Comprehensive Extraction and Chemical Characterization of Bioactive Compounds in Tepals of Crocus sativus L.

Crocus sativus L. is largely cultivated because it is the source of saffron, a well-appreciated and valued spice, not only for its culinary use but also because of its significant biological activities. Stigmas are the main product obtained from flowers, but in addition, tepals, largely considered a waste product, represent a big source of flavonoids and anthocyanins. This study aimed to delve into the phytochemical composition of saffron tepals and investigate whether the composition was influenced by the extraction technique while investigating the main analytical techniques most suitable for the characterization of tepal extracts. The research focuses on flavonoids, a class of secondary metabolites, and their health benefits, including antioxidant, anti-inflammatory, and anticancer properties. Flavonoids occur as aglycones and glycosides and are classified into various classes, such as flavones, flavonols, and flavanones. The most abundant flavonoids in tepals are kaempferol glycosides, followed by quercetin and isorhamnetin glycosides. Overall, this review provides valuable insights into the potential uses of tepals as a source of bioactive compounds and their applications in various fields, promoting a circular and sustainable economy in saffron cultivation and processing.

Quantitative Structure-Retention Relationship Analysis of Polycyclic Aromatic Compounds in Ultra-High Performance Chromatography.

A comparative quantitative structure-retention relationship (QSRR) study was carried out to predict the retention time of polycyclic aromatic hydrocarbons (PAHs) using molecular descriptors. The molecular descriptors were generated by the software Dragon and employed to build QSRR models. The effect of chromatographic parameters, such as flow rate, temperature, and gradient time, was also considered. An artificial neural network (ANN) and Partial Least Squares Regression (PLS-R) were used to investigate the correlation between the retention time, taken as the response, and the predictors. Six descriptors were selected by the genetic algorithm for the development of the ANN model: the molecular weight (MW); ring descriptor types nCIR and nR10; radial distribution functions RDF090u and RDF030m; and the 3D-MoRSE descriptor Mor07u. The most significant descriptors in the PLS-R model were MW, RDF110u, Mor20u, Mor26u, and Mor30u; edge adjacency indice SM09_AEA (dm); 3D matrix-based descriptor SpPosA_RG; and the GETAWAY descriptor H7u. The built models were used to predict the retention of three analytes not included in the calibration set. Taking into account the statistical parameter RMSE for the prediction set (0.433 and 0.077 for the PLS-R and ANN models, respectively), the study confirmed that QSRR models, associated with chromatographic parameters, are better described by nonlinear methods.

Effects of diclofenac on the swimming behavior and antioxidant enzyme activities of the freshwater interstitial crustacean Bryocamptus pygmaeus (Crustacea, Harpacticoida).

Diclofenac (DCF) is one of the most widespread pharmaceutical compounds found in freshwaters as a pseudo-persistent pollutant due to its continuous release from point and diffuse sources, being its removal in Wastewater Treatment Plants incomplete. Moreover, DCF is particularly persistent in interstitial habitats and potentially toxic for the species that spend their whole life cycle among the same sediment grains. This study is aimed at offering a first contribution to the assessment of DCF effects on freshwater invertebrate species living in the interstitial habitats of springs, rivers, lakes and groundwaters. The Crustacea Copepoda are one of the main components of the freshwater interstitial communities, with the primacy taken by the worm-like and small-sized harpacticoids. A sub-lethal concentration of 50 µg L-1 DCF significantly affected six out of the eight behavior parameters of the burrower/interstitial crustacean harpacticoid Bryocamptus pygmaeus recorded by video tracking analysis. DCF exposure reduced swimming speed, swimming activity, exploration ability and thigmotaxis, and increased swimming path tortuosity. The biochemical approach revealed a reduced level of the mitochondrial superoxide dismutase 2 in individuals exposed to DCF. It could be explained by a decline in mitochondrial performance or by a reduced number of functional mitochondria. Since mitochondrial dysfunction may determine ATP reduction, it comes that less energy is produced for maintaining the cell functions of the DCF-exposed individuals. In addition, the increasing energy demand for the detoxification process further contributes to decrease the total energetic budget allocated for other physiological activities. These observations can explain the changes we have observed in the swimming behavior of the copepod B. pygmaeus.

Experimental Design and Response Surface Methodology Applied to Graphene Oxide Reduction for Adsorption of Triazine Herbicides.

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.

Multivariate optimization of an analytical method for the analysis of Abruzzo white wines by ICP OES.

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.

Geographical discrimination of red garlic (Allium sativum L.) produced in Italy by means of multivariate statistical analysis of ICP-OES data.

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).

UHPLC Analysis of Saffron (Crocus sativus L.): Optimization of Separation Using Chemometrics and Detection of Minor Crocetin Esters.

