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Principal Component Analysis Based Interconversion Between Infrared and Near-Infrared Spectra for the Study of Thermal-Induced Weak Interaction Changes of Poly(N-Isopropylacrylamide)

机译:基于主成分分析的红外光谱与近红外光谱之间的转换,用于研究聚(N-异丙基丙烯酰胺)的热致弱相互作用的变化

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摘要

The use of a novel spectral interconversion scheme, principal component analysis (PCA) based spectral prediction, to probe weak molecular interactions of a polymer film is reported. A PCA model is built based on a joint data matrix by concatenating two related spectral data matrices (such as infrared (IR) and near-infrared (NIR) spectra) along the variable direction, then the obtained loading matrix of the model is split into two parts to predict the desired spectra. For a better PCA-based prediction, it is suggested that the samples whose spectra are to be predicted should be as similar as possible to those used in the model. Based on the PCA model, the thermal-induced changes in the weak interaction of poly(N-isopropylacrylamide) (PNiPA) film is revealed by the interconversion between selected spectral ranges measured between 40 and 220 °C. The thermal-induced weak interaction changes of PNiPA, expressed as either the band shift or intensity changes at a specific region, have been probed properly. Meanwhile, the robustness of the spectral prediction is also compared with that achieved by a partial least squares (PLS2) model in detail, illustrating its advantages in predicting more subtle structural changes such as C–H groups.
机译:据报道,使用一种新颖的光谱互转换方案(基于主成分分析(PCA)的光谱预测)来探测聚合物薄膜的弱分子相互作用。通过沿可变方向将两个相关的光谱数据矩阵(例如红外(IR)和近红外(NIR)光谱)连接起来,基于联合数据矩阵构建PCA模型,然后将获得的模型加载矩阵分解为两部分可预测所需的光谱。为了更好地进行基于PCA的预测,建议要预测其光谱的样本应与模型中使用的样本尽可能相似。基于PCA模型,通过在40至220°C之间测量的选定光谱范围之间的相互转换,揭示了聚(N-异丙基丙烯酰胺)(PNiPA)薄膜的弱相互作用的热诱导变化。已正确探测了PNiPA的热诱导弱相互作用变化,该变化表示为特定区域的带移或强度变化。同时,还将光谱预测的鲁棒性与偏最小二乘(PLS2)模型获得的鲁棒性进行了详细比较,说明了其在预测更细微的结构变化(例如CH组)方面的优势。

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    《Applied Spectroscopy》 |2009年第6期|694-699|共6页
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