School of Chemistry, University of Bristol, Bristol BS8 1TS, UK.
Appl Spectrosc. 2010 Feb;64(2):195-200. doi: 10.1366/000370210790619528.
An algorithm to remove cosmic ray (CR) features from Raman spectra collected in mapping experiments using a charge-coupled device (CCD) is presented. Each spectrum is compared to spectra collected from adjacent points in space using correlation values. The most similar neighbor (MSN) spectrum is selected, offset, and used for identification of CRs. The offset values are defined in terms of the noise level for data with a low signal-to-noise ratio and in terms of the peak height for data with a high signal-to-noise ratio. Scaled intensity values of the MSN spectra are used for replacement of contaminated pixels, allowing for full recovery of underlying spectral features. The algorithm is applicable for any Raman map where the particle sizes within the analyzed mixture are larger than the sampling size or to any other data where the sampling is more frequent than the variation, e.g., time series or temperature profiles. Its application to several maps of pharmaceutical samples is discussed here. With an appropriate offset value for the MSN spectra, no misdetections occur, and all CRs more intense than the offset are removed, which includes the CRs that would have hampered subsequent chemometric analysis by methods such as principal component analysis (PCA).
本文提出了一种从使用电荷耦合器件 (CCD) 采集的映射实验中的拉曼光谱中去除宇宙射线 (CR) 特征的算法。每个光谱都与空间相邻点采集的光谱进行比较，使用相关值。选择最相似的邻居 (MSN) 光谱，对其进行偏移，并用于识别 CR。偏移值是根据低信噪比数据的噪声水平和高信噪比数据的峰值高度来定义的。MSN 光谱的缩放强度值用于替换受污染的像素，从而可以完全恢复潜在的光谱特征。该算法适用于分析混合物中颗粒尺寸大于采样尺寸的任何拉曼图谱，或者适用于采样频率高于变化频率的任何其他数据，例如时间序列或温度分布。本文讨论了其在几个药物样本图谱中的应用。对于 MSN 光谱的适当偏移值，不会出现误检，并且会去除所有强度超过偏移值的 CR，包括可能会干扰后续化学计量学分析（如主成分分析 (PCA)）的 CR。
