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.     

Development and optimization of an HPLC analysis of citalopram and its four nonchiral impurities using experimental design methodology

In this study, the RP-HPLC method was investigated for the separation of citalopram and its four impurities by use of statistical experimental design. Initially, the influence of different experimental conditions (buffer pH, flow rate, and column temperature) on the chromatographic behavior of citalopram and its four impurities was investigated by use of partial least squares regression (PLSR) and multilayer perceptron (MLP) artificial neural networks (ANNs) trained by back-propagation. The developed models and the corresponding response surface plots were used to select the optimal HPLC conditions, buffer pH 7.0, flow rate 1.0 mL/ min, and column temperature 25 degrees C, for an efficient separation of citalopram and its four impurities. The elaborated HPLC method was found to be linear, specific, sensitive, precise, accurate, and robust. Retention times of citalopram and its impurities, obtained with the developed HPLC method, and the computed molecular parameters of the examined compounds were used in a quantitative structure retention relationship (QSRR) study. The PLSR and ANN algorithms were applied for the development of the QSRR methods. The MLP-two layers-ANN-QSRR model with root mean square error of prediction 0.105 and r(2) (observed versus predicted) 0.978 was selected. Since many different reaction conditions are applied for the synthesis of citalopram, different impurities and degradation products can be formed. Therefore, the developed QSRR model can be extended to the prediction of the retention times with the other citalopram impurities, degradation products, and metabolites.     

Development of a novel RP‐HPLC method for the efficient separation of aripiprazole and its nine impurities

The development of an RP‐HPLC method for the separation of aripiprazole and its nine impurities was performed with the use of partial least squares regression, response surface plot methodology, and chromatographic response function. The HPLC retention times and computed molecular parameters of the aripiprazole and its nine impurities were further used for the quantitative structure–retention relationship (QSRR) study. The QSRR model, R²: 0.899, Q²: 0.832, root mean square error of estimation: 4.761, root mean square error of prediction: 6.614, was developed. Very good agreement between the predicted and observed retention times (t_R) for three additional aripiprazole impurities (TC1–TC3) indicated the high prediction potential of the QSRR model for t_R evaluation of other aripiprazole impurities and metabolites. The developed HPLC method is the first reported method for the efficient separation of aripiprazole and its nine impurities, which could be used for the analysis of an additional three aripiprazole impurities (TC1–TC3).     

Comparison of theoretical and experimental models for characterizing solvent properties using reversed phase liquid chromatography

Theoretical and experimental quantitative structure–retention relationships (QSRR) models are useful for characterizing solvent properties and column selectivity in reversed phase liquid chromatography (RPLC). The chromatographic behavior of a model analyte, the herbicide atrazine, in a system derived from nine organic solvents and three chromatographic columns was used for developing QSRR models. Multiple linear regression (MLR) and partial least squares regression (PLSR) were used as statistical approaches. The similarities and differences between linear solvation energy relationships (LSER), and semi-empirical and theoretical molecular models were demonstrated. QSRR models show high predictive power, and can successfully predict retention factor (log k) for new solvents. The models are useful for solvent optimization and reducing time for method development in RPLC. The herbicide atrazine can be readily analyzed at a low level, and all three columns provided good resolution, high-performance and symmetrical peaks. The method is suitable for analysis of atrazine in water samples.     

Polynomial Neural Network for Linear and Non-linear Model Selection in Quantitative-Structure Activity Relationship Studies on the Internet

Abstract

This article presents a self-organising multilayered iterative algorithm that provides linear and non-linear polynomial regression models thus allowing the user to control the number and the power of the terms in the models. The accuracy of the algorithm is compared to the partial least squares (PLS) algorithm using fourteen data sets in quantitative-structure activity relationship studies. The calculated data show that the proposed method is able to select simple models characterized by a high prediction ability and thus provides a considerable interest in quantitative-structure activity relationship studies. The software is developed using client-server protocol (Java and C++ languages) and is available for world-wide users on the Web site of the authors.     

