and partial least squares regression (PLSR) are generally used for doi: 10.1255/nirn.1376