The authors investigated the ingredient detection technique of silicone rubber based on the Terahertz spectrum. For this purpose, 18 diverse high-temperature vulcanised silicone rubber (HTVSR) formulations were customised, 8 of which are used as calibration set while the rest 10 as prediction set. Based on the Beer-Lambert Law, the partial-least-square (PLS) regression model and the least-squares support-vector machines (LS-SVM) regression model were used to yield the relationships between the absorption spectrums and the content percentages of polydimethylsiloxane (PDMS), alumina trihydrate (ATH), and silica in HTVSR. The results showed that for the formulations tested, the prediction accuracy of all three main ingredients by the PLS regression model could be improved by changing the spectrum range from 0.2–4 to 0.5–2 THz. If the data were pre-processed by the Savitzky–Golay smoothing method or multiplicative scatter correction method, the prediction accuracy of PDMS could be further enhanced. However, this would lead to a slight decrease in the prediction accuracy of ATH. For the LS-SVM regression model, the radial basis function (RBF) kernel and the linear kernel were studied. It was found that the prediction accuracy of both kernels was better than that of the PLS regression model. With the LS-SVM regression model using the RBF kernel, the correlated coefficients of PDMS and ATH in the prediction set could be up to 0.9915 and 0.9742, respectively.

中文翻译：

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基于太赫兹光谱检测硅橡胶主要成分的研究

作者研究了基于太赫兹光谱的硅橡胶成分检测技术。为此，定制了 18 种不同的高温硫化硅橡胶 (HTVSR) 配方，其中 8 种用作校准集，其余 10 种用作预测集。基于Beer-Lambert定律，采用偏最小二乘（PLS）回归模型和最小二乘支持向量机（LS-SVM）回归模型，得到了吸收光谱与含量百分比之间的关系。 HTVSR 中的聚二甲基硅氧烷 (PDMS)、三水合氧化铝 (ATH) 和二氧化硅。结果表明，对于测试的配方，通过将光谱范围从0.2-4 THz更改为0.5-2 THz，可以提高PLS回归模型对所有三种主要成分的预测精度。如果通过Savitzky-Golay平滑法或乘性散点校正法对数据进行预处理，PDMS的预测精度可以进一步提高。然而，这会导致ATH的预测精度略有下降。对于LS-SVM回归模型，研究了径向基函数(RBF)核和线性核。结果发现，两个核的预测精度均优于PLS回归模型。采用RBF核的LS-SVM回归模型，预测集中PDMS和ATH的相关系数可分别达到0.9915和0.9742。