Solvation in octanol: parametrization of the continuum MST model

This study reports the parametrization of the HF/6‐31G(d) version of the MST continuum model for n‐octanol. Following our previous studies related to the MST parametrization for water, chloroform, and carbon tetrachloride, a detailed exploration of the definition of the solute/solvent interface has been performed. To this end, we have exploited the results obtained from free energy calculations coupled to Monte Carlo simulations, and those derived from the QM/MM analysis of solvent‐induced dipoles for selected solutes. The atomic hardness parameters have been determined by fitting to the experimental free energies of solvation in octanol. The final MST model is able to reproduce the experimental free energy of solvation for 62 compounds and the octanol/water partition coefficient (log P_ow) for 75 compounds with a root‐mean‐square deviation of 0.6 kcal/mol and 0.4 (in units of log P), respectively. The model has been further verified by calculating the octanol/water partition coefficient for a set of 27 drugs, which were not considered in the parametrization set. A good agreement is found between predicted and experimental values of log P_o/w, as noted in a root‐mean‐square deviation of 0.75 units of log P. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 1180–1193, 2001     

Determination of Fipronil in Acetamiprid Formulation by Attenuated Total Reflectance-Mid-Infrared Spectroscopy Combined with Partial Least Squares Regression

There is a high demand for rapid determination of fipronil in pesticide preparations because it has been restricted and even prohibited in many countries. An infrared-based methodology was developed for this analyte in acetamiprid formulations by attenuated total reflectance mid-infrared spectroscopy. The quantitative calibration models of fipronil were established by partial least squares regression. The determination coefficients (R²) of the model were above 0.99 while both the root mean square error of prediction and root mean square error of calibration were below 0.0011, which showed the partial least squares model accurately predicted fipronil concentrations in acetamiprid. The accuracy was further demonstrated by comparison with another two models' results of low (<1.0%, w/w) and high concentration sample sets (1.0%–4.5%, w/w). These results demonstrate the potential of infrared spectroscopy to quickly detect fipronil in acetamiprid.     

Application of genetic algorithm‐support vector regression (GA‐SVR) for quantitative analysis of herbal medicines

In this paper, a genetic algorithm‐support vector regression (GA‐SVR) coupled approach was proposed for investigating the relationship between fingerprints and properties of herbal medicines. GA was used to select variables so as to improve the predictive ability of the models. Two other widely used approaches, Random Forests (RF) and partial least squares regression (PLSR) combined with GA (namely GA‐RF and GA‐PLSR, respectively), were also employed and compared with the GA‐SVR method. The models were evaluated in terms of the correlation coefficient between the measured and predicted values (Rp), root mean square error of prediction, and root mean square error of leave‐one‐out cross‐validation. The performance has been tested on a simulated system, a chromatographic data set, and a near‐infrared spectroscopic data set. The obtained results indicate that the GA‐SVR model provides a more accurate answer, with higher Rp and lower root mean square error. The proposed method is suitable for the quantitative analysis and quality control of herbal medicines. Copyright © 2012 John Wiley & Sons, Ltd.     

