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Chiral recognition and determination of enantiomeric excess of chiral compounds by UV-visible-shortwave near infrared diffuse reflectance spectroscopy with chemometrics

机译:紫外可见短波近红外漫反射光谱与化学计量学的手性识别和对映体过量的对映体测定

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A simple approach is proposed for the chiral recognition and determination of enantiomeric excess of enantiomers, based on a UV-visible-shortwave near infrared diffuse reflectance spectroscopy (UV-vis-SWNIR DRS) technique combined with chemometrics. The results of chiral recognition show that principal component analysis (PCA) combined with UV-vis-SWNIR DRS is able to discriminate chiral compounds based on different chirality. Determination of enantiomeric excess value was performed by linear regression model partial least squares (PLSR) and non-linear regression model support vector machine regression (SVR) combined with UV-vis-SWNIR diffuse reflectance spectroscopy. After wavelength selection, spectral pre-treatments and parameter optimization, both models showed good prediction ability: the determination coefficients (R2) of prediction set by the PLSR model and the SVR model are 0.9921 and 0.9951, respectively, and mean standard errors (MSEs) are 0.0029 and 0.0020, respectively. The SVR model has a better prediction effect. The detection limit (LD) of this method was 0.059. The results prove that this approach can be used to discriminate chiral compounds and determine enantiomeric excess of enantiomers.
机译:基于紫外可见短波近红外漫反射光谱技术(UV-vis-SWNIR DRS)结合化学计量学,提出了一种简单的方法用于手性识别和确定对映体过量的对映异构体。手性识别的结果表明,主成分分析(PCA)与UV-vis-SWNIR DRS结合可以区分不同手性的手性化合物。通过线性回归模型偏最小二乘(PLSR)和非线性回归模型支持向量机回归(SVR)结合UV-vis-SWNIR漫反射光谱法确定对映体超值。经过波长选择,光谱预处理和参数优化,两个模型均具有良好的预测能力:预测集的确定系数( R 2 ) PLSR模型和SVR模型的误差分别为0.9921和0.9951,平均标准误(MSE)分别为0.0029和0.0020。 SVR模型具有较好的预测效果。该方法的检出限(LD)为0.059。结果证明该方法可用于区分手性化合物并确定对映体过量的对映体。

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