Partial least squares density modeling (PLS-DM) - a new class-modeling strategy applied to the authentication of olives in brine by near-infrared spectroscopy.

A new class-modeling method, referred to as partial least squares density modeling (PLS-DM), is presented. The method is based on partial least squares (PLS), using a distance-based sample density measurement as the response variable. Potential function probability density is subsequently calculated on PLS scores and used, jointly with residual Q statistics, to develop efficient class models.

Chemometric brains for artificial tongues.

The last years showed a significant trend toward the exploitation of rapid and economic analytical devices able to provide multiple information about samples. Among these, the so-called artificial tongues represent effective tools which allow a global sample characterization comparable to a fingerprint. Born as taste sensors for food evaluation, such devices proved to be useful for a wider number of purposes.

Comparison between classical and innovative class-modelling techniques for the characterisation of a PDO olive oil.

An authentication study of the Italian PDO (protected designation of origin) olive oil Chianti Classico, based on near-infrared and UV-Visible spectroscopy, an artificial nose and an artificial tongue, with a set of samples representative of the whole Chianti Classico production and a considerable number of samples from a close production area (Maremma) was performed. The non-specific signals provided by the four fingerprinting analytical techniques, after a proper pre-processing, were used for building class models for Chianti Classico oils. The outcomes of classical class-modelling techniques like soft independent modelling of class analogy and quadratic discriminant analysis-unequal dispersed classes were compared with those of two techniques recently introduced into Chemometrics: multivariate range modelling and CAIMAN analogues modelling methods.

Combining information from headspace mass spectrometry and visible spectroscopy in the classification of the Ligurian olive oils.

An electronic nose and an UV-Vis spectrophotometer, in combination with multivariate analysis, have been used to verify the geographical origin of extra virgin olive oils. Forty-six oil samples from three different areas of Liguria were included in this analysis. Initially, the data obtained from the two instruments were analysed separately.

Affinity prediction on A1 adenosine receptor agonists: the chemometric approach.

In this paper, we are presenting a quantitative-structure-activity relationship (QSAR) study performed on 21 selective A(1) adenosine receptor agonists plus the endogenous substrate, adenosine, so as to identify those predictors which play a key role in describing the binding of the ligand with the A(1) receptor. A large number of molecular descriptors plus a calculated receptor-agonist binding energy and atomic charges were taken into account to derive different QSAR models, using different regression techniques. The results obtained both with linear and nonlinear approaches converge to the selection of the same informative parameters, highlighting the correlation of these descriptors with the biological Response.