1‐Octanol/Water Partition Coefficients of 1Alkyl‐3‐methylimidazolium Chloride

The solubilities of 1alkyl‐3‐methylimidazolium chloride, [C_nmim][Cl], where n=4, 8, 10, and 12, in 1octanol and water have been measured by a dynamic method in the temperature range from 270 to 370 K. The solubility data was used to calculate the 1octanol/water partition coefficients as a function of temperature and alkyl substituent. The melting point, enthalpies of fusion, and enthalpies of solid–solid phase transitions were determined by differential scanning calorimetry, DSC. The solubility of [C_nmim][Cl], where n=10 or 12 in 1octanol is comparable and higher than that of [C₄mim][Cl] in 1octanol. Liquid 1n‐octyl‐3‐methylimidazolium chloride, [C₈mim][Cl], is not miscible with 1octanol and water, consequently, the liquid–liquid equilibrium, LLE was measured in this system. The differences between the solubilities in water for n=4 and 12 are shown only in α₁ and γ₁ solid crystalline phases. Additionally, the immiscibility region was observed for the higher concentration of [C₁₀mim][Cl] in water. The intermolecular solute–solvent interaction of 1butyl‐3‐methylimidazolium chloride with water is higher than for other 1alkyl‐3‐methylimidazolium chlorides. The data was correlated by means of the UNIQUAC ASM and two modified NRTL equations utilizing parameters derived from the solid–liquid equilibrium, SLE. The root‐mean‐square deviations of the solubility temperatures for all calculated data are from 1.8 to 7 K and depend on the particular equation used. In the calculations, the existence of two solid–solid first‐order phase transitions in [C₁₂mim][Cl] has also been taken into consideration. Experimental partition coefficients (log P) are negative at three temperatures; this is evidence for the possible use of these ionic liquids as green solvents.     

Determination of polyphenols in oats by near-infrared spectroscopy (NIRS) and two-dimensional correlation spectroscopy

Two-dimensional correlation spectroscopy (2DCOS) and near-infrared spectroscopy (NIRS) were used to determine the polyphenol content in oat grain. A partial least squares (PLS) algorithm was used to perform the calibration. A total of 116 representative oat samples from four locations in China were prepared and the corresponding near-infrared spectra were measured. Two-dimensional correlation spectroscopy was employed to select wavelength bands for the PLS regression model for the polyphenol determination. The number of PLS components and intervals was optimized according to the coefficients of determination (R²) and root mean square error of cross validation (RMSECV) in the calibration set. The performance of the final model was evaluated using the correlation coefficient (R) and the root mean square error of validation (RMSEV) in the prediction set. The results showed the band corresponding to the optimal calibration model was between 1350 and 1848?nm and the optimal spectral preprocessing combination was second derivative with second smoothing. The optimal regression model was obtained with an R² of 0.8954 and an RMSECV of 0.06651 in the calibration set and R of 0.9614 and RMSEV of 0.04573 in the prediction set. These measurements reveal the calibration model had qualified predictive accuracy. The results demonstrated that the 2DCOS with PLS was a simple and rapid method for the quantitative determination of polyphenols in oats.     

Quality Evaluation of Ephedrae herba by near Infrared Spectroscopy

ABSTRACT

A novel method was developed for the quality control of Ephedrae herba by near-infrared (NIR) spectroscopy. First, qualitative models established by discriminant analysis and support vector machine were used for the preliminary screening of unqualified samples of E. herba. Then quantitative models of ephedrine and the total alkali (ephedrine and pseudoephedrine) were established by partial least squares regression and particle swarm optimization based least square support vector machine. The contents of test samples were predicted by the established NIR quantitative models. As a result, the accuracies of unqualified identification were 98.9% by discriminant analysis and 100% by support vector machine. The performance of the particle swarm optimization based least square support vector machine models were better than the partial least squares regression models. The correlation coefficients were both more than 0.98 and relative standard errors of calibrations were less than 9% in the calibration sets of particle swarm optimization based least square support vector machine models. As for the test sets, the correlation coefficients were both more than 0.93 and the relative standard errors of prediction were less than 13%, indicating satisfactory predicted results. All of these results demonstrated that NIR spectroscopy may be a powerful tool for the quality control of E. herba.     

Determination of Chlorpyrifos Residue by Near-Infrared Spectroscopy in White Radish Based on Interval Partial Least Square (iPLS) Model

Abstract

This article presents a multivariate method of rapidly determining chlopyrifos residue in white radish, based on near-infrared spectroscopy and partial least squares (PLS) regression. Interval PLS (iPLS) was utilized to select the optimum wave number range. The number of PLS components and the number of intervals were optimized according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. The result showed that the iPLS model was more reliable than the full model and that near-infrared spectroscopy with iPLS algorithm could be used successfully to analyze chlorpyrifos residue in white radish.     

