Characterization of temperature dependent modifier effects in SFC using linear solvation energy relationships

Summary Linear solvation energy relationships (LSERs) are used to probe the changes in mobile and stationary phase properties of a carbon dioxide-based mobile phase and a polymeric stationary phase under near-critical conditions. Four mobile phase modifiers are compared with respect to dipolarity/polarizability, hydrogen bond donating and accepting ability, and other intermolecular interactions as a function of temperature. As temperature nears the mixture critical point, the differences in these properties between the mobile and stationary phases change to reflect the growing heterogeneity in mobile phase component distribution at the chromatographic interface. The stationary phase loses many of its original characteristics and takes on characteristics typical of the mobile phase modifier due to preferential adsorption of the modifier at the surface of the stationary phase.     

Establishing linear solvation energy relationships between VOCs and monolayer-protected gold nanoclusters using quartz crystal microbalance

Linear solvation energy relationships (LSERs) have been recognized as a useful model for investigating the chemical forces behind the partition coefficients between vapor molecules and absorbents. This study is the first to determine the solvation properties of monolayer-protected gold nanoclusters (MPCs) with different surface ligands. The ratio of partition coefficients/MPC density (K/ρ) of 18 volatile organic compounds (VOCs) for four different MPCs obtained through quartz crystal microbalance (QCM) experiments were used for the LSER model calculations. LSER modeling results indicate that all MPC surfaces showed a statistically significant (p < 0.05) preference to hydrogen-bond acidic molecules. Through dipole-dipole attraction, 4-methoxythiophenol-capped MPCs can also interact with polar organics (s = 1.04). Showing a unique preference for the hydrogen bond basicity of vapors (b = 1.11), 2-benzothiazolethiol-capped MPCs provide evidence of an intra-molecular, proton-shift mechanism on surface of nano-gold.     

Correlation of gas–liquid partition coefficients using a generalized linear solvation energy relationship

In a number of previous communications, we reported on the utility of the solvatochromic linear solvation energy relationship (LSER) method for the correlation of a number of solute and solvent-dependent properties. In those studies, it was our practice to examine the effect of a variety of solvents on a given solute or a number of solutes in a given solvent. Here we report on a novel generalized LSER in which the solute and solvent were both simultaneously varied so as to assess the validity of the entire LSER concept and define its limits. The Hildebrand solubility parameter, δ_H, the Kamlet–Taft solvatochromic parameters, π*, α, β and the solute molar volume, V₂, were used as the explanatory variables. The gas–liquid partition coefficient (K) was the property of interest. We have found that the correlation using the generalized linear solvation energy relationship is statistically as good as the previous LSER correlations despite the use of a far smaller number of freely adjustable parameters. Furthermore, the new approach is able to give reasonable predictions of K values of systems not included in the data set upon which the regression is based.     

Characterization of Ascentis RP-Amide column: Lipophilicity measurement and linear solvation energy relationships

This study investigates lipophilicity determination by chromatographic measurements using the polar embedded Ascentis RP-Amide stationary phase. As a new generation of amide-functionalized silica stationary phase, the Ascentis RP-Amide column is evaluated as a possible substitution to the n  -octanol/water partitioning system for lipophilicity measurements. For this evaluation, extrapolated retention factors, log k^′_w$\text{log}{k^{\prime }}_w$, of a set of diverse compounds were determined using different methanol contents in the mobile phase. The use of n-octanol enriched mobile phase enhances the relationship between the slope (S  ) of the extrapolation lines and the extrapolated log k^′_w$\text{log}{k^{\prime }}_w$ (the intercept of the extrapolation), as well as the correlation between log P   values and the extrapolated log k^′_w$\text{log}{k^{\prime }}_w$ (1:1 correlation, r² = 0.966). In addition, the use of isocratic retention factors, at 40% methanol in the mobile phase, provides a rapid tool for lipophilicity determination. The intermolecular interactions that contribute to the retention process in the Ascentis RP-Amide phase are characterized using the solvation parameter model of Abraham. The LSER system constants for the column are very similar to the LSER constants of the n-octanol/water extraction system. Tanaka radar plots are used for quick visual comparison of the system constants of the Ascentis RP-Amide column and the n-octanol/water extraction system. The results all indicate that the Ascentis RP-Amide stationary phase can provide reliable lipophilic data.     

