Study of the Interactions of Ionic Liquids in IC by QSRR

The nature of ionic liquids (ILs) facilitates their analysis by ion chromatography which, unlike conventional high-performance liquid chromatography, enables analysis both of cations and anions. This paper describes a pioneering ion-chromatographic investigation of IL cations and statistical evaluation of quantitative structure–retention relationships with the objective of predicting the molecular mechanism responsible for retention. Eleven ionic liquid imidazolium and pyridinium cations were analyzed on a CS15 cation-exchange column by isocratic elution with acetonitrile–methanesulfonic acid mixtures. Structural descriptors of the cations obtained from molecular modeling were used to describe their hydrophobicity as determined by chromatography. The most statistically significant were three-term QSRR regression equations relating log kw to analyte n-octanol–water partition coefficient (log P), dipole moment (μ), solvent accessible surface area (ASAS), and hydration energy (HE). They indicate the important role of both hydrophobic and polar the retention of ILs on the CS15 column

A preliminary examination of differential decomposition patterns in mass graves

This study represents a preliminary, quantitative approach to the examination of differential decomposition patterns in mass graves. Five pairs of mass graves, each containing the carcasses of 21 rabbits, were used to examine decomposition rates at four fixed positions within the burial. A pair of graves was exhumed at approximately 100 accumulated degree day (ADD) intervals. At exhumation the total body score (TBS) and internal carcass temperature of each rabbit were recorded. Although there was no significant difference between decomposition rates for core and deep-positioned carcasses (p = 0.13), all other position differences were significant (p < 0.001). Decomposition occurred fastest in shallow carcasses, followed by mid-outer carcasses; both deep and core carcasses exhibited a slower rate. Internal carcass temperature was significantly influenced by carcass location within the mass grave; there was a mean internal temperature difference of ca. 1 oC between deep and shallow carcasses (30 cm apart). Adipocere formation was minimal and confined, with the exception of a single individual in the mid- periphery, to the deepest level. Decomposition rate may be as affected by the compactness of a mass as by interment depth and/or peripheral substrate contact, and further investigation into the role of oxygenation and pH are required

Semi-Empirical Topological Method for Prediction of the Relative Retention Time of Polychlorinated Biphenyl Congeners on 18 Different HR GC Columns

High resolution gas chromatographic relative retention time (HRGC-RRT) models were developed to predict relative retention times of the 209 individual polychlorinated biphenyls (PCBs) congeners. To estimate and predict the HRGC-RRT values of all PCBs on 18 different stationary phases, a multiple linear regression equation of the form RRT = ao + a1 (no. o-Cl) + a2 (no. m-Cl) + a3 (no. p-Cl) + a4 (VM or SM) was used. Molecular descriptors in the models included the number of ortho-, meta-, and para-chlorine substituents (no. o-Cl, m-Cl and p-Cl, respectively), the semi-empirically calculated molecular volume (VM), and the molecular surface area (SM). By means of the final variable selection method, four optimal semi-empirical descriptors were selected to develop a QSRR model for the prediction of RRT in PCBs with a correlation coefficient between 0.9272 and 0.9928 and a leave-one-out cross-validation correlation coefficient between 0.9230 and 0.9924 on each stationary phase. The root mean squares errors over different 18 stationary phases are within the range of 0.0108–0.0335. The accuracy of all the developed models were investigated using cross-validation leave-one-out (LOO), Y-randomization, external validation through an odd–even number and division of the entire data set into training and test sets

Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics

<p>Abstract</p> <p>Background</p> <p>High-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination with database searching algorithms. A major problem with existing methods lies within the significant number of false positive and false negative annotations. So far, standard algorithms for protein identification do not use the information gained from separation processes usually involved in peptide analysis, such as retention time information, which are readily available from chromatographic separation of the sample. Identification can thus be improved by comparing measured retention times to predicted retention times. Current prediction models are derived from a set of measured test analytes but they usually require large amounts of training data.</p> <p>Results</p> <p>We introduce a new kernel function which can be applied in combination with support vector machines to a wide range of computational proteomics problems. We show the performance of this new approach by applying it to the prediction of peptide adsorption/elution behavior in strong anion-exchange solid-phase extraction (SAX-SPE) and ion-pair reversed-phase high-performance liquid chromatography (IP-RP-HPLC). Furthermore, the predicted retention times are used to improve spectrum identifications by a <it>p</it>-value-based filtering approach. The approach was tested on a number of different datasets and shows excellent performance while requiring only very small training sets (about 40 peptides instead of thousands). Using the retention time predictor in our retention time filter improves the fraction of correctly identified peptide mass spectra significantly.</p> <p>Conclusion</p> <p>The proposed kernel function is well-suited for the prediction of chromatographic separation in computational proteomics and requires only a limited amount of training data. The performance of this new method is demonstrated by applying it to peptide retention time prediction in IP-RP-HPLC and prediction of peptide sample fractionation in SAX-SPE. Finally, we incorporate the predicted chromatographic behavior in a <it>p</it>-value based filter to improve peptide identifications based on liquid chromatography-tandem mass spectrometry.</p

The state-of-the-art determination of urinary nucleosides using chromatographic techniques “hyphenated” with advanced bioinformatic methods

Over the last decade metabolomics has gained increasing popularity and significance in life sciences. Together with genomics, transcriptomics and proteomics, metabolomics provides additional information on specific reactions occurring in humans, allowing us to understand some of the metabolic pathways in pathological processes. Abnormal levels of such metabolites as nucleosides in the urine of cancer patients (abnormal in relation to the levels observed in healthy volunteers) seem to be an original potential diagnostic marker of carcinogenesis. However, the expectations regarding the diagnostic value of nucleosides may only be justified once an appropriate analytical procedure has been applied for their determination. The achievement of good specificity, sensitivity and reproducibility of the analysis depends on the right choice of the phases (e.g. sample pretreatment procedure), the analytical technique and the bioinformatic approach. Improving the techniques and methods applied implies greater interest in exploration of reliable diagnostic markers. This review covers the last 11 years of determination of urinary nucleosides conducted with the use of high-performance liquid chromatography in conjunction with various types of detection, sample pretreatment methods as well as bioinformatic data processing procedures

Hydrophilic interaction liquid chromatography (HILIC)—a powerful separation technique

Hydrophilic interaction liquid chromatography (HILIC) provides an alternative approach to effectively separate small polar compounds on polar stationary phases. The purpose of this work was to review the options for the characterization of HILIC stationary phases and their applications for separations of polar compounds in complex matrices. The characteristics of the hydrophilic stationary phase may affect and in some cases limit the choices of mobile phase composition, ion strength or buffer pH value available, since mechanisms other than hydrophilic partitioning could potentially occur. Enhancing our understanding of retention behavior in HILIC increases the scope of possible applications of liquid chromatography. One interesting option may also be to use HILIC in orthogonal and/or two-dimensional separations. Bioapplications of HILIC systems are also presented

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules