Development of Gradient Retention Model in Ion Chromatography. Part I: Conventional QSRR Approach

New retention methodology that integrates the conventional quantitative structure-retention relationship (QSRR) approach and gradient retention modeling based on isocratic retention data is developed and presented in this paper. Such an integrated approach removes the general QSRR limitation of highly predefined application conditions (i.e., QSRR are generally applicable only under the conditions used during model development) and allows the prediction of retentions over a wide range of different elution conditions (practically for any isocratic or gradient elution profile). At the same time, it retains the ability to predict retention of components unknown to the model, i.e., the components that have not been used in modeling. Ion-exchange chromatography (IC) analysis of carbohydrates was selected as modeling environment. Three regression techniques were applied and compared during QSRR modeling, namely: stepwise multiple linear regression, partial least squares (PLS), and uninformative variable elimination–PLS regression. The obtained prediction results of the best QSRR model (root-mean-square error of prediction = 22.69 %) were similar to those found in the literature. The upgrade from QSRR to the integrated model did not diminish the predictive ability of the model, indicating an excellent potential of the developed methodology not only in IC but also in chromatography in general.     

甲基烷烃结构与色谱保留指数相关性的拓扑指数法研究

计算了207个甲基烷烃的127个拓扑指数变量,把变量选择方法GAPLS方法引入到定量结构与气相色谱保留关系研究中,对127个拓扑指数变量进行选择,得到了含7个变量的化合物的定量结构与色谱保留指数关系(QSRR)模型,其复相关系数的平方为0.99998,标准偏差为2.88。交互验证的复相关系数为0.99997,交互验证的预测标准偏差为2.95,表明该模型具有良好的稳定性和可靠性。对获得的7个变量进行了合理的结构解释,表明甲基烷烃色谱保留指数完全能用拓扑指数来精确表征。     

气相色谱保留指数定性方法研究进展

综述了程序升温气相色谱保留指数,I^T与Kovats恒温保留指数,之间的关系、I^T的标准化和重现性问题以及定量结构保留指数关系(QSRR)研究进展;对于应用特殊检测器和极性柱定性、对多环芳烃和多氯联苯类物质利用保留指数定性的情况,选择非正构烷烃类同系物作为参考标准时准确性大为提高,比较了由此得到的保留指数与Kovats保留指数的关系;展望了利用保留指数定性的前景。     

A QSRR Study on the Chromatographic Retention Indices of Hydroxylated Polychlorinated Biphenyls

Hydroxylated Polychlorinated Biphenyls (HO-PCBs) are the metabolite of polychlorinated biphenyls and have drawn much attention because they have hazard on human health and ecosystems. Molecular connectivity index calculation has been performed for 19 HO-PCB compounds. A number of statistically based parameters have been extracted. Linear relationship between chromatographic retention index (RI) and the molecular connectivity index of 15 compounds in the training set has been established by multiple linear regression method. The other 4 HO-PCBs are used as the external test set. The result shows that the parameters can be well used to express the quantitative structure-retention relationship (QSRR) of HO-PCBs. Good stability and predictive ability have been demonstrated by leave-one-out cross-validation and the external test set.     

基于分子结构预测气相色谱程序升温保留指数

从拓扑指数出发,研究了分子结构与气相色谱程序升温保留指数之间的关系。对所选择的部分分子结构,利用主成分回归(PCR)的相关系数R=0.9998,标准偏差S=9.987,交互检验(leave one out cross-valida-tion)所得标准偏差S=11.17。同时,对同一柱型不同升温速率条件下的保留指数之间的关系、同一升温速率不同柱型条件下保留指数之间的关系进行了初步探讨,建立的模型线性关系明显。     

Predictions of Reversed-Phase Gradient Elution LC Separations Supported by QSRR

Previous studies demonstrated that quantitative structure-retention relationships (QSRR) combined with the linear solvent strength (LSS) model allow for prediction of gradient reversed-phase liquid chromatography retention time for any analyte of a known molecular structure under defined LC conditions. A QSRR model derived at the selected gradient time and at the same gradient time was tested. The aim the present study was to evaluate the accuracy of QSRR predictions used during the predictions of LC gradient retention times with variable gradient times. For this purpose, predictions of retention times at two gradient times were used to find the optimal, different gradient times. In the first step, experimental retention data for the model set of analytes were used to derive appropriate QSRR models at two gradient times. These QSRR models were further used to predict gradient retention times for another set of testing analytes at the two selected above gradient times. Then, applying linear solvent-strength (LSS) theory, the predicted retention times for test analytes were used to find other optimal gradient times for those analytes. Satisfactory predictions of gradient retention times for test analytes were obtained at gradient times different from those applied for model analytes.     

