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Studies on regression modeling of spectral data as a means of chiral analysis.

机译:作为手性分析手段的光谱数据回归建模的研究。

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摘要

The enantiomeric composition of samples was determined using spectroscopy and multivariate regression modeling. Partial least-squares (PLS-1) regression models were developed from the spectral data of solutions containing both enantiomers in varying ratios. The developed regression models were used to predict the enantiomeric composition of unknown validation samples. The predictive ability of the models was evaluated in terms of the root mean square absolute error and the root mean square percent relative error.; To address the issue of enantiomeric compositions higher than 0.9, a study was conducted using a large number of samples of phenylalanine and beta-cyclodextrin in the upper percentile range, varying from 90-100%. Validation studies with these samples gave absolute errors of 0.0217.; In order to study the effects of varying the analyte concentration, two compounds were studied at five concentration levels. Three analyses were performed for each compound. One analysis used only the raw spectral data, one analysis included the concentration as a variable, and one analysis utilized the normalized spectra. Solutions of phenylalanine and beta-cyclodextrin resulted in a best absolute error of 0.0316 for the normalized spectral data. Solutions of norephedrine and beta-cyclodextrin resulted in a best absolute error of 0.0367 for the raw data. Finally, the spectral data can be used to predict the concentration, the predicted concentration used to normalize the data, and the new normalized data used to predict the enantiomeric composition with an absolute error of less than 0.06 for both compounds.; Two simple sugars were tested for their use as chiral auxiliaries. Validation studies with fructose gave absolute errors of 0.0211 (2-octanol) and 0.0308 (phenylalanine); validation studies with glucose gave an absolute error of 0.0184. A comparison study between NIR and UV-visible spectral ranges yielded much poorer results in the NIR (absolute error 0.298) than in the UV-visible (absolute error 0.0308).; Finally, a comparison study of 2-octanol and alpha-methylbenzylamine with and without a chiral auxiliary was completed. These results varied widely based on solvent and concentration. Modeling studies with impurities did not resemble the spectral behavior of real samples.
机译:使用光谱学和多元回归模型确定样品的对映体组成。从包含两种比例不同的对映异构体的溶液的光谱数据中开发出偏最小二乘(PLS-1)回归模型。开发的回归模型用于预测未知验证样品的对映体组成。根据均方根绝对误差和均方根相对误差百分比评估模型的预测能力。为了解决高于0.9的对映体组成的问题,使用大量的苯丙氨酸和β-环糊精样品在较高百分位数范围内进行了研究,百分位数范围在90-100%之间。用这些样品进行的验证研究得出的绝对误差为0.0217。为了研究改变分析物浓度的影响,研究了五个浓度水平的两种化合物。对每种化合物进行了三项分析。一种分析仅使用原始光谱数据,一种分析包括浓度作为变量,而另一种分析则使用归一化光谱。苯丙氨酸和β-环糊精的溶液对于归一化的光谱数据产生0.0316的最佳绝对误差。去氧麻黄碱和β-环糊精的溶液对原始数据的最佳绝对误差为0.0367。最后,光谱数据可用于预测浓度,预测的浓度用于归一化数据,新的归一化数据用于预测对映体组成,两种化合物的绝对误差均小于0.06。测试了两种简单的糖作为手性助剂的用途。用果糖进行的验证研究得出的绝对误差为0.0211(2-辛醇)和0.0308(苯丙氨酸)。用葡萄糖进行的验证研究得出的绝对误差为0.0184。 NIR和紫外可见光谱范围之间的比较研究得出,NIR(绝对误差0.298)比紫外可见(绝对误差0.0308)差得多。最后,完成了有和没有手性助剂的2-辛醇和α-甲基苄胺的比较研究。根据溶剂和浓度,这些结果差异很大。用杂质进行建模研究与真实样品的光谱行为并不相似。

著录项

  • 作者

    Ingle, Jemima Rose.;

  • 作者单位

    Baylor University.;

  • 授予单位 Baylor University.;
  • 学科 Chemistry Analytical.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 223 p.
  • 总页数 223
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 化学;
  • 关键词

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