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In this work, pristine graphene oxide and its thermally reduced derivatives, rGO, were tested for the removal of triazines (atraton, prometryn, and atrazine) from water. The reduction process was optimized by means of design of experiments (DOE) coupled with response surface methodology (RSM), relying on the adsorption efficiency of the material. The optimal reduction conditions were calculated at a temperature of 110 °C maintained for 24 h; the mildest and simplest reduction protocol was chosen, as it allows in-air heat treatment with a common laboratory oven. The rGO samples were characterized before use, confirming a partial reduction process that, leaving intact most of the oxygenated functionalities on the graphene skeleton, may still allow favorable adsorption of pollutants through both hydrogen bonds and π-π interactions, which result from a large conjugated polyaromatic system. Triazine analyses were performed by high-performance liquid chromatography (HPLC); the data obtained from the adsorption isotherms were fitted with the Langmuir and Freundlich models, highlighting a slightly different adsorption behavior of atraton and prometryn compared with atrazine. Model outcomes were also used to support the hypotheses about the adsorption process.

Gas chromatography with mass spectrometry detection (GC-MS) coupled with headspace-solid-phase microextraction (HS-SPME) was used to analyse the aroma profile of genuine saffron (Crocus sativus L.) and samples of this spice artificially adulterated with Calendula officinalis L. petals (calendula), Carthamus tinctorius L. petals (safflower) and Curcuma longa L. powdered rhizomes (turmeric). Preliminary analyses of genuine saffron and pure contaminants were performed to select the kind of SPME sorbent. Moreover, an experimental design combined with response surface methodology was applied to optimise the sample temperature and the fibre exposure time with the aim of enhancing the detection of the above adulterants in counterfeited saffron samples. The GC-MS chromatograms collected under the optimised conditions were finally handled by unsupervised and supervised multivariate statistical methods to differentiate the genuine saffron samples produced in three different Italian regions from artificially adulterated samples at 2–5% w/w contamination levels. Thirty genuine and 30 counterfeited (10 for each kind of adulterant) saffron samples were analysed. Principal component analysis was applied to assist the choice of the GC/MS data pre-treatment and classification of genuine and adulterated saffron samples was attempted by partial least square-discriminant analysis (PLS-DA). Predictive performance of PLS-DA models calibrated with 42 samples was finally tested on 18 saffron samples (9 genuine and 9 adulterated). All the external saffron samples were correctly classified regardless of the kind of contaminant, while in calibration, only a saffron sample contaminated with safflower was erroneously assigned to the group of genuine ones. Class modelling of genuine saffron performed by SIMCA (Soft Independent Model Class Analogy) exhibited a good sensitivity and 100% specificity for external adulterated samples.

Clean water is vital for healthy ecosystems, for human life and, in a broader sense, it is directly linked to our socio-economic development. Nevertheless, climate change, pollution and increasing world population will likely make clean water scarcer in the near future. Consequently, it becomes imperative to develop novel materials and more efficient ways of treating waste and contaminated water. Carbon nanotube (CNT) sponges, for example, are excellent in removing oleophilic contaminants; however, due to their super-hydrophobic nature, they are not as efficient when it comes to absorbing water-soluble substances. Here, by means of a scalable method consisting of simply treating CNT sponges at mild temperatures in air, we attach oxygen-containing functional groups to the CNT surface. The functionalized sponge becomes hydrophilic while preserving its micro- and macro-structure and can therefore be used to successfully remove toxic contaminants, such as pesticides, that are dissolved in water. This discovery expands the current range of applications of CNT sponges to those fields in which a hydrophilic character of the sponge is more suitable.

Twenty-nine different Iranian and Italian saffron samples have been characterized using high-performance liquid chromatography (HPLC) equipped with UV–Vis detection as well as ultraviolet–visible (UV–Vis) spectroscopy methods by preparing aqueous extracts according to the ISO 3632 standard. UV–Vis spectral data in the wavelength range of 200–600 nm and the intensities of the chromatographic peaks attributed to picrocrocin, safranal and crocetin esters were considered as variables in principal component analysis (PCA) and linear discriminant analysis (LDA) methods to classify Iranian and Italian saffron samples based on their geographical origin. The geographical classification results based on HPLC data indicated safranal, cis-crocetin (β-d-gentiobiosyl)(β-d-glucosyl) ester (C3Gg), trans-crocetin (β-d-neapolitanosyl)(β-d-glucosyl) ester (T4ng), and trans-crocetin (β-d-gentiobiosyl)(β-d-glucosyl) ester (T3Gg) as the most discriminant. The best classification performance was achieved by the UV–Vis spectra where the most discriminant regions can be associated with both cis- and trans-crocins (326–327 and 260–264 nm), safranal (310 nm), picrocrocin (240–250 nm), and kaempferol derivatives (265 and 340–350 nm).

