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。     

Development of Gradient Retention Model in Ion Chromatography. Part III: Fuzzy Logic QSRR Approach

In this paper, the authors tested methodology that overcame the most common limitation of quantitative structure-retention relationship (QSRR) models: their limited applicability at the specific conditions for which models were developed. The modeling was performed on ion chromatographic analysis of “wood sugars”. Adaptive neuro-fuzzy interference system, an advanced artificial intelligence regression tool, was applied in combination with genetic algorithm scanning to obtain good and reliable QSRR models. The obtained QSRR models were applied for predicting data that were required for further development of general isocratic and gradient retention models. All three developed models (QSRR, isocratic, and gradient) indicated good prediction ability with root mean square error of prediction ≤0.1557. The performances of the methodology were compared with those presented in previous research—namely genetic algorithm in combinations with—stepwise multiple linear regression, partial least squares, uninformative variable elimination–partial least squares, and artificial neural network regression.

     

A heuristic and parallel simulated annealing algorithm for variable selection in near‐infrared spectroscopy analysis

A new heuristic and parallel simulated annealing algorithm was proposed for variable selection in near‐infrared spectroscopy analysis. The algorithm employs a parallel mechanism to enhance the search efficiency, a heuristic mechanism to generate high‐quality candidate solutions, and the concept of Metropolis criterion to estimate accuracy of the candidate solutions. Several near‐infrared datasets have been evaluated under the proposed new algorithm, with partial least squares leading to improved analytical figures of merit upon wavelength selection. Improved robust and predictive regression models were obtained by the new algorithm. The method could also be helpful in other chemometric activities such as classification or quantitative structure‐activity relationship problems.     

Fluorescence spectroscopic determination of triglyceride in human serum with window genetic algorithm partial least squares

Fluorescence spectrum, as well as the first and second derivative spectra in the region of 220–900 nm, was utilized to determine the concentration of triglyceride in human serum. Nonlinear partial least squares regression with cubic B‐spline‐function‐based nonlinear transformation was employed as the chemometric method. Window genetic algorithms partial least squares (WGAPLS) was proposed as a new wavelength selection method to find the optimized spectra wavelengths combination. Study shows that when WGAPLS is applied within the optimized regions ascertained by changeable size moving window partial least squares (CSMWPLS) or searching combination moving window partial least squares (SCMWPLS), the calibration and prediction performance of the model can be further improved at a reasonable latent variable number. SCMWPLS should start from the sub‐region found by CSMWPLS with the smallest root mean squares error of calibration (RMSEC). In addition, WGAPLS should be utilized within the region of smallest RMSEC whether it is the sub‐region found by CSMWPLS or region combination found by SCMWPLS. Moreover, the prediction ability of nonlinear models was better than the linear models significantly. The prediction performance of the three spectra was in the following order: second derivative spectrum < original spectrum < first derivative spectrum. Wavelengths within the region of 300–367 nm and 386–392 nm in the first derivative of the original fluorescence spectrum were the optimized wavelength combination for the prediction model. Copyright © 2012 John Wiley & Sons, Ltd.     

高效液相色谱法构建定量结构-保留关系预测中药肝肾毒性组分的正辛醇-水分配系数值

构建了定量结构-保留关系(QSRR)测定马兜铃酸A、马兜铃酸B、马兜铃内酰胺及白黎芦醇的正辛醇-水分配系数(K_(ow))。采用反相高效液相色谱(RP-HPLC)法,以甲醇-水为流动相,以16种已知K_(ow)值的酸性和中性苯系物为模型化合物,以保留时间两点校正法(DP-RTC)校正保留时间,并由Snyder-Soczewinski方程得100%水相保留因子k_w,建立了表观正辛醇-水分配系数K_(ow)″与k_w的定量关系(QSRR模型),并对模型进行了内、外部验证。结果显示,不同pH下的QSRR模型线性相关性良好(相关系数R~2为0.980~0.987),内部验证(交叉验证相关系数R_(cv)~2为0.982~0.988)和外部验证结果(6种验证化合物的相对误差RE为0.6%~10.9%)令人满意。将建立的QSRR模型应用于中药中4种潜在肝肾毒性化合物的K_(ow)测定,并与软件计算值、摇瓶法(SFM)实验值比较,结果显示该方法准确性更高,且简单快捷。该文提出的采用中性及酸性苯系物建立QSRR模型,通过对结构与性质相似的中药材组分进行RP-HPLC分析,得到各待测组分的保留时间即可获得其K_(ow)值的简便策略,解决了中药组分复杂且难以分离、无法通过SFM测定其K_(ow)值的问题,为通过定量-构效关系(QSAR)模型实现快速预测中药组分的肝肾毒性提供了可靠的K_(ow)数据。     

