Artificial nose, NIR and UV-visible spectroscopy for the characterisation of the PDO Chianti Classico olive oil.

An authentication study of the Italian PDO (Protected Designation of Origin) olive oil Chianti Classico, based on artificial nose, near-infrared and UV-visible spectroscopy, with a set of samples representative of the whole Chianti Classico production area and a considerable number of samples from other Italian PDO regions was performed. The signals provided by the three analytical techniques were used both individually and jointly, after fusion of the respective variables, in order to build a model for the Chianti Classico PDO olive oil. Different signal pre-treatments were performed in order to investigate their importance and their effects in enhancing and extracting information from experimental data, correcting backgrounds or removing baseline variations. Stepwise-Linear Discriminant Analysis (STEP-LDA) was used as a feature selection technique and, afterward, Linear Discriminant Analysis (LDA) and the class-modelling technique Quadratic Discriminant Analysis-UNEQual dispersed classes (QDA-UNEQ) were applied to sub-sets of selected variables, in order to obtain efficient models capable of characterising the extra virgin olive oils produced in the Chianti Classico PDO area.

Characterisation of PDO olive oil Chianti Classico by non-selective (UV-visible, NIR and MIR spectroscopy) and selective (fatty acid composition) analytical techniques.

An authentication study of the Italian PDO (protected designation of origin) extra virgin olive oil Chianti Classico was performed; UV-visible (UV-vis), Near-Infrared (NIR) and Mid-Infrared (MIR) spectroscopies were applied to a set of samples representative of the whole Chianti Classico production area. The non-selective signals (fingerprints) provided by the three spectroscopic techniques were utilised both individually and jointly, after fusion of the respective profile vectors, in order to build a model for the Chianti Classico PDO olive oil. Moreover, these results were compared with those obtained by the gas chromatographic determination of the fatty acids composition. In order to characterise the olive oils produced in the Chianti Classico PDO area, UNEQ (unequal class models) and SIMCA (soft independent modelling of class analogy) were employed both on the MIR, NIR and UV-vis spectra, individually and jointly, and on the fatty acid composition. Finally, PLS (partial least square) regression was applied on the UV-vis, NIR and MIR spectra, in order to predict the content of oleic and linoleic acids in the extra virgin olive oils. UNEQ, SIMCA and PLS were performed after selection of the relevant predictors, in order to increase the efficiency of both classification and regression models. The non-selective information obtained from UV-vis, NIR and MIR spectroscopy allowed to build reliable models for checking the authenticity of the Italian PDO extra virgin olive oil Chianti Classico.

Computer-assisted modelling and optimisation of reversed-phase high-temperature liquid chromatographic (RP-HTLC) separations.

The use of high temperatures (above 100 °C) in reversed-phase liquid chromatography (RP-HTLC) has opened up novel and enhanced applications for this essential separation technique. Although the favourable effects of temperature on LC have been extensively studied both theoretically and practically, its potential application to method development has barely been investigated. These favourable effects include enhanced speed, efficiency, resolution and detectability, as well as changes in selectivity, especially for polar and ionisable compounds, and the emergence of new options such as temperature programming and the concomitant use of solvent and temperature gradients, green separations, and so on. The recent availability of silica-based columns that routinely support high temperatures in addition to more conventional temperature-resistant columns (based on graphitised carbon, polymers and zirconium oxide) and dedicated column ovens that allow accurate temperature control up to 200 °C makes it possible to conceive of RP-HTLC as a routine separation technique in the modern analytical laboratory. On the other hand, the addition of temperature as a new optimisable parameter to RPLC adds further complexity to method development. Thus, new computer-assisted optimisation tools that extend the capabilities of current computer-assisted tools are being specifically developed for this type of separation. A new specially developed computer-assisted method development (CAMD) tool is presented herein, and its efficiency is demonstrated. This CAMD is based on the development of a rugged retention model for peaks, allowing the simulation of any kind of RP-HTLC separation, including isocratic, linear, curved, multilinear and stepwise gradients of solvent composition concomitant with constant, linear and multilinear temperature gradients. Both the retention models and the unattended optimisation of separations are driven by evolutionary algorithms, thus providing negligible-cost, rapid, highly efficient, and user-friendly optimisation processes.

NIR and UV-vis spectroscopy, artificial nose and tongue: comparison of four fingerprinting techniques for the characterisation of Italian red wines.

Four rapid and low-cost vanguard analytical systems (NIR and UV-vis spectroscopy, a headspace-mass based artificial nose and a voltammetric artificial tongue), together with chemometric pattern recognition techniques, were applied and compared in addressing a food authentication problem: the distinction between wine samples from the same Italian oenological region, according to the grape variety. Specifically, 59 certified samples belonging to the Barbera d'Alba and Dolcetto d'Alba appellations and collected from the same vintage (2007) were analysed. The instrumental responses, after proper data pre-processing, were used as fingerprints of the characteristics of the samples: the results from principal component analysis and linear discriminant analysis were discussed, comparing the capability of the four analytical strategies in addressing the problem studied.

Multivariate range modeling, a new technique for multivariate class modeling: the uncertainty of the estimates of sensitivity and specificity.

MRM, multivariate range modeling, is based on models built as parallelepipeds in the space of the original variables and/or of discriminant variables as those of linear discriminant analysis. The ranges of these variables define the boundary of the model. The ranges are increased by a "tolerance" factor to take into account the uncertainty of their estimate. MRM is compared with UNEQ (the modeling technique based on the hypothesis of multivariate normal distribution) and with SIMCA (based on principal components) by means of the sensitivities and specificities of the models, the estimates of type I (sensitivity) and II error rates (specificity) evaluated both with the final model built with all the available objects and by means of cross validation. UNEQ and SIMCA models were obtained with the usual critical significance value of 5% and with the model forced to accept all the objects of the modeled category. The performance parameters of the class models are critically discussed focusing on their uncertainty.