Keywords: saffron, crocins, neuroprotective activity, P2X7 receptor, fraction

An inductively coupled plasma-optical emission spectrometry (ICP OES) method was optimized and applied for determining the concentration of 14 elements (Ba, Ca, Co, Cu, Fe, K, Li, Mg, Mn, Na, Ni, Sr, V, and Zn) in three representative white wines of the Abruzzo region (Italy). In order to optimise an ICP OES method a three level factorial design for three variables was used. The intensity of the emission lines for analytes was simultaneously maximised by using Derringer's desirability function. Using this approach, the optimal experimental conditions for wine analysis of 0.48 L min-1, 1.8 mL min-1 and 0.5 L min-1 for the nebulizer gas flow rate, sample uptake rate and auxiliary gas flow rate respectively were achieved. A total of 46 white wine samples of the three varieties were analysed by using the optimised experimental conditions. Linear Discriminant Analysis (LDA) and Partial Least Squares Linear Discriminant Analysis (PLS LDA) allowed an acceptable classification of the three varietal samples.

An extract of Curcuma longa containing mainly curcumin was included in liposomes formulated with phospholipids differing for the length of the acyl chains, the charge of the polar headgroup or the degree of unsaturation in the presence and in the absence of cholesterol. In fact, even if it is well known that liposomal curcuminoids are more stable and can be solubilized in aqueous media, a systematic approach that correlates the molecular structure of liposomes components to the physicochemical and biological behavior of the vescicles including curcumin and its derivatives is not described. Size and zeta potential of liposomes, the location and the antioxidant activity of the solute in the bilayer and its effect on lipid packing were evaluated and correlated to the pharmacological activity of liposomal extract against a methicillin susceptible Staphylococcus Aureus strain ATCC 29213. The relation between the physicochemical and biological features of formulations points out that the rigidity of the bilayer and liposomes charge are crucial parameters in the interactions of liposomes with the biological environment. In particular, the synergism between curcumin antibacterial properties and the positive liposomes surface brings to a significant reduction of the minimal inhibitory concentration of the liposomal drug with respect to the one of the free extract thus enlarging the prospectives of its pharmacological exploitation.

Photoreceptor neurodegenerative processes are induced by a variety of factors: genetic mutations, environmental stresses, aging, and a combination of them. The final outcome is photoreceptors malfunction and eventually death leading to progressive deterioration of visual function. At present, there is no cure for most retinal diseases. Recently, saffron has become an interesting candidate to cope with morphological and functional consequences of retinal neurodegeneration. Data obtained in both animal models and clinical trials in age-related macular degeneration and Stargardt patients provided evidence that saffron treatment reduces photoreceptor death, slows down the progression of neuroinflammatory processes, and eventually improves and maintains visual function. The ways of action are complex and not limited to reducing reactive oxygen species production. In fact, saffron treatment is also able to activate many genes. In addition, the chemical composition of saffron's components (mainly the ratio between different crocins) is a critical factor in defining the efficacy of treatment.

One hundred and forty-nine (149) Italian saffron samples produced in the years from 2013 to 2016 in distinct sites located in five different Italian regions, Abruzzo, Tuscany, Umbria, Campania and Sardinia, together with twenty-seven (27) commercial samples, have been analyzed by high-performance liquid chromatography with diode array detection (HPLC-DAD). Among the investigated samples, those produced in Abruzzo (L'Aquila area) present an even higher added-value, because, since 2005, saffron of L'Aquila has been granted of the protected designation of origin (PDO) mark. In the present study, two different analytical approaches aimed at distinguishing PDO saffron of L'Aquila from the other samples have been compared. The first strategy is a more traditional approach, where the chromatograms collected at specific wavelengths are classified by Partial Least Squares Discriminant Analysis (PLS-DA). The second strategy exploits the multi-way nature of data, avoiding discarding any source of information. Consequently, the entire spectro-chromatogram is handled by N-Partial Least Squares (N-PLS) and then classified by Linear Discriminant Analysis (LDA). Both approaches provided satisfactory predictions; the best results from the prediction point of view (estimated on an external set of samples) were achieved by the proposed multi-way methodology.

