1,720,979 research outputs found
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 textGoing Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Cross-column prediction of gas-chromatographic retention of polybrominated diphenyl ethers
No full textArtificial neural network modelling of retention of pesticides in various octadecylsiloxane-bonded reversed-phase columns and water-acetonitrile mobile phase
No full textPreviously, retention of 26 pesticides in the reversed-phase column Gemini (Phenomenex) and
water–acetonitrile mobile phase was modelled using a feed-forward artificial neural network (ANN)
learned by error back-propagation, accounting for both the effect of solute structure and mobile phase
composition. To this end, log Kow of solutes and four quantum chemical molecular descriptors (the dipole
moment, the mean polarizability, the anisotropy of the polarizability and an hydrogen-bonding descriptor
based on the atomic charges located on the acid and basic functional groups) and acetonitrile % (v/v)
in the eluent (%ACN) were used as ANN inputs. The above ANN-based approach is here tested on further
five similar octadecylsiloxane-bonded columns inwater–acetonitrile mobile phase within the %ACN
range 30–70%. A quite good predictive performance evaluated on three external solutes in the whole
%ACN range is observed, prediction errors being lower than ±0.1 log k units or slightly higher although
still within ±0.15 log k units. On the other hand, multilinear regression used in place of ANN provides
a more diffuse and non-uniform residual distribution for all the investigated columns. ANN multiplecolumn
retention prediction is attempted by adding to the above variables a column descriptor defined
as the average retention of calibration solutes extrapolated to 100% water. This more general model is
built using 16 solutes and five 5-mcolumns in calibration, while its predictive performance is tested on
the remaining 10 compounds. Under these conditions, prediction errors are generally within ±0.2 log k
units regardless of the kind of column. The possibility of cross-column prediction is evaluated by column
leave-one-out cross-validation within the five 5-m stationary phases and on a 4-m external column.
This analysis reveals that accuracy of retention prediction for unknown solutes in unknown columns is
acceptable provided that the external column is not very dissimilar to those used in calibration
Cross-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
Prediction of the retention of s-triazines in reversed-phase high-performance liquid chromatography under linear gradient-elution conditions
No full textIn this paper, a multilayer artificial neural network is used to model simultaneously the
effect of solute structure and eluent concentration profile on the retention of s-triazines
in reversed-phase high-performance liquid chromatography under linear gradient elution.
The retention data of 24 triazines, including common herbicides and their metabolites, are
collected under 13 different elution modes, covering the following experimental domain:
starting acetonitrile volume fraction ranging between 40 and 60% and gradient slope ranging
between 0 and 1% acetonitrile/min. The gradient parameters together with five selected
molecular descriptors, identified by quantitative structure-retention relationship modelling
applied to individual separation conditions, are the network inputs. Predictive performance
of this model is evaluated on six external triazines and four unseen separation conditions.
For comparison, retention of triazines is modelled by both quantitative structure–retention
relationships and response surface methodology, which describe separately the effect of
molecular structure and gradient parameters on the retention. Although applied to a wider
variable domain, the network provides a performance comparable to that of the above “local”
models and retention times of triazines are modelled with accuracy generally better than
7%
Multiple-column RP-HPLC retention modelling based on solvatochromic or theoretical solute descriptors
No full textRecently, we have proposed an approach to multi-column RP-HPLC retention modelling under isocratic conditions based on a combination of five molecular descriptors, the volume fraction of organic modifier in the mobile phase and a column descriptor, simultaneously considered as dependent variables of artificial neural network (ANN) regression. The column descriptor, in particular, was identified with the observed average retention of the solutes used in calibration extrapolated to pure water as the mobile phase. The ANN-based model was seen to accurately describe retention on a pool of octadecylsiloxane-bonded (C(18)) columns in a wide range of mobile phase composition. Reliability of this approach is further examined here by analysing the retention data of Reta et al. (Anal. Chem. 1999, 71, 3484-3496) referring to 17 aromatic compounds collected in water-methanol mobile phases at the compositions 45, 50, 55 and 60%v/v of methanol with eight different columns based on various hydrocarbon, fluorocarbon and aromatic bonded stationary phases. Further, in this study we compare the explanatory capability of two different kinds of molecular descriptors: the well-known solvatochromic descriptors and theoretical descriptors extracted by genetic algorithm variable selection from the large set provided by the popular software Dragon
Cross-column retention prediction in reversed-phase high-performance liquid chromatography by artificial neural network modelling
No full text"Linear solvation energy relationships (LSERs) are commonly applied to model the effect of solute structure on the retention of analytes in reversed-phase high-performance liquid chromatography (RP-HPLC). Standard LSER approaches can be used, in principle, to predict RP-HPLC behaviour of unknown analytes under fixed separation condition. However, as solute structure is the only source of variability described by the model, a LSER established for a given column\/eluent pair cannot be transferred to external separation conditions. In the present investigation, we attempt cross-column prediction by combining in the same model usual LSER molecular descriptors with observed retentions of selected solutes within the calibration set, adopted to represent the stationary phase features. A multi-layer artificial neural network (ANN) is used as regression tool to model the combined effect of solute structure and column on retention. This model is generated and validated using literature retention data of 34 solutes collected on 15 different RP-HPLC columns at a fixed eluent composition (acetonitrile-water 30:70, v\/v). The calibration set is designed by selecting 25 solutes and 11 columns able to represent the variability of the chemical structure of the investigated compounds and dissimilarity of the stationary phases of the data set, respectively. The final predictive performance of the optimised ANN model is tested on the four columns excluded from calibration. Retention of the 25 solutes used to train the network and that of the nine unknown molecules on the external stationary phases is comparably well predicted. (C) 2011 Elsevier B.V. All rights reserved.
Modelling of UPLC behaviour of acylcarnitines by quantitative structure-retention relationships
No full textIn the present work, the retention time (RT) of acylcarnitines, collected by ultra-performance liquid-chromatography after formation of butyl esters, is modelled by quantitative structure–retentionrelationship (QSRR) method. The investigated set consists of free carnitine and 46 different acylcarnitines,including the isomers commonly monitored in screening metabolic disorders. To describe the structureof (butylated) acylcarnitines, a large number of computational molecular descriptors generated by soft-ware Dragon are subjected to variable selection methods aimed at identifying a small informative subset.The QSRR model is established using two different approaches: the multi linear regression (MLR) com-bined with a genetic algorithm (GA) variable selection and the partial least square (PLS) regression afteriterative stepwise elimination (ISE) of useless descriptors. Predictive performance of both models is eval-uated using an external set consisting of 10 representative acylcarnitines, and, successively, by repeatedrandom data partitions between the calibration and prediction sets. Finally, a principal component anal-ysis (PCA) is performed on the model variables to facilitate the interpretation of the established QSRRs.A PLS model based on seven latent variables extracted from 20 molecular descriptors selected by ISEpermits to calculate/predict the retention time of acylcarnitine with accuracy better than 5%, whereas a6-dimensional model identified by GA-MLR provides a slightly worse performance
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