Multivariate calibration.

The bases of multivariate calibration are presented with special attention to some points usually not considered or underevaluated, i.e., the sampling design, the number of samples necessary to obtain a reliable regression model, the effect of noisy predictors, the significance of the parameters used to evaluate the performance ability of the regression model.

Selection of useful predictors in multivariate calibration.

Ten techniques used for selection of useful predictors in multivariate calibration and in other cases of multivariate regression are described and discussed in terms of their performance (ability to detect useless predictors, predictive power, number of retained predictors) with real and artificial data. The techniques studied include classical stepwise ordinary least-squares (SOLS), techniques based on the genetic algorithms, and a family of methods based on partial least-squares (PLS) regression and on the optimization of the predictive ability. A short introduction presents the evaluation strategies, a description of the quantities used to evaluate the regression model, and the criteria used to define the complexity of PLS models. The selection techniques can be divided into conservative techniques that try to retain all the informative, useful predictors, and parsimonious techniques, whose objective is to select a minimum but sufficient number of useful predictors. Some combined techniques, in which a conservative technique is used to perform a preliminary selection before the use of parsimonious techniques, are also presented. Among the conservative techniques, the Westad-Martens uncertainty test (MUT) used in Unscrambler, and uninformative variables elimination (UVE), developed by Massart et al., seem the most efficient techniques. The old SOLS can be improved to become the most efficient parsimonious technique, by means of the use of plots of the F-statistics value of the entered predictors and comparison with parallel results obtained with a data matrix with random data. This procedure indicates correctly how many predictors can be accepted and substantially reduces the possibility of overfitting. A possible alternative to SOLS is iterative predictors weighting (IPW) that automatically selects a minimum set of informative predictors. The use of an external evaluation set, with objects never used in the elimination of predictors, or of "complete validation" is suggested to avoid overestimate of the prediction ability.

Cluster analysis: significance, empty space, clustering tendency, non-uniformity. II--Empty Space index.

The here presented Empty Space index (ES) evaluates the fraction of the information space without experimental points, i.e. the space where the distance from an experimental point is significantly larger than the mean distance between the experimental points themselves. ES can be used to eliminate the ambiguity of the some clustering indexes, that aim to evaluate the separation of the data set in clusters, but these clustering indexes are really a mixed measure of clustering, of empty space (the empty space does not necessarily correspond to the break between clusters) and of the degree of uniformity of the objects. The ES index can be used also to correct the MST index, the clustering index based on the distribution of edge lengths in the minimum spanning tree connecting the objects. The corrected MST index seems to be a reliable measure of the clustering degree.

Cluster analysis: significance, empty space, clustering tendency, non-uniformity. I--Statistical tests on the significance of clusters.

The agglomerative clustering methods and the tests usually applied to evaluate the significance of clusters are critically evaluated. Many clustering techniques can provide erroneous information about the existence of clusters. The single linkage technique is suggested to identify natural, well separated, clusters. The existing statistical tests on the significance of clusters are not satisfactory. A new statistical test, based on the distribution of the distances between the objects and their first nearest neighbor, is presented. The performances of the test are compared with those of the Sneath test and of the variance-ratio test on some artificial and real data sets.

Multivariate calibration: applications to pharmaceutical analysis.

The principles of multivariate calibration (MC) are presented, with reference to the main objectives of this chellometrics technique: the reduction of the variance in the prediction of a response variable (generally, a chemical quantity) and the possibility of the determination of the response in complex matrices with no or limited sample preparation, as in the case of the determination of a drug in a medicament. In both cases MC uses the whole information in a spectrum (a series of predictors). The possibility of the improvement of the MC performances, eliminating some useless, noisy, predictors is shown. Variable selection has been performed using two original techniques: a stepwise elimination procedure, based on the normalised coefficients of the regression equation relating the response to the predictors and a technique based on iterative repetitions of the regression technique (partial least squares regression, PLS), each time by weighting the predictors by their normalised regression coefficient computed in the previous cycle. These strategies are illustrated by means of different data sets, a synthetic example and a real example where MC, applied to near infrared spectroscopy, is used in the analysis of a drug. In this case also the application of an original MC technique is shown, where a joint regression model is obtained for two different instruments.

Zupan's descriptors in QSAR applied to the study of a new class of cardiotonic agents.

Recently a new class of molecular descriptors has been proposed and used in QSAR with simulated data and with regression performed by neural networks. In the present paper these descriptors (Zups, from the name of their author, Juri Zupan) have been slightly modified and then applied to a real data set with the aim of studying the structure-activity relationships of a new class of cardiotonics. Forty-one molecules (thirty-seven milrinone analogues, the two lead compounds amrinone and milrinone, and two commercial products) have been studied using classical chemometrical techniques such as PCA (Principal Components Analysis) and PLS (Partial Least Squares regression). Zups describe essentially the local geometry of the molecules. They show promising performances, as compared with other classical geometrical descriptors (as molecular volume, etc.), both in that regards the overall performances, measured by the C.V. Explained variance and in the interpretability of the regression equation. However they have not all the requirements of a good structure representation. Moreover some selectable parameters seem to have a great importance, so that the refinement of the regression model requires time and the evaluation step must be performed in condition of full-validation, because predictive optimisation is used in the selection of parameters, and the final model must be checked on molecules never used to refine the model or, in this case, the parameters of the structure representation.