药物RP-HPLC保留值的标准化方法──保留指数系统研究

以烷烃甲基酮类作为标准参照物,建立了反相高效液相色谱(RP-HPLC)的保留指数系统,并探讨了流动相、有机修饰剂浓度对保留指数的影响。这种RP-HPLC保留数据标准化方法可以减小RP-HPLC保留值的变异性。药物保留指数随色谱条件变化改变较小,可在实验室间进行比较。该系列参照物不仅可用于紫外检测系统,还可用于示差折光检测系统,本文所建立的RP-HPLC保留指数系统已在ODS和苯基柱上测定了70多种药物及其代谢物。     

Structural Characterization of Volatile Components of Rosa Banksiae Ait for Estimation and Prediction of Their Linear Retention Indices and Retention Times

The molecular electronegativity-distance vector （MEDV） was used to describe the molecular structure of volatile components of Rosa banksiae Ait, and QSRR model was built up by use of multiple linear regression （MLR）. Furthermore, in virtue of variable screening by the stepwise multiple regression technique, the QSRR models of 10 and 6 variables and linear retention index （LRI） 10, 7 and 6 varieables were built up by combinating MEDV with the Ultra2 column GC retention time （tR） of 53 volatile components of Rosa Banksiae Air. The multiple correlation coefficients （R） of modeling calculation values of QSRR model were 0.906, 0.906, 0.949, 0.943 and 0.949, respectively. The cross-verification multiple correlation coefficients （RCV） were 0.903, 0.904, 0.867, 0.901 and 0.904, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.     

烯烃气相色谱保留指数的QSRR研究

通过对184个烯烃类化合物在不同固定相不同柱温下的617个样本的气相色谱保留指数值(RI)与其部分参数:拓扑指数(mQ)、偶极矩(DPL)、固定液极性值(CP)及柱温(T)建立定量-色谱保留相关(QSRR)模型.分别利用多元线性回归(MLR)、偏最小二乘回归(PLSR)、人工神经网络(ANN)建模,同时采用内部及外部双重验证的办法对所得模型稳定性能进行深入分析和检验,建模计算值、留一法(LOO)交互检验(CV)预测值和外部样本的复相关系数Rcum,QLOO和Rext分别为0.999 2,0.998 4和0.999 2(MLR);0.999 0,0.998 0和0.999 1(PLSR);0.999 4,0.998 7和0.999 2(ANN).结果表明:所建定量结构保留关系(QSRR)模型具有良好的稳定性和预测能力,较好地揭示了烯烃类化合物在不同固定相不同柱温上气相色谱保留指数的变化规律.     

Molecular Distance‐Edge Vector (μ) and Chromatographic Retention Index of Alkanes

A new method of quantitative structure‐retention relationship (QSRR) is proposed for estimating and predicting gas chromatographic retention indices of alkanes by using a novel molecular distance‐edge vector, called μ vector, containing 10 elements. The QSRR model (Ml), between the μ vector and chromatographic retention indices of 64 alkanes, was developed by using multiple linear regression (MLR) with the correlation coefficient being R = 0.9992 and the root mean square (RMS) error between the estimated and measured retention indices being RMS = 5.938. In order to explain the equation stability and prediction abilities of the M1 model, it is essential to perform a cross‐validation (CV) procedure. Satisfactory CV results have been obtained by using one external predicted sample every time with the average correlation coefficient being R = 0.9988 and average RMS = 7.128. If 21 compounds, about one third drawn from all 64 alkanes, construct an external prediction set and the 43 remaining construct an internal calibration set, the second QSRR model (M2) can be created by using calibration set data with statistics being R = 0.9993 and RMS = 5.796. The chromatographic retention indices of 21 compounds in the external testing set can be predicted by the M2 model and good prediction results are obtained with R = 0.9988 and RMS = 6.508.     

多环芳烃分子结构的距边矢量表征及其气相色谱保留指数预测

 从分子图论拓扑学角度出发 ,提出用六元素组成的分子距边矢量VMDE表达上百个多环芳烃化学结构 ,对 10 0种多环芳烃的毛细管气相色谱保留值进行定量相关性研究 ,发现存在良好的线性相关关系 :IR=a +bVMDE,n =10 0 ,r =0 .988,其中a ,b为回归系数 ,可用于多环芳烃的色谱保留指数的分子建模与定量预测。