An expert system for the selection of criteria for selectivity optimization in high-pressure liquid chromatography

Summary Systematic procedures for the optimization of chromatographic selectivity require objective criteria to characterize the quality of separation in a chromatogram. Numerous criteria have been suggested. Different criteria yield different results and the choice will depend on a large number of factors. It is genuinely difficult to select the most suitable criterion in a particular situation. For these reasons, an expert system has been developed to assist chromatographers in the selection of optimization criteria. A structured representation of the required knowledge and its implementation in an expert-system shell are presented in this paper.     

A new and efficient variable selection algorithm based on ant colony optimization. Applications to near infrared spectroscopy/partial least-squares analysis

A new variable selection algorithm is described, based on ant colony optimization (ACO). The algorithm aim is to choose, from a large number of available spectral wavelengths, those relevant to the estimation of analyte concentrations or sample properties when spectroscopic analysis is combined with multivariate calibration techniques such as partial least-squares (PLS) regression. The new algorithm employs the concept of cooperative pheromone accumulation, which is typical of ACO selection methods, and optimizes PLS models using a pre-defined number of variables, employing a Monte Carlo approach to discard irrelevant sensors. The performance has been tested on a simulated system, where it shows a significant superiority over other commonly employed selection methods, such as genetic algorithms. Several near infrared spectroscopic experimental data sets have been subjected to the present ACO algorithm, with PLS leading to improved analytical figures of merit upon wavelength selection. The method could be helpful in other chemometric activities such as classification or quantitative structure-activity relationship (QSAR) problems.     

On the construction of experimental designs for a given task by jointly optimizing several quality criteria: Pareto-optimal experimental designs

Experimental designs for a given task should be selected on the base of the problem being solved and of some criteria that measure their quality. There are several such criteria because there are several aspects to be taken into account when making a choice. The most used criteria are probably the so-called alphabetical optimality criteria (for example, the A-, E-, and D-criteria related to the joint estimation of the coefficients, or the I- and G-criteria related to the prediction variance). Selecting a proper design to solve a problem implies finding a balance among these several criteria that measure the performance of the design in different aspects. Technically this is a problem of multi-criteria optimization, which can be tackled from different views.     

Multiresponse problems: desirability and other optimization approaches

A diversity of multiresponse optimization methods has been introduced in the literature; however, their performance has not been thoroughly explored, and only a classical desirability‐based criterion has been commonly used. With the aim of contributing to help practitioners in selecting an effective criterion for solving multiresponse optimization problems developed under the response surface methodology framework, and thus to find compromise solutions that are technically and economically more favorable, the working ability of several easy‐to‐use criteria is evaluated and compared with that of a theoretically sound method. Four case studies with different numbers and types of responses are considered. Less‐sophisticated criteria were able to generate solutions similar to those generated by sophisticated methods, even when the objective is to depict the Pareto frontier in problems with conflicting responses. Two easy‐to‐use criteria that require less‐subjective information from the user yielded solutions similar to those of a classical desirability‐based criterion. Preference parameters range and increment impact on optimal solutions were also evaluated.     

Advanced simplex optimization of two-factor selectivity by high performance thin-layer chromatography

Summary A computer-assisted mixture design simplex method is presented for optimization of the separation of a mixture of five benzoic acid derivatives in reversephase HPTLC and twelve PTH-amino acids in normal phase HPTLC. A two-factor selectivity rectangle concept was used. The method is based on a special polynomial estimated from nine experimental runs, using the R_F difference as the selection criterion, with connection to a general simplex method for optimization using a microcomputer. Excellent agreement is obtained between predicted data and experimental results, and more than half the number of experiments required in the general simplex method can be omitted.     

计算机辅助高效液相色谱梯度洗脱的优化

本文介绍了计算机辅助反相高效液相色谱梯度洗脱优化分离的方法。首先采用混合设计法，通过９个预试验建立目标函数，然后运用双因素（初始流动相组成Ｃ和梯度时间Ｔ）扫描技术，由计算机给出优化分离条件，实验结果和预示完全一致。     

Criteria for judging the quality of separation in chromatograms containing solvent peaks

Summary Formal procedures used for optimizing chromatographic selectivity require objective, numerical functions to assess the quality of each chromatogram obtained during the process. Many of such optimization criteria have been suggested for chromatograms in which all or a number of well-shaped peaks need to be separated. This paper describes a method by which resolution can be measured in chromatograms in which very large, highly non-symmetrical background peaks,e.g. typical solvent peaks, are present. Using this method, various optimization criteria can be calculated. The method is evaluated using a simulation program which constructs chromatograms from the recorded profiles of individual experimental peaks. It is also demonstrated for use in an experimental optimization procedure in reversed-phase liquid chromatography.     

