Optimization of HS-SPME-GC/MS Analysis of Wine Volatiles Supported by Chemometrics for the Aroma Profiling of Trebbiano d'Abruzzo and Pecorino White Wines Produced in Abruzzo (Italy).

Headspace Solid-Phase Microextraction coupled to Gas-Chromatography with Mass Spectrometry detection (HS-SPME/GC-MS) has been widely used to analyze the composition of wine aroma. This technique was here applied to investigate the volatile profile of Trebbiano d'Abruzzo and Pecorino white wines produced in Abruzzo (Italy). Optimization of SPME conditions was conducted by Design of Experiments combined with Response Surface Methodology. We investigated the influence of the kind of sorbent, PDMS, CW/DVB, or PDMS/CAR/DVB, and the effect of the fiber exposure time, temperature, and salt concentration on the total area of the chromatogram and the extraction efficiency of ethyl decanoate and 3-methyl-1-butanol, representative of apolar and polar compounds, respectively. The PDMS/CAR/DVB sorbent allowed the extraction of about 70 compounds, whereas only a part of these substances could be extracted on the PDMS and CW/DVB fibers. Reliable response surfaces for the total area and peak areas of the selected volatiles collected on the PDMS and PDMS/CAR/DVB sorbents and, in the latter case, principal component analysis were evaluated to find the optimal conditions. The optimized extraction conditions were applied for a preliminary comparison of the volatile profile of the two wine varieties and in a successive varietal discrimination study based on data-fusion approaches.

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.

Synthesis of hydrophilic carbon nanotube sponge via post-growth thermal treatment.

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.

Saffron Characterization by a Multidisciplinary Approach.

Saffron is a spice obtained from the drying process of the stigmas of the flower Crocus sativus Linnaeus. It is well known that the organoleptic characteristics of this spice are closely linked to the production area and harvesting year. The present work aims to evaluate whether saffron samples produced in different years and origins present sensibly different crocin profiles. To achieve this goal, 120 saffron samples were harvested between 2016 and 2020 in four different Italian areas. The crocins were analysed, identified, and quantified by high-performance liquid chromatography-electrospray-tandem mass spectrometry (HPLC-ESI-MS/MS) in multiple reaction monitoring mode (MRM). Subsequently, ANOVA-simultaneous component analysis (ASCA) was used to evaluate whether the origin and annuity significantly affected the composition of the crocins. ASCA confirmed the relevance of these effects. Eventually, soft independent modelling by class analogy (SIMCA) models were created for each of the four different origins. Mixtures of saffron from different areas were also prepared to test the robustness of the models. SIMCA provided satisfying results; in fact, models provided 100% sensitivity for three origins (Cascia, Sardinia, and Città della Pieve) on the external test set (48 samples) and 88% (sensitivity on the external test set) for the Spoleto class.

Farming for Pharming: Novel Hydroponic Process in Contained Environment for Efficient Pharma-Grade Production of Saffron.

Soilless cultivation of saffron (Crocus sativus) in a controlled environment represents an interesting alternative to field cultivation, in order to obtain a standardized high-quality product and to optimize yields. In particular, pharma-grade saffron is fundamental for therapeutic applications of this spice, whose efficacy has been demonstrated in the treatment of macular diseases, such as Age-related Macular Degeneration (AMD). In this work, a hydroponic cultivation system was developed, specifically designed to meet the needs of C. sativus plant. Various cultivation recipes, different in spectrum and intensity of lighting, temperature, photoperiod and irrigation, have been adopted to study their effect on saffron production. The experimentation involved the cultivation of corms from two subsequent farm years, to identify and validate the optimal conditions, both in terms of quantitative yield and as accumulation of bioactive metabolites, with particular reference to crocins and picrocrocin, which define the 'pharma-grade' quality of saffron. Through HPLC analysis and chromatography it was possible to identify the cultivation parameters suitable for the production of saffron with neuroprotective properties, evaluated by comparison with an ISO standard and the REPRON® procedure. Furthermore, the biochemical characterization was completed through NMR and high-resolution mass spectrometry analyses of saffron extracts. The whole experimental framework allowed to establish an optimized protocol to produce pharma-grade saffron, allowing up to 3.2 g/m2 harvest (i.e., more than three times higher than field production in optimal conditions), which meets the standards of composition for the therapy of AMD.