Investigation of sport supplements quality by Raman spectroscopy and principal component analysis

In this work, sport supplements were investigated by Raman spectroscopy. Samples were obtained from health foods shops, gyms and sports centers covering a wide range of available supplement powders. A systematic comparison of Raman spectra of the analyzed supplements allowed identifying the supplement type through the characteristic vibrational modes of carbohydrates and proteins. The protein supplements were identified by Raman bands at 1650, 1250 and 1004 cm⁻¹, while the spectral range between 1200 and 800 cm⁻¹ was useful to identify the carbohydrate supplements. Due to the diversity in composition of sport supplements, a chemometric tool such as principal component analysis (PCA) was employed to assist in the interpretation of Raman spectra, allowing also the identification of compounds present in sport supplements. Especially, the Raman scattering of aromatic and aliphatic amino acids residues contributes to the existence of bands characteristic for the different types of proteins. This kind of information is very important for the quality control of these products, for detecting the presence of fraud or a sample composition in disagreement with the label, thus ensuring the provenance of the supplements.     

Characterization of some new polysiloxane stationary phases by principal component analysis

Summary In this study, several new stationary phases were characterized by principal component analysis. Fourteen new stationary phases, including substituted phenyl and oligoethyleneoxide functionalities on polysiloxane polymers, were tested and compared to three well known stationary phases. The main features of these phases were studied using a series of test solutes of varying chemical characteristics representing the data set for principal component analysis. Two principal compounds were found to account for 99.20% of the variance (the first accounted for 94.96% and the second for 4.24%). The data were represented as a two-dimensional map for visual representation of the characteristics of these stationary phases. The first principal component represented a selectivity based on polarity (r²=0.998), while the second showed Lewis acid-base characteristics of the phases. Polarizable and amphoteric characteristics of these phases also became evident using this evaluation method.     

Characterization of stationary phases by a linear solvation energy relationship utilizing supercritical fluid chromatography

Supercritical fluid chromatography was utilized in combination with the Abraham model of linear solvation energy relationship to characterize 11 different HPLC stationary phases. System constants were determined at one supercritical fluid chromatography condition for each stationary phase. The results indicate that several types of silica columns, including type B silica, type C silica, and fused core silica, are very similar in their retention behavior. Several aromatic stationary phases were characterized and it was found that, in contrast to the other phases studied, all of the aromatic stationary phases had positive contributions from the dispersion/cavity (v) term of the linear solvation energy relationship. Several aliphatic phases were characterized and there were several linear solvation energy relationship constants that differentiated the phases from each other, mainly the polar terms (dipolarity and hydrogen bonding). One stationary phase, a fused core pentafluorophenyl (PFP) phase, had very poor regression quality. The column volume of this phase was lower than the others in the study, which may have had some impact on the results of the regression.     

Characterization of biopartitioning micellar chromatography system using monolithic column by linear solvation energy relationship and application to predict blood–brain barrier penetration

The linear solvation energy relationship (LSER) was applied to characterize biopartitioning micellar chromatography (BMC) system using monolithic column, and was utilized to compare the above system with other physicochemical and biological processes in this study. The solute volume and HB basicity had the maximum influence on the retention of the solutes, and an increase in the dipolarity/polarizability, HB basicity, HB acidity or excess molar refraction of the solutes decreased the retention. Principal component analysis of LSER coefficients showed that the system had certain similarity to drug biomembrane transport processes, such as blood–brain barrier penetration, transdermal and oral absorption. The quantitative retention–activity relationship (QRAR) of drug penetration across blood–brain barrier was established and its predictive capability for this biological process was evaluated. With the aid of the high flow rate, the monolithic column significantly facilitated the high-throughput analysis of large compounds’ bank without changing the mechanism of the retention in BMC and without impairing good predictive capability of the biological processes. Accordingly, the BMC system, together with monolithic column, allows for high-throughput profiling the biological processes, such as blood–brain barrier penetration.     

