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首页> 外文期刊>Spectrochimica acta, Part A. Molecular and biomolecular spectroscopy >Prediction of P-31 nuclear magnetic resonance chemical shifts for phosphines
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Prediction of P-31 nuclear magnetic resonance chemical shifts for phosphines

机译：膦的P-31核磁共振化学位移的预测

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Quantitative relationships of the P-31 NMR chemical shifts of the phosphorus atoms in 291 phosphines with the atomic ionicity index (INI) and stereoscopic effect parameters (epsilon(alpha), epsilon(beta) ,epsilon(delta)) were primarily investigated in this paper for modeling some fundamental quantitative structure-spectroscopy relationships (QSSR). The results indicated that the P-31 NMR chemical shifts of phosphines can be described as the quantitative equation by multiple linear regression (MLR): delta(p) (ppm) = -174.0197 - 2.6724 INI + 40.4755 epsilon(alpha) + 15.1141 epsilon(beta) - 3.1858 epsilon(gamma), correlation coefficient R = 0.9479, root mean square error (rms) = 13.9, and cross-validated predictive correlation coefficient was found by using the leave-one-out procedure to be Q(2) = 0.8919. Furthermore, through way of random sampling, the estimative stability and the predictive power of the proposed MLR model were examined by constructing data set randomly into both the internal training set and external test set of 261 and 30 compounds, respectively, and then the chemical shifts were estimated and predicted with the training correlation coefficient R = 0.9467 and rms = 13.4 and the external predicting correlation coefficient Q(ext) = 0.9598 and rms = 10.8. A partial least square model was developed that produced R = 0.9466, Q = 0.9407 and Q(ext) = 0.9599, respectively. Those good results provided a new, simple, accurate and efficient methodology for calculating P-31 NMR chemical shifts of phosphines. (c) 2007 Published by Elsevier B.V.
机译：本文主要研究了291膦中磷原子的P-31 NMR化学位移与原子电离指数（INI）和立体效应参数（ε，α，β，ε）的定量关系。建模一些基本的定量结构-光谱关系（QSSR）的论文。结果表明，膦的P-31 NMR化学位移可通过多重线性回归（MLR）描述为定量方程：delta（p）（ppm）= -174.0197-2.6724 INI + 40.4755epsilonα+ 15.1141 epsilon β-3.1858epsilonγ，相关系数R = 0.9479，均方根误差（rms）= 13.9，使用留一法求出交叉验证的预测相关系数为Q（2） = 0.8919。此外，通过随机抽样的方式，通过将数据集分别随机构建到内部训练集和外部测试集中分别包含261和30种化合物的数据集中，检验了所提出的MLR模型的估计稳定性和预测能力，然后进行了化学位移通过训练相关系数R = 0.9467和rms = 13.4以及外部预测相关系数Q（ext）= 0.9598和rms = 10.8进行估计和预测。开发了偏最小二乘模型，分别产生R = 0.9466，Q = 0.9407和Q（ext）= 0.9599。这些良好的结果为计算膦的P-31 NMR化学位移提供了一种新颖，简单，准确和有效的方法。 （c）2007年由Elsevier B.V.

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