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.

Extraction of curcuminoids by using ethyl lactate and its optimisation by response surface methodology.

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.

Geographical classification of Italian saffron (Crocus sativus L.) based on chemical constituents determined by high-performance liquid-chromatography and by using linear discriminant analysis.

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.

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.

Optimisation of temperature-programmed gas chromatographic separation of organochloride pesticides by response surface methodology.

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.

Quantitative structure-retention relationships of cannabimimetic aminoalkilindole derivatives and their metabolites.

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.

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.

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.

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.

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.

Prediction of the retention of s-triazines in reversed-phase high-performance liquid chromatography under linear gradient-elution conditions.

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%.

Near-field electrospinning of light-emitting conjugated polymer nanofibers.

The authors report on the realization of ordered arrays of light-emitting conjugated polymer nanofibers by near-field electrospinning. The fibers, made from poly[2-methoxy-5-(2-ethylhexyloxy)-1,4-phenylenevinylene], have diameters of a few hundreds of nanometers and their emission peaked at 560 nm. The observed blue-shift compared to the emission from reference films is attributed to different polymer packing in the nanostructures. Optical confinement in the fibers is also analyzed through self-waveguided emission. These results open interesting perspectives for the realization of complex and ordered architectures by light-emitting nanofibers, such as photonic circuits, and for the precise positioning and integration of conjugated polymer fibers into light-emitting devices.

Quantitative structure/eluent-retention relationships in reversed-phase high-performance liquid chromatography based on the solvatochromic method.

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.

Impact of water quality on removal of carbamazepine in natural waters by N-doped TiO2 photo-catalytic thin film surfaces.

Photocatalytic experiments on the pharmaceutical pollutant carbamazepine (CBZ) were conducted using sol-gel nitrogen-doped TiO(2)-coated glass slides under a solar simulator. CBZ was stable to photodegradation under direct solar irradiation. No CBZ sorption to the catalyst surface was observed, as further confirmed by surface characterization using X-ray photoelectron spectroscopic analysis of N-doped TiO(2) surfaces. When exposing the catalyst surface to natural organic matter (NOM), an excess amount of carbon was detected relative to controls, which is consistent with NOM remaining on the catalyst surface. The catalyst surface charge was negative at pH values from 4 to 10 and decreased with increasing pH, correlated with enhanced CBZ removal with increasing medium pH in the range of 5-9. A dissolved organic carbon concentration of 5mg/L resulted in ~20% reduction in CBZ removal, probably due to competitive inhibition of the photocatalytic degradation of CBZ. At alkalinity values corresponding to CaCO(3) addition at 100mg/L, an over 40% decrease in CBZ removal was observed. A 35% reduction in CBZ occurred in the presence of surface water compared to complete suppression of the photocatalytic process in wastewater effluent.

Protective effects of flavanol-rich dark chocolate on endothelial function and wave reflection during acute hyperglycemia.

Nitric oxide plays a pivotal role in regulating vascular tone. Different studies show endothelial function is impaired during hyperglycemia. Dark chocolate increases flow-mediated dilation in healthy and hypertensive subjects with and without glucose intolerance; however, the effect of pretreatment with dark chocolate on endothelial function and other vascular responses to hyperglycemia has not been examined. Therefore, we aimed to investigate the effects of flavanol-rich dark chocolate administration on (1) flow-mediated dilation and wave reflections; (2) blood pressure, endothelin-1 and oxidative stress, before and after oral glucose tolerance test (OGTT). Twelve healthy volunteers (5 males, 28.2±2.7 years) randomly received either 100 g/d dark chocolate or flavanol-free white chocolate for 3 days. After 7 days washout period, volunteers were switched to the other treatment. Flow-mediated dilation, stiffness index, reflection index, peak-to-peak time, blood pressure, endothelin-1 and 8-iso-PGF(2α) were evaluated after each treatment phase and OGTT. Compared with white chocolate, dark chocolate ingestion improved flow-mediated dilation (P=0.03), wave reflections, endothelin-1 and 8-iso-PGF(2α) (P<0.05). After white chocolate ingestion, flow-mediated dilation was reduced after OGTT from 7.88±0.68 to 6.07±0.76 (P=0.027), 6.74±0.51 (P=0.046) at 1 and 2 h after the glucose load, respectively. Similarly, after white chocolate but not after dark chocolate, wave reflections, blood pressure, and endothelin-1 and 8-iso-PGF(2α) increased after OGTT. OGTT causes acute, transient impairment of endothelial function and oxidative stress, which is attenuated by flavanol-rich dark chocolate. These results suggest cocoa flavanols may contribute to vascular health by reducing the postprandial impairment of arterial function associated with the pathogenesis of atherosclerosis.