1,721,022 research outputs found
Retention modelling of polychlorinated biphenyls in comprehensive two-dimensional chromatography
No full text"In this paper, we use a quantitative structure-retention relationship (QSRR) method to predict the retention times of polychlorinated biphenyls (PCBs) in comprehensive two-dimensional gas chromatography (GCxGC). We analyse the GCxGC retention data taken from the literature by comparing predictive capability of different regression methods. The various models are generated using 70 out of 209 PCB congeners in the calibration stage, while their predictive performance is evaluated on the remaining 139 compounds. The two-dimensional chromatogram is initially estimated by separately modelling retention times of PCBs in the first and in the second column ((1) t (R) and (2) t (R), respectively). In particular, multilinear regression (MLR) combined with genetic algorithm (GA) variable selection is performed to extract two small subsets of predictors for (1)t(R) and (2)t(R) from a large set of theoretical molecular descriptors provided by the popular software Dragon, which after removal of highly correlated or almost constant variables consists of 237 structure-related quantities. Based on GA-MLR analysis, a four-dimensional and a five-dimensional relationship modelling (1)t(R) and (2)t(R), respectively, are identified. Single-response partial least square (PLS-1) regression is alternatively applied to independently model (1)t(R) and (2)t(R) without the need for preliminary GA variable selection. Further, we explore the possibility of predicting the two-dimensional chromatogram of PCBs in a single calibration procedure by using a two-response PLS (PLS-2) model or a feed-forward artificial neural network (ANN) with two output neurons. In the first case, regression is carried out on the full set of 237 descriptors, while the variables previously selected by GA-MLR are initially considered as ANN inputs and subjected to a sensitivity analysis to remove the redundant ones. Results show PLS-1 regression exhibits a noticeably better descriptive and predictive performance than the other investigated approaches. The observed values of determination coefficients for (1)t(R) and (2)t(R) in calibration (0.9999 and 0.9993, respectively) and prediction (0.9987 and 0.9793, respectively) provided by PLS-1 demonstrate that GCxGC behaviour of PCBs is properly modelled. In particular, the predicted two-dimensional GCxGC chromatogram of 139 PCBs not involved in the calibration stage closely resembles the experimental one. Based on the above lines of evidence, the proposed approach ensures accurate simulation of the whole GCxGC chromatogram of PCBs using experimental determination of only 1/3 retention data of representative congeners.
Quantitative structure/eluent-retention relationships in reversed-phase high-performance liquid chromatography based on the solvatochromic method.
No full text"Some predictive approaches aimed at modelling the combined effect of solute molecular structure and mobile phase composition on retention in reversed-phase high-performance chromatography (RP-HPLC) have been developed in the literature. These models are established for a given binary eluent (normally acetonitrile-water or methanol-water) by non-linear (curvilinear or artificial neural network) regression assuming as the mobile phase descriptor the volume fraction phi of the organic modifier. In the present investigation, we propose a model applicable simultaneously to acetonitrile-water and methanol-water eluents. To this end, the Kamlet-Taft solvatochromic descriptors of the eluent and the solvatochromic descriptors of the analytes are considered as the input variables of a multi-layer artificial neural network (ANN) providing the solute retention as the response. This approach is applied to a set of 31 molecules analyzed with five different columns in the phi range 20-70 % at 10 % steps for both acetonitrile- and methanol-containing mobile phases. For each column, an ANN-based model is built using retention data of 25 molecules selected by the Kennard-Stones algorithm while retention data of the unselected six solutes are considered in the final evaluation of predictive performance of the trained network. To test cross-eluent prediction, the network optimized for a given column was successively trained with data collected in eight out of 12 eluents and applied to deduce retention in the four remaining mobile phases. The results reveal that RP-HPLC behavior of external solutes is quite accurately modelled in the whole explored composition range of acetonitrile- and methanol-water mobile phases. Moreover, the model exhibits a promising capability of deducing retention of external solutes even in unknown eluents.