Saffron: Chemical Composition and Neuroprotective Activity.

Crocus sativus L. belongs to the Iridaceae family and it is commonly known as saffron. The different cultures together with the geoclimatic characteristics of the territory determine a different chemical composition that characterizes the final product. This is why a complete knowledge of this product is fundamental, from which more than 150 chemical compounds have been extracted from, but only about one third of them have been identified. The chemical composition of saffron has been studied in relation to its efficacy in coping with neurodegenerative retinal diseases. Accordingly, experimental results provide evidence of a strict correlation between chemical composition and neuroprotective capacity. We found that saffron's ability to cope with retinal neurodegeneration is related to: (1) the presence of specific crocins and (2) the contribution of other saffron components. We summarize previous evidence and provide original data showing that results obtained both "in vivo" and "in vitro" lead to the same conclusion.

Retention Modelling of Phenoxy Acid Herbicides in Reversed-Phase HPLC under Gradient Elution.

Phenoxy acid herbicides are used worldwide and are potential contaminants of drinking water. Reversed phase high-performance liquid chromatography (RP-HPLC) is commonly used to monitor phenoxy acid herbicides in water samples. RP-HPLC retention of phenoxy acids is affected by both mobile phase composition and pH, but the synergic effect of these two factors, which is also dependent on the structure and pKa of solutes, cannot be easily predicted. In this paper, to support the setup of RP-HPLC analysis of phenoxy acids under application of linear mobile phase gradients we modelled the simultaneous effect of the molecular structure and the elution conditions (pH, initial acetonitrile content in the eluent and gradient slope) on the retention of the solutes. In particular, the chromatographic conditions and the molecular descriptors collected on the analyzed compounds were used to estimate the retention factor k by Partial Least Squares (PLS) regression. Eventually, a variable selection approach, Genetic Algorithms, was used to reduce the model complexity and allow an easier interpretation. The PLS model calibrated on the retention data of 15 solutes and successively tested on three external analytes provided satisfying and reliable results.

Adsorption of triazine herbicides from aqueous solution by functionalized multiwall carbon nanotubes grown on silicon substrate.

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.

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.

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.

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%.

Unlocking new dimensions in long-acting injectables using lipid mesophase-based beads.

In this study, we explored the use of lipid mesophases (LMPs) as a biocompatible and biodegradable material for sustained drug delivery. Our hypothesis centered on leveraging the high surface-to-volume ratio of LMP-based beads to enhance strength, stability, and surface interaction compared to the LMP bulk gel. To modulate drug release, we introduced antioxidant vitamin E into the beads, influencing mesophase topologies and controlling drug diffusion coefficients. Four drugs with distinct chemical properties and intended for three different pathologies and administration routes were successfully loaded into the beads with a drug entrapment efficiency exceeding 80 %. Notably, our findings revealed sustained drug release, irrespective of the drugs' chemical properties, culminating in the development of an injectable formulation. This formulation allows direct administration into the target site, minimizing systemic exposure, and thereby mitigating adverse effects. Our approach demonstrates the potential of LMP-based beads for tailored drug delivery systems with broad applications in diverse therapeutic scenarios.

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.

Novel liposomal formulations for protection and delivery of levodopa: Structure-properties correlation.

Liposomes are promising drug carriers for a wide range of central nervous system disorders, such as Parkinson's disease (PD), since they can protect active substances from degradation and could be administered intranasally, ensuring a direct access to the brain. Levodopa (LD), the drug commonly used to treat PD, spontaneously oxidizes in aqueous solutions and thus needs to be stabilized. Our investigation focuses on the preparation and the physico-chemical characterization of mixed liposomes to vehiculate LD and two natural substances (L-ascorbic acid and quercetin) that can prevent its oxidation and contribute to the treatment of Parkinson's disease. These co-loaded vesicles were prepared using a saturated phospholipid and structurally related cationic or analogue N-oxide surfactants and showed different properties, based on their composition. In particular, ex-vivo permeability tests using porcine nasal mucosa were performed, denoting that subtle variations of the lipids structure can significantly affect the delivery of LD to the target site.

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.

Multiple-column RP-HPLC retention modelling based on solvatochromic or theoretical solute descriptors.

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.

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).

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.