A Box–Behnken experimental design combined with response surface methodology was applied to investigate the effect of the eluent composition on the retention of common cations in ion chromatography. In particular, we modelled the retention factors of Na+, K+, NH4 +, Ca++ and Mg++ and the chromatogram resolution. Response surfaces were evaluated to assess interactive effects of nitric acid, pyridine-2,6-dicarboxylic acid and percentage of acetonitrile in the eluent on the above parameters. Nitric acid concentration and acetonitrile content appeared as the most and the least influential components of the eluent in determining the retention factors of individual ions or the global resolution of the chromatogram, while the concentration of the complexing agent exhibits an intermediate role. As expected, acidification of the mobile phase produces a decrease in the retention factors and a progressive worsening of resolution, and this effect can be modulated by the concentration of the complexing agent. The proposed approach seems suitable to investigate the retention phenomena in ion chromatography under application of complex eluent phases.

PDO (protected designation of origin) Zafferano dell’Aquila (AQ), Iranian (IR), and commercial (CS) saffron samples were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). The ICP-MS data of 30 elements were handled by unsupervised (principal component analysis (PCA)) and supervised (linear discriminant analysis (LDA)) multivariate statistical methods to identify a subset of discriminant variables useful for geographical classification. Moreover, class modeling of AQ saffron was performed by both UNEQ (unequal disperses classes) and SIMCA (soft independent modeling of class analogy) methods. A good differentiation of the AQ, IR, and CS samples was obtained by the LDA based on four selected elements: Sr, Ca, Mo, and Fe. A UNEQ class model for the PDO AQ saffron based on the above four elements provided 100% sensitivity (all authentic AQ saffron were accepted) and 100% specificity (all IR and CS were rejected), while slightly worse results were obtained using the SIMCA (89% sensitivity and 96% specificity).

The 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.

Sixty-five samples of red garlic (Allium sativum L.) coming from four different production territories of Italy were analysed by means of inductively coupled plasma optical emission spectrometry. The garlic samples were discriminated according to the geographical origin using the content of seven elements (Ba, Ca, Fe, Mg, Mn, Na and Sr). Both classification and class modelling methods by using linear discriminant analysis (LDA) and soft independent model class analogy (SIMCA), respectively, were applied. Classification ability and modelling efficiency were evaluated on an external prediction set (21 garlic samples) designed by application of duplex Kennard-Stone algorithm. All the calibration and prediction samples were correctly classified by means of LDA. The class models developed using SIMCA exhibited high sensitivity (almost all the calibration and external samples were accepted by the respective classes) and good specificity (the majority of extraneous samples were refused by each class model).

Multi-walled carbon nanotubes (MWCNTs), because of their small size and large available surface area, are potentially efficient sorbents for the extraction of water solutes. Dispersion of MWCNTs in aqueous medium is suitable to adsorb organic contaminants from small sample volumes, but, the recovery of the suspended sorbent for successive re-use represents a critical step, which makes this method inapplicable in large-scale water-treatment technologies. To overcome this problem, we proposed here MWCNTs grown on silicon supports and investigated on a small-volume scale their adsorption properties towards triazine herbicides dissolved in water. The adsorption efficiency of the supported MWCNTs has been tested on seven triazine herbicides, which are emerging water contaminants in Europe and USA, because of their massive use, persistence in soils and potential risks for the aquatic organisms and human health. The investigated compounds, in spite of their common molecular skeleton, cover a relatively large property range in terms of both solubility in water and hydrophilicity/hydrophobicity. The functionalisation of MWCNTs carried out by acidic oxidation, apart from increasing wettability of the material, results in a better adsorption performance. Increasing of functionalisation time between 17 and 60 h progressively increases the extraction of all seven pesticides and produces a moderate increment of selectivity.

