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Identification of Magnesium Oxychloride Cement Biomaterial Heterogeneity using Raman Chemical Mapping and NIR Hyperspectral Chemical Imaging

机译:使用拉曼化学作图和近红外高光谱化学成像鉴定三氯氧化镁水泥生物材料的异质性

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摘要

The present study investigated spatial heterogeneity in magnesium oxychloride cements within a model of a mould using hyperspectral chemical imaging (HCI). The ability to inspect cements within a mould allows for the assessment of material formation in real time in addition to factors affecting ultimate material formation. Both macro scale NIR HCI and micro scale pixel-wise Raman chemical mapping were employed to characterise the same specimens. NIR imaging is rapid, however spectra are often convoluted through the overlapping of overtone peaks, which can make interpretation difficult. Raman spectra are more easily interpretable, however Raman imaging can suffer from slower acquisition times, particularly when the signal to noise ratio is relatively poor and the spatial resolution is high. To overcome the limitations of both, Raman/NIR data fusion techniques were explored and implemented. Spectra collected using both modalities were co-registered and intra and inter-modality peak correlations were investigated while k-means cluster patterns were compared. In addition, partial least squares regression models, built using NIR spectra, predicted chemical-identifying Raman peaks with an R2 of up to >0.98. As macro scale imaging presented greater data collection speeds, chemical prediction maps were built using NIR HCIs.
机译:本研究使用高光谱化学成像(HCI)技术研究了模具模型中氯氧化镁水泥中的空间异质性。除影响最终材料形成的因素外,检查模具中水泥的能力还可以实时评估材料形成。宏观NIR HCl和微型像素级拉曼化学图谱均用于表征相同的样品。 NIR成像速度很快,但是频谱经常通过泛音峰的重叠而卷积,这可能使解释变得困难。拉曼光谱更容易解释,但是拉曼成像的采集时间会变慢,特别是当信噪比相对较差且空间分辨率较高时。为了克服两者的局限性,探索并实现了拉曼/近红外数据融合技术。使用这两种模态收集的光谱被共配准,并研究了模态内和模态间的峰值相关性,同时比较了k-均值聚类模式。此外,使用近红外光谱建立的偏最小二乘回归模型预测化学鉴定的拉曼峰,R 2 最高> 0.98。由于宏观成像显示出更高的数据收集速度,因此使用近红外HCI建立了化学预测图。

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