Cross-column prediction of gas-chromatographic retention of polychlorinated biphenyls by artificial neural networks
No full textIn this paper, we build a multiple-column retention model able to predict the behaviour of polychlorinated biphenyls (PCBs) in capillary gas-chromatography (CC) within a wide range of separation conditions. To this end, GC retention is related to both chemical structure of PCBs, encoded by selected theoretical molecular descriptors, and the kind of stationaiy phase, represented by the relative retention time (RRT) of a suitable small number of analytes. The model was generated using the retention data of 70 PCBs extracted from the pool of the 209 possible congeners collected on 17 different capillary columns featured by non-polar or moderately polar stationary phases, reported in the literature [20]. Multilinear regression combined with genetic algorithm variable selection was preliminarily applied to generate a four-dimensional quantitative structure-retention relationship (QSRR) for each of the 17 columns, based on theoretical molecular descriptors extracted from the large set provided by the software Dragon. 33 molecular descriptors obtained by merging the non-common descriptors of various single-column QSRRs, combined with RRTs values of the less and the most retained PCB, were considered as the starting independent variables of the multiple-column retention model. A multi-layer artificial neural network (ANN), optimised on a validation set extracted from the calibration data, was applied to generate the multi-column retention model. The influence of starting inputs on the network output was evaluated by a sensitivity analysis and model complexity was reduced through a step-wise elimination of redundant molecular descriptors, while RRTs of further PCBs were included to improve description of the stationary phase. Nine molecular descriptors and RRTs of eight selected PCBs are considered as the independent variables of the final ANN-based model, whose predictive performance was tested on the 139 PCBs excluded from calibration and on six external columns and/or temperature programs. (C) 2011 Elsevier B.V. All rights reserved
Stratigraphic and Geochemical Study of Clastic Sediments of the Grotte di Stiffe Karstic System
No full textThe present work reports the results of a geochemical and stratigraphic study carried out on clastic sediments from the karstic system of the Grotte di Stiffe in Abruzzo. In order to describe in more significant manner the system from which the sediments have been taken, the rocks from which they derive and ground samples from the plateau immediately above the caves have also been examined. Stratigraphic and chemical studies, with particular regard to the presence of heavy metals, have been performed on some 50 cm of drawn sediment, utilising atomic absorption spectrophotometry. The data obtained in this way have been subjected to Principal Components Analysis in order to investigate possible correlations
Development of a liquid chromatographic method for the determination of sildenafil in seminal plasma
No full textA sensitive high performance liquid chromatographic (HPLC) method with ultraviolet absorption detection (230 nm) was developed and validated for the determination of a phosphodiesterase V inhibitor, Sildenafil, in seminal plasma. A single step liquid-liquid extraction procedure using ethyl acetate was performed to recover sildenafil from 1.0 mL of seminal plasma combined with 200 muL of NaOH 0.1 M. A symmetry C-18 column (150 x 4.6 mm I.D. 5 mum) was used as a stationary phase and the mobile phase consisted of 32% acetonitrile and 68% phosphoric acid (0.016 M; pH 5.3) at a flow rate of 1.0 mL/min. The quantitation limit was 5 ng/mL. Intra- and inter-day relative standard deviation (RSD) did not exceed 6.6%. This HPLC method has been successfully used in medical laboratories to assay seminal plasma samples for studies on the treatment with sildenafil
Artificial neural network prediction of multilinear gradient retention in reversed-phase HPLC: comprehensive QSRR-based models combining categorical or structural solute descriptors and gradient profile parameters
No full textCross-column prediction of gas-chromatographic retention indices of saturated esters
No full textWe combine computational molecular descriptors and variables related with the gas-chromatographicstationary phase into a comprehensive model able to predict the retention of solutes in external columns.To explore the quality of various approaches based on alternative column descriptors, we analyse theKováts retention indices (RIs) of 90 saturated esters collected with seven columns of different polarity (SE-30, OV-7, DC-710, OV-25, XE-60, OV-225 and Silar-5CP). Cross-column retention prediction is evaluatedon an internal validation set consisting of data of 40 selected esters collected with each of the sevencolumns, sequentially excluded from calibration. The molecular descriptors are identified by a geneticalgorithm variable selection method applied to a large set of non-empirical structural quantities aimed atfinding the best multi-linear quantitative structure–retention relationship (QSRR) for the column OV-25having intermediate polarity. To describe the columns, we consider the sum of the first five McReynoldsphase constants and, alternatively, the coefficients of the corresponding QSRRs. Moreover, the mean RIvalue for the subset of esters used in QSRR calibration or RIs of a few selected compounds are usedas column descriptors. For each combination of solute and column descriptors, the retention model isgenerated both by multi-linear regression and artificial neural network regression
