1,721,077 research outputs found
Investigation by response surface methodology of the combined effect of pH and composition of water-methanol mixtures on the stability of curcuminoids
No full textResponse 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
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
Study on Intermicellar Interactions and Micellar Size in Aqueous Solutions of Sodium Taurocholate by Measurements of Collective Diffusion and Self Diffusion Coefficients
No full textRetention 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.
Geographical identification of saffron (Crocus sativus L.) by linear discriminant analysis applied to the UV–visible spectra of aqueous extracts
No full textWe 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
Geographical discrimination and authentication of lentils (Lens culinaris Medik.) by ICP-OES elemental analysis and chemometrics
No full textGeographical classification and authentication of lentils (Lens culinaris Medik.) was attempted by discriminant
and modelling pattern-recognition methods applied to multi-elemental composition determined by means of
inductively coupled plasma optical emission spectrometry (ICP-OES). After microwave-assisted digestion, the
content of 15 elements was determined in 89 Italian lentil samples produced in three relatively close areas of the
Central Apennines (Castelluccio di Norcia, Colfiorito and Santo Stefano di Sessanio) and 20 samples imported
from Canada. Preliminary exploration of the ICP-OES data revealed a visible effect of the production year on the
mineral composition. A good geographical classification of the lentil samples was obtained by discriminant
approaches. Class models generated by Soft Independent Model Class Analogy presented high sensitivity (all the
calibration and external samples were correctly accepted by the target classes) and good specificity since most of
non-compliant samples were refused by each of the four modelled classes
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.
Analisi simultanea di pesticidi in campioni di acqua minerale, mediante estrazione in fase solida e analisi HPLC-DAD
No full textArtificial 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
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