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首页> 外文期刊>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
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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

机译：用近红外光谱和神经网络分析法区分不同增塑剂的聚氯乙烯样品并预测聚氯乙烯中的增塑剂含量

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摘要

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.
机译：在回收聚氯乙烯（PVC）中，需要区分每种增塑剂以进行质量控制。为此，对具有不同增塑剂（DINP，DOP，DOA，TOTM和聚酯）和不同增塑剂含量（0-49％）的41种PVC样品测量了近红外光谱。将神经网络分析应用于通过二阶导数处理预处理的近红外光谱。他们彼此歧视。神经网络分析还允许我们提出一个校准模型，该模型可以预测PVC中增塑剂的含量。发现DINP校准模型的相关系数（R）和预测的均方根误差（RMSEP）分别为0.999和0.41 wt％。相比之下，进行了偏最小二乘回归分析。计算得出DINP校准模型的R和RMSEP分别为0.993和1.27 wt％。已经发现，将近红外光谱测量与神经网络分析相结合可用于塑料回收。

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