Gas cromatography with mass spectrometry detection coupled to headspace solid-phase microextraction (SPME) was used to analyse the aroma profile of saffron (Crocus sativus L.). The influence of the saffron amount and form (intact stigmas vs. powder) on the intensity of the chromatograms was preliminarily evaluated. Moreover, the mutual effects of the sample temperature and exposition time of a polydimethylsiloxane SPME fiber was investigated by response surface methodology. Saffron samples produced in L’Aquila (Italy), Iran and Morocco, and commercial spices were analysed under application of the optimised SPME conditions and geographical classification was attempted by linear discriminant analysis applied to the relative peak areas of all the detected volatiles. The spices produced in the different geographical areas and the commercial products were discriminated with good accuracy, 91% of external samples being correctly classified using leave-one-out cross-validation. An acceptable differentiation (73% of correct predictions) was obtained by considering only the major aroma components.

Novel polystyrene-based molecularly imprinted polymer nanofibers were synthesized through the electrospinning technique. The molecularly imprinted polymers were prepared using a non-covalent approach and atrazine as template. For comparison, nonimprinted polymer nanofibers were also synthesized. The morphology of the synthesized nanofibers was characterized using scanning electron microscopy. The adsorption of pesticides, atrazine, atrazine desisopropyl, atraton, carboxin, linuron, and chlorpyrifos was studied under equilibrium (batch) conditions. To describe the adsorption capability of the synthesized polymers, Langmuir and Freundlich models were used. The Freundlich model provided a better mathematical approximation of the sorption characteristic for polymers nanofibers. To evaluate the adsorption capacity in the presence of interferents experiments on river water samples spiked with a mixture of six pesticides were also performed. The results obtained for the highest concentration levels investigated, show a greater amount of pesticide adsorbed on molecularly imprinted polymers and non-imprinted polymers compared to those obtained using commercial stationary phases used as reference.

Response surface methodology (RSM) was applied to optimise the extraction of curcuminoids (curcumin, demethoxycurcumin and bisdemethoxycurcumin) from turmeric using ethyl lactate (EL), ethanol and water under mild conditions (magnetic stirring at room temperature). An augmented simplex-centroid mixture design was used to monitor the dependence of the extraction efficiency from the proportions of the three solvents in the extraction medium. HPLC was used to establish the content of curcuminoids in turmeric and in the extracts. Surface plots for the extracted amount of each curcuminoid covering the whole composition domain were generated by interpolation of the experimental data with quadratic canonical polynomial models. The response surfaces of the three curcuminoids are qualitatively similar and the maximum extraction efficiency was obtained with water-EL 30:70v/v that ensured the almost quantitative recovery of the three compounds from turmeric. While degradation of the three curcuminoids in water at moderate alkaline pH is relatively fast (half-times are between 0.23 and 8.5h at pH=8.6), their stability is noticeably greater in EL (half-times are within 21-69days). Addition of EL to water is also able to inhibit the alkaline hydrolysis of curcumin and its derivatives, their half-times in the water-EL 30:70v/v, being within 40-70h at pH=8.6. The above evidences suggest that EL is a promising solvent for the extraction of curcuminods from turmeric and a suitable medium for vehiculation of these compounds into drugs or foods.

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.

In 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%.

We attempted geographical classification of saffron using UV-visible spectroscopy, conventionally adopted for quality grading according to the ISO Normative 3632. We investigated 81 saffron samples produced in L'Aquila, Citta della Pieve, Cascia, and Sardinia (Italy) and commercial products purchased in various supermarkets. Exploratory principal component analysis applied to the UV-vis spectra of saffron aqueous extracts revealed a clear differentiation of the samples belonging to different quality categories, but a poor separation according to the geographical origin of the spices. On the other hand, linear discriminant analysis based on 8 selected absorbance values, concentrated near 279, 305 and 328nm, allowed a good distinction of the spices coming from different sites. Under severe validation conditions (30% and 50% of saffron samples in the evaluation set), correct predictions were 85 and 83%, respectively.

One hundred and forty-four Italian saffron samples produced in the years from 2009 to 2015 in five distinct areas located in four different regions, Abruzzo (L'Aquila), Tuscany (Florence), Umbria (Cascia and Citta della Pieve) and Sardinia, have been analysed by high-performance liquid chromatography with diode array detection. Intensities of the chromatographic peaks attributed to crocins, safranal, picrocrocin and its derivatives and flavonoids were considered as variables in linear discriminant analysis to attempt geographical classification. The results revealed that spices produced at different sites of the Italian territory can be discriminated with good accuracy. The differentiation of saffron cultivated in Sardinia from those produced in Central Italy was mainly attributed to different contents of the most abundant crocins. Good differentiation of spices produced in close sites of Central Italy was also observed, 88% of validation samples being correctly classified; some minor crocins are responsible for such discrimination.

