Angelo Antonio D’Archivio

Cross-column retention prediction in reversed-phase high-performance liquid chromatography by artificial neural network modelling

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.

Optimisation by response surface methodology of microextraction by packed sorbent of non steroidal anti-inflammatory drugs and ultra-high performance liquid chromatography analysis of dialyzed samples

A procedure based on microextraction by packed sorbent (MEPS) followed by ultra-high performance liquid chromatography (UHPLC) with photodiode array (PDA) detection has been developed for the analysis of seven selected non steroidal anti-inflammatory drugs (NSAIDs) in human dialysates. The influence on MEPS efficiency of pH of the sample, pH of the washing solvent and methanol content in the hydro-alcoholic elution mixture has been investigated by response surface methodology based on a Box-Behnken design of experiments. Among the above factors, pH of sample is the variable that mostly influences MEPS recovery. UHPLC separation of the NSAIDs was completed within less than 4min under isocratic elution conditions on a Fortis SpeedCore C18 column (150×4.6mm I.D., 2.6μm) using acetonitrile-phosphate buffer as the mobile phase. Calibration curves of the NSAIDs were linear over the concentration range 0.025-15μg/mL, with correlation coefficients≥0.998. Intra- and inter-assay relative standard deviations were <8% and recovery values ranged from 94% to 100% for the quality control samples. The results reveal that the developed MEPS/PDA-UHPLC method exhibits a good accuracy and precision and is well suited for the rapid analysis of human dialysate from patients treated with the selected NSAIDs.

Cross-column prediction of gas-chromatographic retention of polychlorinated biphenyls by artificial neural networks

In this paper, we build a multiple-column retention model able to predict the behaviour of polychlorinated biphenyls (PCBs) in capillary gas-chromatography (GC) 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 stationary 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. 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.

Cross-column prediction of gas-chromatographic retention of polybrominated diphenyl ethers

In this paper, we predict the retention of polybrominated diphenyl ethers (PBDEs) in capillary gas-chromatography (GC) within a useful range of separation conditions. In a first stage of this study, quantitative structure-retention relationships (QSRRs) of PBDEs in six stationary phases with different polarity are established. The single-column QSRR models are generated using the retention data of 126 PBDE congeners by multilinear regression (MLR) coupled to genetic algorithm variable selection applied to a large set of theoretical molecular descriptors of different classes. A quite accurate fitting of experimental retentions is obtained for each of the six GC columns adopting five molecular descriptors. In a further step of this work six molecular descriptors were extracted within the set of molecular descriptors (17 variables) involved in the various single-column QSRRs. The selected molecular descriptors are combined with observed retentions of ten representative PBDEs, adopted as descriptors of the GC system. These quantities are considered as the independent variables of a multiple-column retention model able to simultaneously relate GC retention to PBDE molecular structure and kind of column. The quantitative structure/column-retention relationship is established using a multi-layer artificial neural network (ANN) as regression tool. To optimise the ANN model, a validation set is generated by selecting two out of the six calibration columns. Splitting of columns between training and validation sets, as well as selection of PBDE congeners to be used as column descriptors, is performed with the help of a principal component analysis on the retention data. Cross-column predictive performance of the final model is tested on a large external set consisting of retention data of 180 PBDEs collected in four separation conditions different from those considered in model calibration (different columns and/or temperature program).

Multi-variable retention modelling in reversed-phase high-performance liquid chromatography based on the solvation method: A comparison between curvilinear and artificial neural network regression

The linear solvation energy relationships (LSERs) have been widely used in the last decades for description and prediction of retention in reversed-phase high-performance liquid chromatography (RP-HPLC). LSERs are usually applied to model the effect of solute structure on the RP-HPLC retention at a fixed separation condition. Some authors by combining LSER with known empirical relationships relating retention with mobile phase composition of binary eluents (ϕ) have proposed a predictive model able to simultaneously relate RP-HPLC retention to both solute LSER descriptors and mobile phase composition. The resulting relationship can be established for a given column/organic modifier combination by curvilinear regression aimed at defining 18 model coefficients. In this study, we compare predictive performance of such approach and that of artificial neural network (ANN) regression in which the five solute LSER descriptors and ϕ are directly considered as the network inputs. To this purpose we analyse literature retention data of 31 molecules of different types collected on five reversed-phase columns either in water-acetonitrile and water-methanol mobile phase, the organic modifier content ranging between 20 and 70% (v/v). For each column/organic modifier combination both a curvilinear and an ANN-based model is built using data referred to 25 solutes, while the alternative models are later tested on the remaining six solutes excluded from calibration. Further, we compare capability of curvilinear and ANN regression after including into the respective models also variability related with the stationary phase, represented by the average retention of calibration solutes extrapolated at pure water as the mobile phase. The results of this investigation demonstrate that regardless of the kind of column and organic modifier ANN regression, as compared with curvilinear modelling, provides lower prediction errors and these are more uniformly distributed over the investigated retention range.

