Discrimination of poly(vinyl chloride) samples with different plasticizers and prediction of plasticizer contents in poly(vinyl chloride) using near-infrared spectroscopy and neural-network analysis.
In the recycling of poly(vinyl chloride) (PVC), it is required to discriminate every plasticizer for quality control. For this purpose, the near-infrared spectra were measured for 41 kinds of PVC samples with different plasticizers (DINP, DOP, DOA, TOTM and Polyester) and different plasticizer contents (0-49%). A neural-network analysis was applied to the near-infrared spectra pretreated by second-derivative processing. They were discriminated from one another. The neural-network analysis also allowed us to propose a calibration model which predicts the contents of plasticizers in PVC. The correlation coefficient (R) and the root-mean-square error of prediction (RMSEP) for the DINP calibration model were found to be 0.999 and 0.41 wt%, respectively. In comparison, a partial least-squares regression analysis was carried out. The R and RMSEP of the DINP calibration model were calculated to be 0.993 and 1.27 wt%, respectively. It is found that a near-infrared spectra measurement combined with a neural-network analysis is useful for plastic recycling.
Saeki
Kazumitsu
K
Toyama Industrial Technology Center, 150 Futagami-machi, Takaoka, Toyama 933-0981, Japan.
Funatsu
Kimito
K
Tanabe
Kazutoshi
K
D003247:Conservation of Natural Resources / Q000379:methods; D016571:Neural Networks, Computer; D010968:Plasticizers / Q000032:analysis*; D011143:Polyvinyl Chloride / Q000737:chemistry*; D011786:Quality Control; D013055:Spectrophotometry, Infrared / Q000295:instrumentation / Q000379:methods* / Q000592:standards
D010968:Plasticizers; D011143:Polyvinyl Chloride