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 were8% 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.

Ultra-high performance liquid chromatography (UHPLC) coupled with diode array detection (DAD) was applied to improve separation and detection of mono- and bis-glucosyl esters of crocetin (crocins), the main red-colored constituents of saffron (Crocus sativus L.), and other polar components. Response surface methodology (RSM) was used to optimise the chromatographic resolution on the Kinetex C18 (Phenomenex) column taking into account of the combined effect of the column temperature, the eluent flow rate and the slope of a linear eluent concentration gradient. A three-level full-factorial design of experiments was adopted to identify suitable combinations of the above factors. The influence of the separation conditions on the resolutions of 22 adjacent peaks was simultaneously modelled by a multi-layer artificial neural network (ANN) in which a bit string representation was used to identify the target analytes. The chromatogram collected under the optimal separation conditions revealed a higher number of crocetin esters than those already characterised by means of mass-spectrometry data and usually detected by HPLC. Ultra-high performance liquid chromatography analyses carried out on the novel Luna Omega Polar C18 (Phenomenex) column confirmed the large number of crocetin derivatives. Further work is in progress to acquire mass-spectrometry data and to clarify the chemical structure to the newly found saffron components.

Recently, 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 octade-cylsiloxane-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.

The ability of the Weighted Holistic Invariant Molecular (WHIM) and GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors to represent the effect of molecular structure on the retention of pesticides in reversed-phase high-performance liquid chromatography (RP-HPLC) is investigated. To this end, two retention data sets previously collected in water–acetonitrile mobile phase are re-examined. The first data set ( data-set-1 ) consists of retention data of 26 neutral compounds belonging to widely used pesticide classes, collected within the mobile phase composition range 40–65% (v/v) acetonitrile. The second data set ( data-set-2 ) describes retention of phenoxy acid herbicides and structurally related compounds (benzoic acid and phenylacetic acid derivatives), as a whole covering the p K a range 2.3–4.3, as a function of mobile phase composition, ranging between 30 and 70% (v/v) acetonitrile, and pH, ranging between 2 and 5. For each data set, the mobile phase attributes are combined with WHIM or GETAWAY descriptors into “mixed” predictive models in order to attempt retention modelling within the whole mobile phase composition range of analytical interest. Six- or seven-dimensional multilinear models, preliminarily selected using a genetic algorithm, were improved using a multi-layer artificial neural network (ANN) learned by back propagation. ANN performance was tested on three molecules not used in the learning stage and by leave-more-out cross validation. The results reveal that while WHIM descriptors seem not adequate to model retention of solutes of data-set-1 , GETAWAY descriptors provide a satisfactory retention model. On the other hand WHIM and GETAWAY descriptors applied to data-set-2 provide a similar performance, even if slightly worse as compared with the above case. Accuracy of retention modelling in these cases is comparable or slightly poorer as compared with the results previously obtained by combining quantum chemical descriptors or usual physico-chemical solute properties (log k ow and p K a ) and mobile phase attributes.

A response surface methodology (RSM) approach is applied to optimise the temperature-programme gas-chromatographic separation of 16 organochloride pesticides, including 12 compounds identified as highly toxic chemicals by the Stockholm Convention on Persistent Organic Pollutants. A three-parameter relationship describing both linear and curve temperature programmes is derived adapting a model previously used in literature to describe concentration gradients in liquid chromatography with binary eluents. To investigate the influence of the three temperature profile descriptors (the starting temperature, the gradient duration and a shape parameter), a three-level full-factorial design of experiments is used to identify suitable combinations of the above variables spanning over a useful domain. Resolutions of adjacent peaks are the responses modelled by RSM using two alternative methods: a multi-layer artificial network (ANN) and usual polynomial regression. The proposed ANN-based approach permits to model simultaneously the resolutions of all the consecutive analyte pairs as a function of the temperature profile descriptors. Four critical pairs giving partially overlapped peaks are identified and multiresponse optimisation is carried out by analysing the surface plot of a global resolution defined as the average of the resolutions of the critical pairs. Descriptive/predictive performance and applicability of the ANN and polynomial RSM methods are compared and discussed.