Prediction of internal standards in reversed-phase liquid chromatography: IV. Correlation and prediction of retention in reversed-phase ion-pair chromatography based on linear solvation energy relationships

This paper describes the results of the evaluation of a new solvation parameter model for reversed-phase ion-pair chromatography by linear gradient elution. This model is described as . The first six terms are the usual solvation parameter equation for neutral solutes, and the seventh term represents the contribution to retention from solute’s ionization. The last term describes the retention increase due to ion-pair effect. Retention times obtained for 60 solutes (neutral, acidic and basic) in acetonitrile/aqueous mobile phases with different ion-pair reagents (phosphoric acid, trifluoroacetic acid, heptafluorobutyric acid, perchloric acid, and hexafluorophosphoric acid) are used to evaluate the capability of the function. It is concluded that the model describes the retention of ionizable/ionized compounds under ion-pair conditions very well. Accordingly, the function extends the application of linear solvation energy relationships (LSERs) to ionizable compounds in ion-pair chromatography, and allows us to easily predict their retention for chromatographic optimization, including selectivity optimization and internal standard selection. Finally, the conclusion can be extended to ioscratic elution.     

Gas chromatographic evaluation of wool wax alcohols supported by principal component analysis

Summary Capillary gas chromatography with flame ionization detection has been applied to the separation of the components of wool wax alcohols. Twenty-six commercial and non-commercial (laboratory) samples were investigated. Twenty-eight components found in the samples were used as variables for further characterization by a chemometric procedure. Principal component analysis was applied to the differentiation of samples from different sources and obtained by different technologies.     

Comparision of varuious bulk fluids and modifiers as near-crtical mobile phases on a polymeric column using linear solvatio energy realationships

Linear solvation energy realationships (LSERS) were used to qunatitatively compare the relative contributions of dipolarity/polarizability, hydrogen bonding, and other types of intermolecular interactions to retention and selectivity using various bulk moblie phase component. Using experimental condition which differ only by the compostion of the bulk moble phase component, the factors which cause selctivity to differ between HPLC, subcritical fluid chromatography and supercritical fluid charomatography were decovoluted. Heptance-based HPLC mobile phases showed superior selectivities towards analytes which differ in hydrogen bond donating ability, gas-to-hexadecane partion coefficint and dipolarity/ploarizability. subcritical fluid chromatography with HFC-134a (1, 1, 1, 2-tetrafluoroethane) as the bulk fluid produced superior seclectivities for which differ in hydrogen bond accepting ability and execess molar refraction properties. Many of these factors showed temperature dependences which act to attenuate or accenture the particular intermolcular interaction.     

青海生活饮用水中矿质元素含量检测以及主成分分析

为了深入了解青海生活饮用水中矿质元素存在的现状,并对饮用水资源高效开发利用与保护提供一定的科学基础,采用微波消解-电感耦合等离子体质谱(ICP-MS)法测定了青海20个地区的生活饮用水中17种矿质元素K、Ca、Mg、Na、Fe、Mn、Zn、Al、Cu、Pb、Cd、Ge、As、Hg、Cr、Co和Se的含量,并对测定结果进行主成分分析。结果表明,17种元素的相对标准偏差为1.5%～3.9%,加标回收率是97.6%～101%。此外,20个地区的生活饮用水中富含及其丰富的微量元素,其中德令哈市饮用水的钙含量达到(32.75±0.29) mg/L,海东市乐都区的饮用水中Se含量达到(1.09±0.01) mg/L,有潜在生理毒性的元素如Pb、Cd、As、Hg、Cr的含量较低。在此基础上,将水样17种元素进行主成分分析降维得到7个线性不相关主成分,7个主成分方差贡献率分别为19.69%、16.05%、11.02%、10.41%、8.28%、7.39%、6.52%,合计贡献率达79.36%,可综合初始数据的绝大部分信息。通过雷达图分析,每个主成分在20个地区间的差异显著。每个主成分综合的元素信息各不...     

