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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Fusion of Near-Infrared and Raman Spectroscopy for In-Line Measurement of Component Content of Molten Polymer Blends
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Fusion of Near-Infrared and Raman Spectroscopy for In-Line Measurement of Component Content of Molten Polymer Blends

机译：近红外和拉曼光谱的融合用于熔融聚合物共混物组分含量的在线测量

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Spectral measurement techniques, such as the near-infrared (NIR) and Raman spectroscopy, have been intensively researched. Nevertheless, even today, these techniques are still sparsely applied in industry due to their unpredictable and unstable measurements. This paper put forward two data fusion strategies (low-level and mid-level fusion) for combining the NIR and Raman spectra to generate fusion spectra or fusion characteristics in order to improve the in-line measurement precision of component content of molten polymer blends. Subsequently, the fusion value was applied to modeling. For evaluating the response of different models to data fusion strategy, partial least squares (PLS) regression, artificial neural network (ANN), and extreme learning machine (ELM) were applied to the modeling of four kinds of spectral data (NIR, Raman, low-level fused data, and mid-level fused data). A system simultaneously acquiring in-line NIR and Raman spectra was built, and the polypropylene/polystyrene (PP/PS) blends, which had different grades and covered different compounding percentages of PP, were prepared for use as a case study. The results show that data fusion strategies improve the ANN and ELM model. In particular, mid-level fusion enables the in-line measurement of component content of molten polymer blends to become more accurate and robust.
机译：光谱测量技术，例如近红外（NIR）和拉曼光谱，已经得到了广泛的研究。然而，即使到了今天，由于这些技术的不可预测和不稳定的测量，它们仍然在工业上很少使用。为了提高熔融共混物组分含量的在线测量精度，本文提出了两种数据融合策略（低层和中层融合），将近红外光谱和拉曼光谱结合起来生成融合光谱或融合特性。随后，将融合值应用于建模。为了评估不同模型对数据融合策略的响应，将偏最小二乘（PLS）回归，人工神经网络（ANN）和极限学习机（ELM）用于四种光谱数据（NIR，拉曼，低级融合数据和中级融合数据）。建立了同时获取在线近红外光谱和拉曼光谱的系统，并准备了聚丙烯/聚苯乙烯（PP / PS）共混物，它们具有不同的等级，涵盖了不同的PP的混合百分比，作为案例研究。结果表明，数据融合策略改善了ANN和ELM模型。特别地，中级熔融使得能够在线测量熔融聚合物共混物的组分含量，从而变得更加准确和坚固。

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