Development of chromatographic analyses of synthetic cannabinoids is complicated by the lack of commercial reference standards, especially for new analogues introduced in the clandestine market to bypass legal controls and for their metabolites. In the present work, we explore the possibility of predicting the retention behaviour of the cannabimimetic aminoalkilindoles and their urinary metabolites in high-performance liquid-chromatography using a quantitative structure-retention relationship (QSRR) generated by multilinear regression. To represent the structure of the 43 investigated analytes, 617 computational molecular descriptors are subjected to genetic algorithm variable selection aimed at identifying a small but informative subset. Predictive performance of the QSRR model is evaluated on an external set consisting of 10 representative compounds, including both drugs and their metabolites, and, successively by a Monte Carlo validation method. The best QSRR model, based on six molecular descriptors, exhibits a promising predictive performance and robustness.

A multilayer artificial neural network (ANN) is used to model the reversed-phase liquid chromatography retention times of 16 selected compounds, including purines, pyrimidines and nucleosides. The analysed data, taken from literature, were collected in acetonitrile-water eluents under the application of 16 different multilinear gradients. The parameters describing the gradient profile together with solute descriptors are considered as the independent variables of an ANN-based model providing the retention time as response. Categorical variables or, alternatively, a selected set of molecular descriptors of computational origin are adopted to represent the solutes. Network training, validation and testing are performed preliminarily using data of 12, 2 and 4 gradients, respectively and successively, to investigate model performance under more severe calibration conditions, with data of 9, 2 and 7 gradients. The proposed approach allows a quite accurate prediction of retention times of the target analytes in external multilinear gradients. Categorical variables can successfully represent the target solutes when the model is called to transfer retention data from calibration to external gradients. In particular, using a five-dimensional bit string to represent the analytes, mean errors on retention times are 2 and 3 % under the most and less favourable calibration conditions, respectively. A comparable performance is observed if the categorical variables are replaced by five molecular descriptors, selected by a genetic algorithm within a large set of structural variables of computational origin.

In 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%.

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 Kovats 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.

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 φ 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 φ 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.

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.

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.

Recently, 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.

Previously, 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, logK(ow) 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 in water-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 multiple-column 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-microm columns 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-microm stationary phases and on a 4-microm 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.

In this paper, a quantitative structure-retention relationship (QSRR) method is employed to model the retention behaviour in reversed-phase high-performance liquid chromatography of arylpropionic acid derivatives, largely used non-steroidal anti-inflammatory drugs (NSAIDs). Computed molecular descriptors and the organic modifier content in the mobile phase are associated into a comprehensive model to describe the effect of both solute structure and eluent composition on the isocratic retention of these drugs in water-acetonitrile mobile phases. Multilinear regression (MLR) combined with genetic algorithm (GA) variable selection is used to extract from a large set of computed 3D descriptors an optimal subset. Based on GA-MLR analysis, a five-dimensional QSRR model is identified. All the four selected molecular descriptors belong to the category of GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors. The related multilinear model exhibits a quite good fitting and predictive performance. This model is further improved using an artificial neural network (ANN) learned by error back-propagation. Finally, the ANN-based model displays a remarkably better performance as compared with the MLR counterpart and, based on external validation, is able to predict with good accuracy the behaviour of unknown arylpropionic NSAIDs in the range of mobile phase composition of analytical interest (between 35 and 75% acetonitrile (v/v)).

In this paper, a quantitative structure-retention relationships (QSRR) method is employed to predict the retention behaviour of pesticides in reversed-phase high-performance liquid chromatography (HPLC). A six-parameter nonlinear model is developed by means of a feed-forward artificial neural network (ANN) with back-propagation learning rule. Accurate description of the retention factors of 26 compounds including commonly used insecticides, herbicides and fungicides and some metabolites is successfully achieved. In addition to the acetonitrile content, included to describe composition of the water-acetonitrile mobile phase, the octanol-water partition coefficient (from literature) and four quantum chemical descriptors are considered to account for the effect of solute structure on the retention. These are: the total dipole moment, the mean polarizability, the anisotropy of polarizability and a descriptor of hydrogen bonding ability based on the atomic charges on hydrogen bond donor and acceptor chemical functionalities. The proposed nonlinear QSRR model exhibits a high degree of correlation between observed and computed retention factors and a good predictive performance in wide range of mobile phase composition (40-65%, v/v acetonitrile) that supports its application for the prediction of the chromatographic behaviour of unknown pesticides. A multilinear regression model based on the same six descriptors shows a significantly worse predictive capability.