Analysis of the mineral composition of Italian saffron by ICP-MS and classification of geographical origin.

27 Saffron spices produced in three Italian regions, Abruzzo (L'Aquila), Umbria and Sardinia, were analysed by inductively coupled plasma-mass spectrometry to test potentiality of mineral composition for geographical traceability. A linear discriminant analysis (LDA) based on concentrations of 12 selected elements, Li, B, Na, Ga, Rb, Sr, Zr, Nb, Cs, Ba, Sm, and Hf, allows more than 80% of correct predictions in leave-one cross-validation. Four elements (B, Na, Sr and Rb), identified by step-wise LDA, provide more than 90% of correct predictions. Moreover, saffron spices cultivated in Sardinia and Central Italy can be distinguished under more severe validation conditions (20% of samples in the prediction set). In summary, mineral composition results a promising indicator of saffron geographical origin.

Cross-column prediction of gas-chromatographic retention of polychlorinated biphenyls by artificial neural networks.

In this paper, we build a multiple-column retention model able to predict the behaviour of polychlorinated biphenyls (PCBs) in capillary gas-chromatography (GC) 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 stationary 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. 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.

Retention modelling of polychlorinated biphenyls in comprehensive two-dimensional gas chromatography.

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 (GC×GC). We analyse the GC×GC 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 GC×GC behaviour of PCBs is properly modelled. In particular, the predicted two-dimensional GC×GC 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 GC×GC chromatogram of PCBs using experimental determination of only 1/3 retention data of representative congeners.

Adsorption of s-triazines onto polybenzimidazole: a quantitative structure-property relationship investigation.

The adsorption of 25 symmetric triazines (s-triazines) on polybenzimidazole (PBI) beads is investigated under equilibrium (batch) conditions. The observed adsorption isotherms of the selected compounds are accurately described by the Freundlich model, while the agreement between the Langmuir model and the experimental data is moderately worse, which seems to reflect the heterogeneous meso- and micro-porosity of PBI and polydispersion in the interaction mechanism. Methylthio- and methoxytriazines exhibit a greater adsorption tendency as compared with chlorotriazines, moreover, progressive dealkylation of amino groups results in a progressive increase of triazine uptake on PBI. Based on these evidences, the adsorption mechanism seems to be governed by a combination of pi-pi and hydrogen-bonding interactions. Genetic algorithm (GA) variable selection and multilinear regression (MLR) are combined in order to describe the effect of triazine structure on the extraction performance of PBI according to the quantitative structure-property relationship (QSPR) method. q(max), the amount of triazine adsorbed per weight unit of PBI assuming homogeneous monolayer (Langmuir) mechanism, exhibits a great variability within the set of investigated triazines and is the quantity here modelled by QSPR. On the other hand, the Freundlich constant, KF, which expresses the adsorption efficiency under multilayer heterogeneous conditions, even if markedly increases passing from chloro- to methylthio- or methoxytriazines, is less noticeably affected by the fine details of the adsorbate structure, as the number or nature of alkyl fragments bound to the amino groups. To quantitatively relate q(max) with the triazine structure GA-MLR analysis is performed on the set of 1664 theoretical molecular descriptors provided by the software Dragon. Finally, a four-dimensional QSPR model is selected based on leave-one-out cross-validation and its prediction ability is further tested on four representative triazines excluded from model calibration. The four descriptors selected by GA-MLR, all belonging to the class of three-dimensional GETAWAY (GEometry, Topology, and Atom-Weights AssemblY) descriptors, adequately represent the structural factors influencing the affinity of triazines to PBI in the batch extraction process.

Stabilization of chloroperoxidase by polyethylene glycols in aqueous media: kinetic studies and synthetic applications.

Chloroperoxidase (CPO) is one of the most versatile of the heme peroxidase enzymes for synthetic applications. Despite the potential use of CPO, commercial processes have not been developed because of the low water solubility of many organic substrates of synthetic interest and the limited stability due to inactivation by H(2)O(2). CPO catalytic properties have been studied in aqueous solutions in the presence of short-chain poly(ethylene glycol)s (PEGs), and the sulfoxidation of thioanisole, as model substrate, has been investigated. The addition of PEGs allows a better substrate solubilization in the reaction mixture and the enzyme to retain more of its initial activity, with respect to pure buffer. Kinetic studies were performed to optimize the experimental conditions, and complete enantioselective conversion to the (R)-sulfoxide (ee = 99%) was observed in the presence of PEG 200 and tri(ethylene glycol). The relevant stabilization of chloroperoxidase due to the presence of PEGs allows the enzyme to convert the substrate with significant product yields even after 10 days, with a consequent increase in enzyme productivity. This is a promising result in view of industrial application of the enzyme.