A random version of principal component analysis in data clustering

Principal component analysis (PCA) is a widespread technique for data analysis that relies on the covariance/correlation matrix of the analyzed data. However, to properly work with high-dimensional data sets, PCA poses severe mathematical constraints on the minimum number of different replicates, or samples, that must be included in the analysis. Generally, improper sampling is due to a small number of data respect to the number of the degrees of freedom that characterize the ensemble. In the field of life sciences it is often important to have an algorithm that can accept poorly dimensioned data sets, including degenerated ones. Here a new random projection algorithm is proposed, in which a random symmetric matrix surrogates the covariance/correlation matrix of PCA, while maintaining the data clustering capacity. We demonstrate that what is important for clustering efficiency of PCA is not the exact form of the covariance/correlation matrix, but simply its symmetry.     

Application of a quartz crystal nanobalance and principal component analysis for the detection and determination of histidine

The aim of the present investigation was to develop a biosensor based on a quartz crystal nanobalance (QCN) for the detection of histidine (His). A thin layer of nickel was electrochemically deposited over the gold crystal electrode and exposed to H₂O₂ to form nickel oxide. The composite electrode was then used to determine His. The frequency shifts were linear with respect to the concentration of His in solution. His can be measured in the range of 100–2000 mg L⁻¹. A lower limit of detection of 48 mg L⁻¹ and a sensitivity factor of 0.0307 Hz/mg L⁻¹ was obtained. Some possible interferences were checked for, and the performance of the sensor was found to be unaffected by any interference except for those from arginine, cysteine and NaH₂PO₄. Principal component analysis (PCA) was used to process the frequency response data of the single piezoelectric crystal at various times, considering the different adsorption–desorption dynamics of His and the interfering compounds. Over 85% of the variance in the data was explained by two principal components. A score plot of the data for the first two PCs showed that the modified QCN yields favorable identification and quantification performances for His and the interfering compounds.     

Applications of maximum likelihood principal component analysis: incomplete data sets and calibration transfer

The application of a new method to the multivariate analysis of incomplete data sets is described. The new method, called maximum likelihood principal component analysis (MLPCA), is analogous to conventional principal component analysis (PCA), but incorporates measurement error variance information in the decomposition of multivariate data. Missing measurements can be handled in a reliable and simple manner by assigning large measurement uncertainties to them. The problem of missing data is pervasive in chemistry, and MLPCA is applied to three sets of experimental data to illustrate its utility. For exploratory data analysis, a data set from the analysis of archeological artifacts is used to show that the principal components extracted by MLPCA retain much of the original information even when a significant number of measurements are missing. Maximum likelihood projections of censored data can often preserve original clusters among the samples and can, through the propagation of error, indicate which samples are likely to be projected erroneously. To demonstrate its utility in modeling applications, MLPCA is also applied in the development of a model for chromatographic retention based on a data set which is only 80% complete. MLPCA can predict missing values and assign error estimates to these points. Finally, the problem of calibration transfer between instruments can be regarded as a missing data problem in which entire spectra are missing on the ‘slave’ instrument. Using NIR spectra obtained from two instruments, it is shown that spectra on the slave instrument can be predicted from a small subset of calibration transfer samples even if a different wavelength range is employed. Concentration prediction errors obtained by this approach were comparable to cross-validation errors obtained for the slave instrument when all spectra were available.     

Determination of the lipophilicity of Salvia miltiorrhiza Radix et Rhizoma (danshen root) ingredients by microemulsion liquid chromatography: optimization using cluster analysis and a linear solvation energy relationship‐based method

We evaluated 26 microemulsion liquid chromatography (MELC) systems for their potential as high‐throughput screening platforms capable of modeling the partitioning behaviors of drug compounds in an n‐octanol–water system, and for predicting the lipophilicity of those compounds (i.e. logP values). The MELC systems were compared by cluster analysis and a linear solvation energy relationship (LSER)‐based method, and the optimal system was identified by comparing their Euclidean distances with the LSER coefficients. The most effective MELC system had a mobile phase consisting of 6.0% (w/w) Brij35 (a detergent), 6.6% (w/w) butanol, 0.8% (w/w) cyclohexane, 86.6% (w/w) buffer solution and 8 mm cetyltrimethyl ammonium bromide. The reliability of the established platform was confirmed by the agreement between the experimental data and the predicted values. The logP values of the ingredients of danshen root (Salvia miltiorrhiza Radix et Rhizoma) were then predicted. Copyright © 2015 John Wiley & Sons, Ltd.