Characterisation of Gas-Chromatographic Poly(Siloxane) Stationary Phases by Theoretical Molecular Descriptors and Prediction of McReynolds Constants.

Retention in gas-liquid chromatography is mainly governed by the extent of intermolecular interactions between the solute and the stationary phase. While molecular descriptors of computational origin are commonly used to encode the effect of the solute structure in quantitative structure-retention relationship (QSRR) approaches, characterisation of stationary phases is historically based on empirical scales, the McReynolds system of phase constants being one of the most popular. In this work, poly(siloxane) stationary phases, which occupy a dominant position in modern gas-liquid chromatography, were characterised by theoretical molecular descriptors.

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.

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.

Cross-column prediction of gas-chromatographic retention of polybrominated diphenyl ethers.

In this paper, we predict the retention of polybrominated diphenyl ethers (PBDEs) in capillary gas-chromatography (GC) within a useful range of separation conditions. In a first stage of this study, quantitative structure-retention relationships (QSRRs) of PBDEs in six stationary phases with different polarity are established. The single-column QSRR models are generated using the retention data of 126 PBDE congeners by multilinear regression (MLR) coupled to genetic algorithm variable selection applied to a large set of theoretical molecular descriptors of different classes.

Cross-column retention prediction in reversed-phase high-performance liquid chromatography by artificial neural network modelling.

Linear solvation energy relationships (LSERs) are commonly applied to model the effect of solute structure on the retention of analytes in reversed-phase high-performance liquid chromatography (RP-HPLC). Standard LSER approaches can be used, in principle, to predict RP-HPLC behaviour of unknown analytes under fixed separation condition. However, as solute structure is the only source of variability described by the model, a LSER established for a given column/eluent pair cannot be transferred to external separation conditions.