Prediction of the chromatographic retention of saturated alcohols on stationary phases of different polarity applying the novel semi-empirical topological index

The novel semi-empirical topological index (I_ET), previously developed by Heinzen and Yunes, was extended to predict the chromatographic retention of saturated alcohols on low polarity stationary phases (OV-1). The predictive ability of I_ET was also verified on stationary phases of different polarity (SE-30, OV-3, OV-7, OV-11, OV-17 and OV-25). Simple linear regressions between the retention indices and the semi-empirical topological index (RI=a+bI_ET) were established for each stationary phase separately, showing good statistical parameters. Statistical analysis showed that the QSRR model used on stationary phases of low polarity (OV-1) has high internal stability and good predictive ability for external groups. The polarity of the SE-30, OV-3, OV-7, OV-11, OV-17 and OV-25 stationary phases, indicated by retention polarity (P_R) given by Tarján et al., is reflected in the ‘a’ (intercept) and ‘b’ (slope) coefficients of the equations obtained for each of these phases. The linear relationship between the ‘a’ coefficient and P_R showed satisfactory statistical quality. Thus, it was possible to generate a single combined model of QSRR, including a parameter that represents the property of the stationary phase P_R. The combined model also has a satisfactory predictive quality, as shown by the plot of calculated versus experimental retention indices for saturated alcohols on six stationary phases of different polarity (r²=0.9956; S.D.=9.54).     

Influence of structure of alkenes on their retention on different stationary phases

Summary Retention indices of different alkenes are correlated with connectivity and topological parameters. The appropriate statistically valid relations hips are derived and discussed.     

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.     

Prediction of gas chromatographic retention indices of linear,branched, and cyclic alkanes from their physicochemical properties

Kováts retention indices for a series of linear, branched, and cyclic alkanes on squalane at any temperature, and on other stationary phases of different polarity at a given temperature, are related to physicochemical properties of the solutes, such as boiling point and molar refraction, by multiple regression analysis. The equations found permit calculation of the Kováts retention index for all alkanes, with standard deviations close to experimental error. The same equations can also be used for calculating the physicochemical parameters they contain.     

碳氢化合物、醛、酮和硫醇的定量结构-色谱保留指数相关性研究

采用平衡电负性和相对化学键长对传统距离矩阵进行修正,构建新拓扑指数Nt。结合路径数,建立碳氢化合物、醛、酮和硫醇等化合物在24种极性和非极性色谱柱上的定量结构-色谱保留指数关系(QSRR)模型,23种模型的相关系数大于0.99。模型经留n法交叉检验,显示出良好稳健性和预测能力。模型物理意义明确,表明色谱保留指数可用分子的大小、平衡电负性、支化度和形状等内在结构信息进行有效表征。模型经Needham公式分析,结果显示新指数Nt对保留指数影响最大。借助Hyperchem软件进行对比研究,结果表明拓扑化学法优于量子化学AM1法。     

饱和醇定量结构-保留相关研究中人工神经网络的应用

以拓扑指数（分子连接性指数）为结构描述符,用人工神经网络技术建立了醇类化合物的结构与色谱保留值的相关性模型。研究了网络构造对模型稳定性的影响,考察了模型在单一固定相上及固定相上的适应性。与多元线性回归法相比较,人工神经网络模型具有更好的预测结果,但外推能力较弱。     

The study of the relationship between the new topological index A(m) and the gas chromatographic retention indices of hydrocarbons by artificial neural networks

The newly developed topological indices A_m1-A_m3 and the molecular connectivity indices ^mX were applied to multivariate analysis in structure-property correlation studies. The topological indices calculated from the chemical structures of some hydrocarbons were used to represent the molecular structures. The prediction of the retention indices of the hydrocarbons on three different kinds of stationary phase in gas chromatography can be achieved applying artificial neural networks and multiple linear regression models. The results from the artificial neural networks approach were compared with those of multiple linear regression models. It is shown that the predictive ability of artificial neural networks is superior to that of multiple linear regression method under the experimental conditions in this paper. Both the topological indices ²X and A_m1 can improve the predicted results of the retention indices of the hydrocarbons on the stationary phase studied.     

A method of calculating the specific retention volumes of substances from their retention indices and specific retention volumes of n-alkanes

The choice of stationary phase for gas chromatography is dictated by the nature of the analytes. Polarity and selectivity are known to play an important role. This paper suggests equations for calculation of specific retention volumes of any compounds from their retention indices.     