Use of a generalized fisher equation for global optimization in chemical kinetics

A new approach for parameter estimation in chemical kinetics has been recently proposed (Ross et al. Proc. Natl. Acad. Sci. U.S.A. 2010, 107, 12777). It makes use of an optimization criterion based on a Generalized Fisher Equation (GFE). Its utility has been demonstrated with two reaction mechanisms, the chlorite-iodide and Oregonator, which are computationally stiff systems. In this Article, the performance of the GFE-based algorithm is compared to that obtained from minimization of the squared distances between the observed and predicted concentrations obtained by solving the corresponding initial value problem (we call this latter approach "traditional" for simplicity). Comparison of the proposed GFE-based optimization method with the "traditional" one has revealed their differences in performance. This difference can be seen as a trade-off between speed (which favors GFE) and accuracy (which favors the traditional method). The chlorite-iodide and Oregonator systems are again chosen as case studies. An identifiability analysis is performed for both of them, followed by an optimal experimental design based on the Fisher Information Matrix (FIM). This allows to identify and overcome most of the previously encountered identifiability issues, improving the estimation accuracy. With the new data, obtained from optimally designed experiments, it is now possible to estimate effectively more parameters than with the previous data. This result, which holds for both GFE-based and traditional methods, stresses the importance of an appropriate experimental design. Finally, a new hybrid method that combines advantages from the GFE and traditional approaches is presented.     

Geometry optimization in density functional methods

The geometry optimization in delocalized internal coordinates is discussed within the framework of the density functional theory program deMon. A new algorithm for the selection of primitive coordinates according to their contribution to the nonredundant coordinate space is presented. With this new selection algorithm the excessive increase in computational time and the deterioration of the performance of the geometry optimization for floppy molecules and systems with high average coordination numbers is avoided. A new step selection based on the Cartesian geometry change is introduced. It combines the trust radius and line search method. The structure of the new geometry optimizer is described. The influence of the SCF convergence criteria and the grid accuracy on the geometry optimization are discussed. A performance analysis of the new geometry optimizer using different start Hessian matrices, basis sets and grid accuracies is given.     

氢键准则在DMSO水溶液中应用的评价

利用分子动力学模拟方法, 分别采用几何准则和能量准则分析了不同浓度下的二甲基亚砜(DMSO)水溶液的氢键统计和动力学等特性. 结果显示, 两种氢键准则可以很好地反映出溶液的氢键性质随浓度的变化趋势. 通过分析比较发现, 由于几何准则不能有效地排除具有弱对势能的分子对, 因此其统计的氢键数量要大于能量准则的结果.此外, 能量准则对于分子间相对取向的区分存在不足, 进而引起氢键寿命的计算结果偏大.因此,为使氢键分析更加准确, 本文建议使用几何-能量混合型氢键准则.     

Optimization of selectivity by tuning column temperatures for series-coupled columns in a dual-oven GC system

An improved optimization criterion has been introduced for computer assisted optimization of selectivity of two different capillary columns coupled in series and placed in two independently heated ovens. The suitability of this criterion, which incorporates the change of resolution power with the change of working conditions, has been verified by simultaneously tuning the temperatures of two columns differing in retention mechanism for separation of isomeric branched C₈ hydrocarbons.     

通用模拟退火用于稳健多元分析校正

模拟退火是一种全局优化算法，具有跨越局部最优点的机制，最小一乘是一种较常用的最小二乘更为稳健的优化准则，更适用于可能偏离正态分布的实际数据集，本文探讨了用最小一乘为准则并利用模拟退火方法同时测定多组分体系的可能性。应用于2－3组分药物体系分析，获得了满意的结果，本文还探讨了改变步长提高模拟退火算法优化精度的方法。     

New cut-off criterion for uninformative variable elimination in multivariate calibration of near-infrared spectra for the determination of heroin in illicit street drugs

A new cut-off criterion has been proposed for the selection of uninformative variables prior to chemometric partial least squares (PLS) modelling. After variable elimination, PLS regressions were made and assessed comparing the results with those obtained by PLS models based on the full spectral range. To assess the prediction capabilities, uninformative variable elimination (UVE)-PLS and PLS were applied to diffuse reflectance near-infrared spectra of heroin samples. The application of the proposed new cut-off criterion, based on the t-Students distribution, provided similar predictive capabilities of the PLS models than those obtained using the original criteria based on quantile value. However, the repeatability of the number of selected variables was improved significantly.     

Computer-assisted multifactor optimization of selectivity in gas chromatography

Summary A computer-assisted method is presented for the simultaneous multifactor optimization (stationary phase loading, column temperature and carrier gas flow rate) of the analytical conditions for the optimum separation of multicomponent samples in gas chromatography. The optimization of the separation over the experimental region is based on a special polynomial from twelve preliminary experiments using the resolution as the selection criterion. Computer scanning technique was used for optimum selection in three dimensions. Excellent agreement was obtained between the predicted data and the experimental results.     