Determination of Iridoids in Gentiana rigescens by Infrared Spectroscopy and Multivariate Analysis

Gentiana rigescens is a famous herbal medicine in China for treatment of convulsion, rheumatism, and jaundice. Here, the infrared determination of gentiopicroside, swertiamarin, sweroside, and loganic acid in G. rigescens from different areas and varieties was presented for the first time. Reference information for the iridoids were obtained by high-performance liquid chromatography. Partial least squares was used to characterize the relationship between spectra matrix and concentration vector for the determination of the analytes. For determination of gentiopicroside, the appropriate performance of partial least squares model was acquired with coefficient of determination of calibration and coefficient of determination of prediction values of 0.965 and 0.868. The root mean square error of estimation (RMSEE), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) values were 2.612, 5.292, 5.239?mg g^?1, and 2.701, respectively, based on the first derivative and multiplicative scatter correction. For determination of the total iridoids, the best results were obtained using the coefficient of determination of calibration and coefficient of determination of prediction of 0.943 and 0.834, RMSEE, RMSECV, RMSEP and RPD of 3.896, 7.536, 6.543?mg g^?1 and 2.438, respectively, based on the first derivative. Both models were reliable and robust. The results demonstrated that infrared spectroscopy provided a rapid, low-cost tool to monitor the quality of G. rigescens by the determination of the iridoids.     

The effects of atom pyramidalization and square distribution on the stability of F4F6‐(BN)n polyhedrons

The structures and stability of F₄F₆‐(BN)_n polyhedrons (n = 20–30) with the alternation of B and N atoms were studied with DFT method. The calculation results reveal that the atoms at square–square fusions with large pyramidalization angles are remarkably extruded out of the surfaces of (BN)_n polyhedrons. The energetically favorable isomers do not contain square–square bonds and the energies of those isomers containing square–square bonds increase with the number of square–square bonds linearly, demonstrating that the energetically favorable structures of F₄F₆‐(BN)_n polyhedrons satisfy the isolated square rule and square adjacency penalty rule. The atom pyramidalization determines the stability of the isomers. The binding energy is fitted to the numbers of vertices formed from different faces and a model is proposed to predict the relative stability of these polyhedral molecules. © 2009 Wiley Periodicals, Inc. Int J Quantum Chem, 2011     

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.     

Octanol/water partitioning simulation by RP‐HPLC for structurally diverse acidic drugs: Comparison of three columns in the presence and absence of n‐octanol as the mobile phase additive

The advantageous effect of n‐octanol as a mobile phase additive for lipophilicity assessment of structurally diverse acidic drugs both in the neutral and ionized form was explored. Two RP C₁₈ columns, ABZ⁺ and Aquasil, were used for the determination of logk_w indices, and the results were compared with those previously reported on a base‐deactivated silica column. At pH 2.5, the use of n‐octanol‐saturated buffer as the mobile phase aqueous component led to high‐quality 1:1 correlation between logk_w and logP for the ABZ⁺ column, while inferior statistics were obtained for Aquasil. At physiological pH, the correlations were significantly improved if strongly ionized acidic drugs were treated separately from weakly ionized ones. In the latter case, 1:1 correlations between logD_7.4 and logk_w^oct indices were obtained in the presence of 0.25% n‐octanol. Concerning strongly ionized compounds, adequate correlations were established under the same conditions; however, slopes were significantly lower than unity, while large negative intercepts were obtained. According to the absolute difference (diff = logD_7.4–logk_w) pattern, base‐deactivated silica showed a better performance than ABZ⁺, however, the latter seems more efficient for the lipophilicity assessment of highly lipophilic acidic compounds. Aquasil may be the column of choice if logD_7.4<3 with the limitation, however, that very hydrophilic compounds cannot be measured.     