偏最小二乘线性拟合滴定甘蔗糖蜜中多组分有机酸

将滴定体系调节至pH 2.0,用碱标准溶液滴定至特定pH所消耗滴定荆为测量指标,构建了多组分有机酸滴定数据阵,分别以主成分回归法、偏最小二乘法以及人工神经元网络法进行多组分拟合.结果表明,偏最小二乘法的拟合结果最佳,对混合体系中乙酸、乳酸、草酸、琥珀酸、柠檬酸和乌头酸总量的相对预测均方根误差分别为5.80%、8.88%...     

Simultaneous spectrophotometric determination of p-benzoquinone and chloranil after microcrystalline naphthalene extraction by using genetic algorithm-based wavelength selection-partial least squares regression.

Microcrystalline naphthalene extraction has been used for the preconcentration of p-benzoquinone and tetrachloro-p-benzoquinone (chloranil), after their reaction by aniline, and later simultaneous spectrophotometric analysis by genetic algorithm-partial least squares (GA-PLS) calibration. The chemical variables affecting the analytical performance of the methodology were studied and optimized. Under the optimum conditions i.e., [aniline] = 0.05 M and [naphthalene] = 2.2% (w/v), preconcentration of 25 ml of sample solution permitted the detection of 0.32 and 0.23 microg ml(-1) for p-benzoquinone and chloranil, respectively. The predictive abilities of partial least squares regression (PLS) and genetic algorithm-partial least squares regression (GA-PLS) were examined for simultaneous determination of two quinones. The GA-PLS shows superiority over other PLS methods due to the wavelength selection in PLS calibration using a genetic algorithm without loss of prediction capacity, provides useful information about the chemical system.     

The predicting study for chromatographic retention index of saturated alcohols by MLR and ANN

A QSRR method was followed to relate the observed Kovats retention indexes of saturated alcohol compounds with their molecular connectivity indices by means of multilinear regression analysis and artificial neural networks technique. The alcohols included linear, branched with hydroxyl group on a primary, secondary, or tertiary carbon atom. At first, models were generated for six OV (Ohio Valley) series columns separately, with high value of R and F statistics. Then a combined model, added a polarity term of stationary phase (M), was also developed for all these columns, and the result was satisfactory. For comparison, the neural network of BP algorithm was applied, and it was found that the neural network could exceed the level of the multiple regression method. The stability and validity of both models were tested by cross-validation technique and by prediction response values for the prediction set.     

Multi-mechanical properties comprehensive evaluation by single excitation mode using controlled laser air-force detection (CLAFD) technique

ABSTRACT

The controlled laser air-force detection (CLAFD) technique was developed to explore the feasibility of multi-mechanical property detection of polyurethane by single excitation mode. The adhesiveness, elastic modulus, hardness, resilience, and cohesiveness of polyurethane were predicted by the global variable partial least squares regression (Gv-PLSR) algorithm. Different preprocessing methods were used to preprocess the original laser data. The interval partial least squares regression (I-PLSR) algorithm was used to decrease the influences of the multicollinearity of the global laser variable and increase the stability of the multi-mechanical property prediction models. To further improve the prediction accuracy of the modeling of I-PLSR algorithm, the synergy interval PLSR (Si-PLSR) algorithm was used to combine the intervals with the higher evaluation index root-mean-square error of cross-validation (RMSECV) to predict the multi-properties. The results demonstrated that as a novel mechanical property detection technique, the CLAFD technique predicts in an efficient way. A suitable preprocessing method for the original laser data could greatly improve the effectiveness of prediction. The I-PLSR algorithm was used to improve the model’s stability significantly. Nevertheless, the prediction accuracy decreased. Comparing the I-PLSR algorithm with the Si-PLSR algorithm, the prediction accuracy and the modal stability were optimized by the latter. However, the accuracy was still lower than the Gv-PLSR algorithm. Therefore, the Gv-PLSR was the best algorithm to establish the multi-mechanical properties prediction model. This study provided a new comprehensive, nondestructive, and cross-contamination-free method to evaluate the comprehensive mechanical properties (adhesiveness, elastic modulus, hardness, resilience, and cohesiveness) of materials efficiently, especially for the soft materials such as biomaterial and food material.