Optimization of gradient profiles in ion-exchange chromatography using computer simulation programs

Optimization procedure of gradient separations in ion-exchange chromatography using simplex optimization method in combination with the computer simulation program for ion-exchange chromatography is presented. The optimization of parameters describing gradient profile for the separation in ion chromatography is based on the optimization criterion obtained from calculated chromatograms. The optimization criterion depends on the parameters used for calculations and thus exhibits the quality of gradient conditions for the separation of the analytes. Simplex method is used to calculate new gradient profiles in order to reach optimum separations for the selected set of analytes. The Simplex algorithm works stepwise, for each new combination of parameters that describe the gradient profile a new calculation is performed and from the calculated chromatogram the optimization criterion is determined. The proposed method is efficient and may reduce the time and cost of analyses of complex samples with ion-exchange chromatography.     

Optimization strategy based on genetic algorithms and neural networks applied to a polymerization process

The article presents a simple and general methodology, especially destined to the optimization of complex, strongly nonlinear systems, for which no extensive knowledge or precise models are available. The optimization problem is solved by means of a simple genetic algorithm, and the results are interpreted both from the mathematical point of view (the minimization of the objective function) and technological (the estimation of the achievement of individual objectives in multiobjective optimization). The use of a scalar objective function is supported by the fact that the genetic algorithm also computes the weights attached to the individual objectives along with the optimal values of the decision variables. The optimization strategy is accomplished in three stages: (1) the design and training of the neural model by a new method based on a genetic algorithm where information about the network is coded into the chromosomes; (2) the actual optimization based on genetic algorithms, which implies testing different values for parameters and different variants of the algorithm, computing the weights of the individual objectives and determining the optimal values for the decision variables; (3) the user's decision, who chooses a solution based on technological criteria. © 2007 Wiley Periodicals, Inc. Int J Quantum Chem, 2008     

Computer-assisted optimization of mobile phase compositions and development distance selectivity in gradient two-step development high-performance thin-layer chromatography

A computer-assisted method is presented for optimization of mobile phase compositions and development distance in gradient two-step development HPTLC. The method is based on a system which can predict the final Rf values for gradient two-step development from values measured using five preliminary runs. The statistical scanning method is then used for optimization, using Rf difference as the selection criterion. The method was evaluated using a mixture of eight components. Excellent agreement was obtained between predicted and experimental results. Gradient two-step development HPTLC, mobile phase composition optimization, velopment distance optimizationde-     

A generic stepwise optimization strategy for liquid chromatography electrospray ionization tandem mass spectrometry methods

The feasibility and advantages of using sophisticated chemometric tools in combination with the execution of thoroughly planned experiments to determine experimental conditions for optimal performance of an LC-ESI-MS/MS analysis is demonstrated. A stepwise strategy is proposed, which provides a controlled optimization procedure of the chromatographic quality (in terms of separation among the sample constituents) and maximizes the mass spectrometric signal of the selected product ions. Design of experiments (DOE) and response surface methodology are applied throughout the procedure. The stepwise approach has the advantage of dealing with the different optimization criteria separately, i.e. first ensuring sufficient chromatographic separation, then maximizing the amount of precursor ion entering the mass spectrometer, and finally generating high amounts of selected product ions. The experiments are performed on a linear ion trap mass spectrometer. Retention mapping using the band-tracking model is applied during LC development, which facilitates the optimization of segmented gradients. A set of different siderophores, strong iron chelates, is used as the model substances.     

串级萃取量优化理论和精确计算优化萃取比新方程

稀土;串级萃取量优化理论和精确计算优化萃取比新方程     

Constrained numerical gradients and composite gradients: Practical tools for geometry optimization and potential energy surface navigation

A method is proposed to easily reduce the number of energy evaluations required to compute numerical gradients when constraints are imposed on the system, especially in connection with rigid fragment optimization. The method is based on the separation of the coordinate space into a constrained and an unconstrained space, and the numerical differentiation is done exclusively in the unconstrained space. The decrease in the number of energy calculations can be very important if the system is significantly constrained. The performance of the method is tested on systems that can be considered as composed of several rigid groups or molecules, and the results show that the error with respect to conventional optimizations is of the order of the convergence criteria. Comparison with another method designed for rigid fragment optimization proves the present method to be competitive. The proposed method can also be applied to combine numerical and analytical gradients computed at different theory levels, allowing an unconstrained optimization with numerical differentiation restricted to the most significant degrees of freedom. This approach can be a practical alternative when analytical gradients are not available at the desired computational level and full numerical differentiation is not affordable. © 2015 Wiley Periodicals, Inc.