Open AccessDiscrimination 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
Author(s) -
Kazumitsu Saeki,
Kimito Funatsu,
K. Tanabe
Publication year - 2003
Publication title -
analytical sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.392
H-Index - 73
eISSN - 1348-2246
pISSN - 0910-6340
DOI - 10.2116/analsci.19.309
Subject(s) - plasticizer , vinyl chloride , chemistry , polyvinyl chloride , partial least squares regression , calibration , infrared spectroscopy , analytical chemistry (journal) , near infrared spectroscopy , spectroscopy , nuclear chemistry , chromatography , organic chemistry , polymer , optics , mathematics , copolymer , quantum mechanics , statistics , physics
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.