Alternative common bases and signal compression for wavelets application in chemometrics.

Representation or compression of data sets in the wavelet space is usually performed to retain the maximum variance of the original or pretreated data, like in the compression by means of principal components. In order to represent together a number of objects in the wavelet space, a common basis is required, and this common basis is usually obtained by means of the variance spectrum or of the variance wavelet tree. In this study, the use of alternative common bases is suggested, both for classification and regression problems. In the case of classification or class-modeling, the suggested common bases are based on the spectrum of the Fisher weights (a measure of the between-class to within-class variance ratio) or on the spectrum of the SIMCA discriminant weights. In the case of regression, the suggested common bases are obtained by the correlation spectrum (the correlation coefficients of the predictor variables with a response variable) or by the PLS (Partial Least Squares regression) importance of the predictors (the product between the absolute value of the regression coefficient of the predictor in the PLS model and its standard deviation). Other alternative strategies apply the Gram-Schmidt supervised orthogonalization to the wavelet coefficients. The results indicate that, both in classification and regression, the information retained after compression in the wavelets space can be more efficient than that retained with a common basis obtained by variance.

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.

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. In this review, a critical overview of artificial tongue applications over the last decade is outlined. In particular, the focus is centered on the chemometric techniques, which allow the extraction of valuable information from nonspecific data. The basic steps of signal processing and pattern recognition are discussed and the principal chemometric techniques are described in detail, highlighting benefits and drawbacks of each one. Furthermore, some novel methods recently introduced and particularly suitable for artificial tongue data are presented.

Development of a voltammetric electronic tongue for discrimination of edible oils.

In this paper, we propose a novel strategy to perform cyclic voltammetric measurements with a platinum microelectrode directly in edible oil samples. The microelectrode was employed as an electronic tongue that, along with the application of chemometrics to the current-potential responses, proved useful for discriminating oils on the basis of their quality and geographical origin. The method proposed here is based on the use of suitable room temperature ionic liquids, added to oils as supporting electrolytes to provide conductivity to the low-polarity samples. The entire voltammograms, recorded directly on the oil/RTIL mixtures, were processed via principal component analysis and a classification technique (K nearest neighbors), to extract information on samples characteristics. Data processing showed that oils having different nature (i.e. maize and olive) or geographical origin (i.e. olive oils coming from different regions) can be distinguished.

Analysis and classification of bacteria by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and a chemometric approach.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a useful technique for the identification of bacteria on the basis of their characteristic protein mass spectrum fingerprint. Highly standardized instrumental analytical performance and bacterial culture conditions are required to achieve useful information. A chemometric approach based on multivariate analysis techniques was developed for the analysis of MALDI data of different bacteria to allow their identification from their fingerprint. Principal component analysis, linear discriminant analysis (LDA) and soft independent modelling of class analogy (SIMCA) were applied to the analysis of the MALDI MS mass spectra of two pathogenic bacteria, Escherichia coli O157:H7 and Yersinia enterocolitica, and the non-pathogenic E. coli MC1061. Spectra variability was assessed by growing bacteria in different media and analysing them at different culture growth times. After selection of the relevant variables, which allows the evaluation of an m/z value pattern with high discriminant power, the identification of bacteria by LDA and SIMCA was performed independently of the experimental conditions used. In order to better evaluate the analytical performance of the approach used, the ability to correctly classify different bacteria, six wild-type strains of E. coli O157:H7, was also studied and a combination of different chemometric techniques with a severe validation was developed. The analysis of spiked bovine meat samples and the agreement with an independent chemiluminescent enzyme immunoassay demonstrated the applicability of the method developed for the detection of bacteria in real samples. The easy automation of the MALDI method and the ability of multivariate techniques to reduce interlaboratory variability associated with bacterial growth time and conditions suggest the usefulness of the proposed MALDI MS approach for rapid routine food safety checks.

Modelling aroma of three Italian red wines by headspace-mass spectrometry and potential functions.

