Li Boyan, Calvet Amandine, Casamayou-Boucau Yannick, Morris Cheryl, Ryder Alan G
Nanoscale Biophotonics Laboratory, School of Chemistry, National University of Ireland, Galway, Galway, Ireland.
Anal Chem. 2015 Mar 17;87(6):3419-28. doi: 10.1021/ac504776m. Epub 2015 Mar 4.
A robust and accurate analytical methodology for low-content (<0.1%) quantification in the solid-state using Raman spectroscopy, subsampling, and chemometrics was demonstrated using a piracetam-proline model. The method involved a 5-step process: collection of a relatively large number of spectra (8410) from each sample by Raman mapping, meticulous data pretreatment to remove spectral artifacts, use of a 0-100% concentration range partial least-squares (PLS) regression model to estimate concentration at each pixel, use of a more accurate, reduced concentration range PLS model to calculate analyte concentration at each pixel, and finally statistical analysis of all 8000+ concentration predictions to produce an accurate overall sample concentration. The relative prediction accuracy was ∼2.4% for a 0.05-1.0% concentration range, and the limit of detection was comparable to high performance liquid chromatography (0.03% versus 0.041%). For data pretreatment, we developed a unique cosmic ray removal method and used an automated baseline correction method, neither of which required subjective user intervention and thus were fully automatable. The method is applicable to systems which cannot be easily analyzed chromatographically, such as hydrate, polymorph, or solvate contamination.
使用吡拉西坦 - 脯氨酸模型展示了一种强大且准确的分析方法，用于通过拉曼光谱、二次抽样和化学计量学对固态中的低含量（<0.1%）进行定量分析。该方法包括一个五步过程：通过拉曼映射从每个样品中收集相对大量的光谱（8410个），进行细致的数据预处理以去除光谱伪像，使用0 - 100%浓度范围的偏最小二乘（PLS）回归模型估计每个像素处的浓度，使用更准确的、浓度范围缩小的PLS模型计算每个像素处的分析物浓度，最后对所有8000多个浓度预测进行统计分析以得出准确的整体样品浓度。对于0.05 - 1.0%的浓度范围，相对预测准确度约为2.4%，检测限与高效液相色谱相当（0.03%对0.041%）。对于数据预处理，我们开发了一种独特的宇宙射线去除方法并使用了自动基线校正方法，这两种方法都不需要用户进行主观干预，因此完全可以自动化。该方法适用于难以通过色谱法分析的系统，如水合物、多晶型物或溶剂化物污染。