Adsorption of s-triazines onto polybenzimidazole: A quantitative structure-property relationship investigation
No full textThe adsorption of 25 symmetric triazines (s-triazines) on polybenzimidazole (PBI) beads is investigated under equilibrium (batch) conditions. The observed adsorption isotherms of the selected compounds are accurately described by the Freundlich model, while the agreement between the Langmuir model and the experimental data is moderately worse, which seems to reflect the heterogeneous meso- and microporosity of PBI and polydispersion in the interaction mechanism. Methylthio- and methoxytriazines exhibit a greater adsorption tendency as compared with chlorotriazines, moreover, progressive dealkylation of amino groups results in a progressive increase of triazine uptake on PBI. Based on these evidences, the adsorption mechanism seems to be governed by a combination of pi-pi and hydrogen-bonding interactions. Genetic algorithm (GA) variable selection and multilinear regression (MLR) are combined in order to describe the effect of triazine structure on the extraction performance of PBI according to the quantitative structure-property relationship (QSPR) method. q(max), the amount of triazine adsorbed per weight unit of PBI assuming homogeneous monolayer (Langmuir) mechanism, exhibits a great variability within the set of investigated triazines and is the quantity here modelled by QSPR. On the other hand, the Freundlich constant, K(F), which expresses the adsorption efficiency under multilayer heterogeneous conditions, even if markedly increases passing from chloro- to methylthio- or methoxytriazines, is less noticeably affected by the fine details of the adsorbate structure, as the number or nature of alkyl fragments bound to the amino groups. To quantitatively relate q(max) with the triazine structure GA-MLR analysis is performed on the set of 1664 theoretical molecular descriptors provided by the software Dragon. Finally. a four-dimensional QSPR model is selected based on leave-one-out cross-validation and its prediction ability is further tested,on four representative triazines excluded from model calibration. The four descriptors selected by CA-MLR, all belonging to the class of three-dimensional GETAWAY (GEometry, Topology, and Atom-Weights AssemblY) descriptors, adequately represent the structural factors influencing the affinity of triazines to PBI in the batch extraction process. (C) 2009 Elsevier B.V. All rights reserved
Investigation by Response Surface Methodology of Extraction of Caffeine, Gallic Acid and Selected Catechins from Tea Using Water-Ethanol Mixtures
No full textThe simultaneous influence of pH and composition of water–ethanol mixtures on the extraction from tea of caffeine (CF), gallic acid (GA) and the selected catechins epicatechin (EC), epicatechin-3-gallate (ECG) and epigallocatechin-3-gallate (EGCG) is investigated by response surface methodology. Extraction experiments are carried out at room temperature according to a three-level full-factorial design in which pH, measured before mixing with ethanol, is varied between 6 and 8 and volume fraction of ethanol is varied between 30 and 70 % v/v. Response surfaces are determined by fitting of extracted amounts of the above substances, determined by HPLC analysis, with a second-degree polynomial model. Within the investigated experimental domain, extraction efficiency of CF is substantially the same and extraction of ECG and EGCG is not affected by acidity of the medium while both pH and composition influence the extraction of EC and GA
N-Doped TiO2 Nanofibers Deposited by Electrospinning
No full text"N-doped TiO2 nanofibers were deposited by electrospinning onto silicon and then annealed in air. They have been analyzed by secondary electron microscopy, X-ray diffraction, and X-ray photo:mission. After the thermal processes, the fibers, having a diameter between 300 and 400 nm, contain a small amount of nitrogen and still;how the TiO2 anatase crystalline structure. Different solutions and annealing processes were used to vary the nitrogen concentration and the crystalline phase. These nanofibers were successfully tested as photocatalytic devices for methylene blue degradation under visible light irradiation.
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