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| A Quantitative Analysis Method for GCB as Rubber Additive by Terahertz Spectroscopy |
| YIN Xian-hua1, 2, WANG Qiang1, 2, MO Wei1, CHEN Tao1, 2*, SONG Ai-guo3 |
1. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
2. Guangxi Key Laboratory of Automatic Detection Technology and Instruments, Guilin 541004, China
3. School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China |
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Abstract Gas carbon black (GCB) is one of the important additives in rubber. Its content has an important influence on the performance of rubber. Nitrile butadiene rubber (NBR) is a synthetic rubber used widely in industrial production. It is important to study the content of GCB in NBR. In this paper, the content of GCB in eight kinds of samples consisted of GCB and NBR with different proportion is detected via terahertz time-domain spectroscopy (THz-TDS). Absorption spectra data of these samples is obtained in the frequency ranging from 0.3 to 1.4 THz. Two quantitative analysis models of GCB are established respectively using partial least squares (PLS) method and support vector regression (SVR) method. The uniform gradient method is used to select the calibration set and the prediction set of two models. The correlation coefficient (r) and the root mean square error (RMSE) of two models were calculated. The r and RMSE for the prediction set of PLS model were 0.985 8 and 2.098 9%. The r and RMSE for the prediction set of SVR model were 0.998 0 and 0.785 4%. Experimental results showed that the predictive result of SVR model was better than that of PLS model. In order to prove the stability of the SVR model, we used the random selection method several times to select its calibration set and prediction set, and got their r and RMSE. The results showed that all the r and RMSE of SVR model are better than that of PLS model, whether the uniform gradient method or the random selection method is used to select the calibration set and the prediction set of the SVR model.
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Received: 2017-11-22
Accepted: 2018-03-28
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Corresponding Authors:
CHEN Tao
E-mail: ct63307@163.com
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