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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 ± 17 h, 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. © 2016 Elsevier Ltd


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, Città 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 328 nm, 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. © 2016 Elsevier Ltd


PubMed | Via Novus, University of L'Aquila and University of Chieti Pescara
Type: | Journal: Journal of pharmaceutical and biomedical analysis | Year: 2016

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 (1504.6mm I.D., 2.6m) using acetonitrile-phosphate buffer as the mobile phase. Calibration curves of the NSAIDs were linear over the concentration range 0.025-15g/mL, with correlation coefficients0.998. 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.


D'Archivio A.A.,University of L'Aquila | Maggi M.A.,Via Novus | Ruggieri F.,University of L'Aquila
Journal of Pharmaceutical and Biomedical Analysis | Year: 2014

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. © 2014.


D'Archivio A.A.,University of L'Aquila | Maggi M.A.,Via Novus | Ruggieri F.,University of L'Aquila
Journal of Chromatography A | Year: 2014

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. © 2014 Elsevier B.V.


D'Archivio A.A.,University of L'Aquila | Maggi M.A.,Via Novus | Ruggieri F.,University of L'Aquila
Journal of Pharmaceutical and Biomedical Analysis | Year: 2015

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. © 2015 Elsevier B.V.


D'Archivio A.A.,University of L'Aquila | Maggi M.A.,Via Novus | Ruggieri F.,University of L'Aquila
Analytical and Bioanalytical Chemistry | Year: 2015

Abstract 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. © 2014 Springer-Verlag Berlin Heidelberg.


D'Archivio A.A.,University of L'Aquila | Maggi M.A.,Via Novus | Ruggieri F.,University of L'Aquila
Food Analytical Methods | Year: 2016

Summary: 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. © 2016 Springer Science+Business Media New York


D'Archivio A.A.,University of L'Aquila | Giannitto A.,University of L'Aquila | Maggi M.A.,Via Novus | Ruggieri F.,University of L'Aquila
Food Chemistry | Year: 2016

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 Città 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. © 2016 Elsevier Ltd.


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