Dual‐mode hydrophilic interaction normal phase and reversed phase liquid chromatography of polar compounds on a single column

Adopting a stationary phase convention circumvents problematic definition of the boundary between the stationary and the mobile phase in the liquid chromatography, resulting in thermodynamically consistent and reproducible chromatographic data. Three stationary phase definition conventions provide different retention data, but equal selectivity: (i) the complete solid phase moiety; (ii) the solid porous part carrying the active interaction centers; (iii) the volume of the inner column pores. The selective uptake of water from the bulk aqueous‐organic mobile phase significantly affects the volume and the properties of polar stationary phases. Some polar stationary phases provide dual‐mode retention mechanism in aqueous‐organic mobile phases, reversed‐phase in the water‐rich range, and normal‐phase at high concentrations of the organic solvent in water. The linear solvation energy relationship model characterizes the structural contributions of the non‐selective and selective polar interactions both in the water‐rich and organic solvent‐rich mobile phases. The inner‐pore convention provides a single hold‐up volume value for the retention prediction on the dual‐mode columns over the full mobile phase range. Using the dual‐mode monolithic polymethacrylate zwitterionic micro‐columns alternatively in each mode in the first dimension of two‐dimensional liquid chromatography, in combination with a short reversed‐phase column in the second dimension, provides enhanced sample information.     

A simple,fast, and accurate thermodynamic‐based approach for transfer and prediction of gas chromatography retention times between columns and instruments Part I: Estimation of reference column geometry and thermodynamic parameters

The transfer of retention times based on thermodynamic models between columns can aid in separation optimization and compound identification in gas chromatography. Although earlier investigations have been reported, this problem remains unsuccessfully addressed. One barrier is poor predictive accuracy when moving from a reference column or system to a new target column or system. This is attributed to challenges associated with the accurate determination of the effective geometric parameters of the columns. To overcome this, we designed least squares‐based models that account for geometric parameters of the columns and thermodynamic parameters of compounds as they partition between mobile and stationary phases. Quasi‐Newton‐based algorithms were then used to perform the numerical optimization. In this first of three parts, the model used to determine the geometric parameters of the reference column and the thermodynamic parameters of compounds subjected to separation is introduced. As will be shown, the overall approach significantly improves the predictive accuracy and transferability of thermodynamic data (and retention times) between columns of the same stationary phase chemistry. The data required for the determination of the thermodynamic parameters and retention time prediction are obtained from fast and simple experiments. The proposed model and optimization algorithms were tested and validated using simulated and experimental data.     

Modeling based on subspace orthogonal projections for QSAR and QSPR research

A novel projection modeling method for quantitative structure activity relationship (QSAR) and quantitative structure property relationship (QSPR) is developed in this paper. Orthogonalization of block variables is introduced to deal with the problem of variable selection. Projections based on least squares are used to construct the modeling space in order to search for the best regression directions for chemical modeling. A suitable prediction space for such a model is further defined to confine the usage range of the model. Three real data sets were analyzed to check the performance of the proposed modeling method. The results obtained from Monte‐Carlo cross‐validation (MCCV) showed that the proposed modeling method might provide better results for QSAR and QSPR modeling than PCR and PLS with respect to both fitting and prediction abilities. Copyright © 2007 John Wiley & Sons, Ltd.