The performance of 1‐ethyl‐3‐methylimidazolium tetrafluoroborate ionic liquid as mobile phase additive in HPLC‐based lipophilicity assessment

Ionic liquids have been widely used as green alternative mobile phase additives to shield the residuals silanols groups and modify the stationary/mobile phase HPLC systems. The present study aimed to evaluate the performance of the ionic liquid 1‐ethyl‐3‐methylimidazolium tetrafluoroborate ([EMIM][BF4]) in producing extrapolated logk_w indices suitable to substitute for octanol–water logP or logD values. The effect of [EMIM][BF4] was investigated for a set of basic and neutral drugs using two different columns, BDS and ABZ⁺. [EMIM][BF4] was added simply alone or in combination with n‐octanol and was compared with the conventional masking agent n‐decylamine. [EMIM][BF4] reduced the retention by suppressing silanophilic interactions, althoug to a lower extent than n‐decylamine. Addition of n‐octanol further decreased the retention by shielding silanol sites on BDS and/or interacting with polar groups through hydrogen bonding on ABZ⁺. Logk_w/logD_7.4 relationships proved moderate compared with those derived upon addition of n‐decylamine. They were considerably improved upon the introduction of protonated fraction F⁺ in the correlation, reflecting ion pair formation between the chaotropic anion [BF4]^‐ and the protonated basic compounds. In this aspect, the ionic liquid [EMIM][BF4], although efficient as a masking agent, cannot be recommended as mobile phase additive to reproduce octanol–water partitioning. Copyright © 2010 John Wiley & Sons, Ltd.     

Determination of Nitrogen in Rapeseed by Fourier Transform Infrared Photoacoustic Spectroscopy and Independent Component Analysis

Fourier-transform mid-infrared photoacoustic spectroscopy was utilized for rapid and nondestructive determination of nitrogen in rapeseeds. Rapeseed spectra were characterized by independent component analysis for quantitative calibration. A calibration model was built by using independent components as the input for partial least squares. Compared to full-spectrum partial least squares, the combined model achieved higher prediction accuracy with a residual predictive deviation of 2.06. Moreover, a genetic algorithm coupled with partial least squares was adopted to optimize the independent components for partial least square modeling and provide a further refined model with the highest residual predictive deviation of 2.12. A t-test verified a high congruence between results obtained by calibration models and the reference Kjeldahl method. This study demonstrated the promise of Fourier-transform mid-infrared photoacoustic spectroscopy for the determination of nitrogen in rapeseeds and the applicability of independent components for multivariate calibration.     

Polynomial multivariate least‐squares regression for modeling nonlinear data applied to in‐depth characterization of chromatographic resolution

Well‐established, linear multivariate calibration methods such as multivariate least‐squares regression (MLR), principal component regression (PCR), or partial least squares (PLS) have two limitations: (i) measured data must be linearly related to the response variables and (ii) predictor variables x_{n = 1, …, N} cannot be coupled to each other. For evaluation of nonlinear data, however, these restrictions need to be overcome and thus polynomial multivariate least‐squares regression (PMLR or “response surfaces”) has been introduced here. PMLR is based on multivariate least squares but incorporates all combinations of predictor variables up to a user‐selected polynomial order (e.g., including u or v = 0). Because of the inclusion of such coupled terms and their powers, PMLR models are better adapted to model nonlinear data and can help to enhance the prediction step's accuracy and precision. PMLR has been based on MLR because it facilitates—unlike PCR or PLS—a physical and chemical interpretation of the predictors. Hence, the origins and the relevance of nonlinear and/or coupled predictors can be investigated. The details of the PMLR algorithm and its implementation are presented along with a method for model optimization utilizing gradients of response surfaces. Newly developed PMLR models up to quintic order have been applied to predict a chromatograph's peak resolution as a function of six‐instrument parameters. It has been demonstrated that PMLR is better capable than MLR and PCR to describe these nonlinear and coupled instrument parameters. In addition, the novel software tool has been utilized for model optimization to determine instrument parameters, which result in the best chromatographic resolution. Copyright © 2011 John Wiley & Sons, Ltd.