The comparison of two column classification systems during the chromatographic analysis of steroids

Nowadays, due to the availability of hundreds of brands of reversed-phase liquid chromatographic columns, the selection of suitable columns can be difficult. Therefore, a good characterization and classification system is very important. Among published papers, the classification system based on quantitative structure-retention relationships and a method developed at the Katholieke Universiteit Leuven also exist. In quantitative structure-retention relationships, retention is evaluated in terms of the chemical structure of the analytes and the physicochemical properties of both the stationary and mobile phase. The second system allows to rank columns due to the values of four parameters and the calculation of specific F(KUL)-values for a reference column and to be compared with others. In this paper, the classification systems based both on quantitative structure-retention relationships and the F(KUL)-values using principal components analysis were compared. Moreover, the proposed column ranking systems have been checked in clinical practice case considering liquid chromatography determination of six steroid hormones in urine samples. Despite that the matching of both methods is not exactly the same, both classification systems provide simple, reliable and comparable results.     

Study of the influence of electronic effects on the retention of substitutedN-benzylideneanilines in normal-phase liquid chromatography

Summary A series of 72 substitutedN-benzylideneanilines (NBA) has been studied by normal-phase liquid chromatography by use of an experimental design based on variation of the composition of mobile phases prepared from heptane and three modifiers, tetrahydrofuran, 1-octanol and ethyl acetate, for each of which the specific interactions are different. Seven mobile phases were defined in the experimental design. The chromatographic data obtained are used to discuss the behavior of NBA by application of complementary chemometric methods—hierarchical ascending classification (HAC) and correspondence factor analysis (CFA). Although solute polarity has the greatest effect on retention, construction of HAC and CFA plots shows that solute behavior is also influenced by second-order electronic effects arising as a result of specific interactions between the solutes and the different modifier solvents. A quantitative structure-retention relationship has been established between Hammett's constants and the solutes projection on the first factorial axis of CFA.     

Molecular graph fingerprint: a new molecular structural characterization method for the modelling and prediction of chromatographic retention behaviour of several persistent organic pollutants

How to extract and characterize information on molecular microstructures is deemed to be the key task to accurately simulate and predict molecular properties. In terms of atomic attributes, atoms in a molecule are divided into three levels. Based upon that, inter-atomic correlations are mapped to certain reasonable spatial coordinates in virtue of radial distribution function, generating the novel molecular graph fingerprint (MoGF), which essentially provides insight into molecular inner structures. MoGF, committing itself to transformation of molecular structures into characteristic graph curves, shows valuable advantages such as easy calculation, experimental parameters-free, rich information content, and structural significance and intuitive expressions. QSRR studies were performed for 115 polychlorinated dibenzofurans (PCDFs), 41 polychlorinated dibenzo-p-dioxins (PCDDs), 62 polychlorinated naphthalenes (PCNs), and 210 polychlorinated biphenyls (PCBs including the biphenyl)) tested for their retention behaviours on gas chromatographic column DB-5. The resulting PLS models showed good performances with correlation coefficients for both training and test sets above 0.97.     

Hamiltonian as a Hessian on the Hibert space,eigenvectors as critical points,and their relation to topological invariants in the variation method

If a quantum vector varies in the Hilbert space, as trial functions do in the variational method, a vector field gets defined whose critical points are the eigenvectors of the Hamiltonian. The numbers of each type of critical point (minima, maxima, saddle points of various indices) are related to the topology of the compact variety, the closed multidimensional surface on which the trial vectors wander when they are restricted to unit normalization. The global results from that approach are compared with those of the local theory in which the type of each critical point is obtained from the Hessian on the Hilbert space whose eigenvalues are derived in terms of those of the Hamiltonian involved in the vector field. In a configuration-interaction (CI) problem for example, the type of saddle point each excited state represents is determined.     

Shifting of gas chromatographic retention times due to solvent effects — a study using sulfur chemiluminescence detection

Gas chromatrography (GC) with sulfur chemiluminescence detection (SCD) is an outstanding combination for selectively determining trace concentrations of sulfur compounds in hydrocarbon samples. GC peaks can be identified by retention times when reproducible, automated injection techniques are used. However, as described in this work, analysts should be on the look out for retention shifting due to solvent effects from sample components. Three examples of retention shifting are presented: (1) thiophene by benzene; (2) methylthiophenes by toluene; and (3) dibenzothiophene by gas oil. Depending on samples and analysis conditions, retention shifts from a few hundredths to 1.5 min have been observed. Such retention shifts are likely to cause errors in peak identifications. Therefore, when using SCD, simultaneous FID monitoring is recommended as an aid in evaluating chromatograms for possible retention shifting due to solvent effects.