Modelling of the effect of solute structure and mobile phase pH and composition on the retention of phenoxy acid herbicides in reversed-phase high-performance liquid chromatography

A feed-forward artificial neural network (ANN) learned by error back-propagation is used to generate a retention predictive model for phenoxy acid herbicides in isocratic reversed-phase high-performance liquid chromatography. The investigated solutes (18 compounds), apart from the most common herbicides of this class, include some derivatives of benzoic acid and phenylacetic acid structurally related to phenoxy acids, as a whole covering a pK(a) range between 2.3 and 4.3. A mixed model in terms of both solute descriptors and eluent attributes is built with the aim of predicting retention in water-acetonitrile mobile phases within a large range of composition (acetonitrile from 30% to 70%, v/v) and acidity (pH of water before mixing with acetonitrile ranging between 2 and 5). The set of input variables consists of solute pK(a) and quantum chemical molecular descriptors of both the neutral and dissociated form, %v/v of acetonitrile in the mobile phase and pH of aqueous phase before mixing with acetonitrile. After elimination of redundant variables, a nine-dimensional model is identified and its prediction ability is evaluated by external validation based on three solutes not involved in model generation and by cross-validation. A multilinear counterpart in terms of the same descriptors is seen to provide a noticeably poorer retention prediction.

Investigation by response surface methodology of the combined effect of pH and composition of water-methanol mixtures on the stability of curcuminoids.

Response surface methodology, coupled to a full factorial three-level experimental design, was applied to investigate the combined influence of pH (between 7.0 and 8.6) and composition of methanol-water mixtures (between 30 and 70% v/v of methanol content) on the stability of curcumin and its analogues demethoxycurcumin and bisdemethoxycurcumin. The response plots revealed that addition of methanol noticeably improved the stability of curcuminoids, this effect being both pH- and structure-dependent. In the central point of the experimental domain, half-life times of curcumin, demethoxycurcumin and bisdemethoxycurcumin were 3.8±0.2, 27±2 and 251±17h, respectively. Stability of curcuminoids increased at lower pH and higher methanol content and decreased in the opposite vertex of the experimental domain. These results can be interpreted by assuming that addition of methanol to water produces a different variation of pH of the medium and apparent pKa values of the ionisable groups of curcuminoids.

Modelling of UPLC behaviour of acylcarnitines by quantitative structure-retention relationships

In 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-retention relationship (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 structure of (butylated) acylcarnitines, a large number of computational molecular descriptors generated by software 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) combined with a genetic algorithm (GA) variable selection and the partial least square (PLS) regression after iterative stepwise elimination (ISE) of useless descriptors. Predictive performance of both models is evaluated using an external set consisting of 10 representative acylcarnitines, and, successively, by repeated random data partitions between the calibration and prediction sets. Finally, a principal component analysis (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 ISE permits to calculate/predict the retention time of acylcarnitine with accuracy better than 5%, whereas a 6-dimensional model identified by GA-MLR provides a slightly worse performance.

Cross-column prediction of gas-chromatographic retention indices of saturated esters

We combine computational molecular descriptors and variables related with the gas-chromatographic stationary 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 the Kováts 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 evaluated on an internal validation set consisting of data of 40 selected esters collected with each of the seven columns, sequentially excluded from calibration. The molecular descriptors are identified by a genetic algorithm variable selection method applied to a large set of non-empirical structural quantities aimed at finding the best multi-linear quantitative structure-retention relationship (QSRR) for the column OV-25 having intermediate polarity. To describe the columns, we consider the sum of the first five McReynolds phase constants and, alternatively, the coefficients of the corresponding QSRRs. Moreover, the mean RI value for the subset of esters used in QSRR calibration or RIs of a few selected compounds are used as column descriptors. For each combination of solute and column descriptors, the retention model is generated both by multi-linear regression and artificial neural network regression.

Adsorption of s-triazines onto polybenzimidazole: A quantitative structure–property relationship investigation

The 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 micro-porosity 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, KF, 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 GA-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.