Analytical Sciences

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

Kazumitsu SAEKI,*  Kimito FUNATSU,** and Kazutoshi TANABE***

*Toyama Industrial Technology Center, 150 Futagami-machi, Takaoka, Toyama 933-0981, Japan
**Department of Knowledge-based Information Engineering, Toyohashi University of Technology, 1-1 Hibarigaoka, Tempaku, Toyohashi, Aichi 441-8580, Japan
***Department of Management Information Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba 275-0016, Japan

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 neuralnetwork 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.

J-STAGE: View this article in J-STAGE