The aromas of 41 samples of wine from two Italian regions, Piedmont and Tuscany, were analysed by headspace-mass spectrometry. Samples were from three Italian wines (Barbera, Dolcetto and Chianti) produced in the same vintage, from different grape varieties and producing zones. The headspace generating conditions were optimised by full factorial experimental design then chemometric techniques were applied to verify the discriminating power of headspace-mass spectrometry among the three wine aromas. The modelling method based on potential function, applied on the first nine significant components of the 201 measured m/z, revealed best discrimination among the three wine aromas: cross-validated mean prediction rate of 96.7% and mean prediction rate of 83.3% on external test sets were obtained.

The information content of visible spectra of extra virgin olive oil in the characterization of its origin.

The information content of visible spectra has been evaluated, by means of some selected chemometrical techniques, for its ability to trace the geographical origin of extra virgin olive oils coming from several Mediterranean regions. Special attention was paid to extra virgin olive oil produced in West Liguria, a North Italy region which leans over the Mediterranean Sea and borders France. The peculiar organoleptic features of this "niche product" deserved the protected designation of origin "Riviera Ligure-Riviera dei fiori". Unfortunately, this expensive oil is often submitted to profitable adulterations, commonly involving addition of other cheaper Mediterranean oils. Using suitable transforms, such as profiles and derivatives, the visible spectra of extra virgin olive oils showed a very important discriminant power in that regards the geographical characterization of the studied samples. In particular, the developed class models for West Liguria oils have 100% sensitivity and specificity. Moreover, even if this paper is focused on West Liguria oil, it is important to emphasize that a similar study, involving a so widespread and timesaving technique, could be analogously developed for all the other Mediterranean regions taken into account and it could be used in other olive oil characterization problems.

Use of near-infrared spectroscopy and feature selection techniques for predicting the caffeine content and roasting color in roasted coffees.

Near-infrared spectroscopy (NIRS), combined with diverse feature selection techniques and multivariate calibration methods, has been used to develop robust and reliable reduced-spectrum regression models based on a few NIR filter sensors for determining two key parameters for the characterization of roasted coffees, which are extremely relevant from a quality assurance standpoint: roasting color and caffeine content. The application of the stepwise orthogonalization of predictors (an "old" technique recently revisited, known by the acronym SELECT) provided notably improved regression models for the two response variables modeled, with root-mean-square errors of the residuals in external prediction (RMSEP) equal to 3.68 and 1.46% for roasting color and caffeine content of roasted coffee samples, respectively. The improvement achieved by the application of the SELECT-OLS method was particularly remarkable when the very low complexities associated with the final models obtained for predicting both roasting color (only 9 selected wavelengths) and caffeine content (17 significant wavelengths) were taken into account. The simple and reliable calibration models proposed in the present study encourage the possibility of implementing them in online and routine applications to predict quality parameters of unknown coffee samples via their NIR spectra, thanks to the use of a NIR instrument equipped with a proper filter system, which would imply a considerable simplification with regard to the recording and interpretation of the spectra, as well as an important economic saving.

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. Then, the potential of the synergy between these two technologies for testing food authenticity and quality was investigated. Application of Linear Discriminant Analysis, after feature selection, was sufficient to differentiate the three geographical denominations of Liguria ("Riviera dei Fiori", "Riviera del Ponente Savonese" and "Riviera di Levante"), obtaining 100% success in classification and close to 100% in prediction. The models built using SIMCA as a class-modelling tool, were not so effective, but confirmed that the results improve using the synergy between different analytical techniques. This paper shows that objective instrumental data related to two important organoleptic features such as oil colour and aroma, supply complementary information.

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. The evaluation 'a priori' of these predictors could therefore represent a useful tool in the screening of large libraries of compounds and in the rational design of new selective agonists.

Optimisation of a new headspace mass spectrometry instrument. Discrimination of different geographical origin olive oils.

A fast head-space analysis instrument, constituted by an automatic sample introduction system directly coupled to a mass detector without performing any chromatographic separation, was assembled. A suitable and original response was computed to optimise, by experimental design, the measured signals for discrimination purposes. The volatile fractions of 105 extra virgin olive oils coming from five different Mediterranean areas were analysed. The rough information collected by this system was unravelled and explained by well-known chemometrical techniques of display (principal component analysis), feature selection (stepwise linear discriminant analysis) and classification (linear discriminant analysis). The 93.4% of samples resulted to be correctly classified and the 90.5% correctly predicted by cross-validation procedure, whilst the 80.0% of an external test set, created to full validate the classification rule, were correctly assigned.