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In this study, the possibility of using the chromatographic fingerprint of the phenolic fraction of extra virgin olive oils coupled to chemometric classification techniques to trace the origin of extra virgin olive oils with a protected denomination of origin (PDO) was investigated. In particular, this approach was used to characterize the PDO Sabina (an oil producing area in the center of Italy). Partial least squares-discriminant analysis (PLS-DA) models were at first built on the HPLC chromatographic profiles recorded at three different wavelengths (254 nm, 280 nm and 340 nm). Better results were obtained by selecting the most relevant regions of the chromatograms by a combination of backward-interval-PLS and genetic algorithms (biPLS-GA), especially in the case of the data at 280 nm and 340 nm (about 85% correct classification rate). Lastly, the use of a mid-level data fusion approach to combine the regions selected at 280 and 340 nm allowed further improving the specificity of the traceability model for PDO Sabina towards other oils. Identification of the compounds corresponding to regions of the chromatograms selected by biPLS-GA using mass spectroscopy suggested that vanillic acid, p -coumaric acid, luteolin, pinoresinol, acetoxypinoresinol, apigenin and metoxyluteolin can play a significant role as traceability markers for the PDO Sabina.

A method for the quantification of the enantiomeric excess of two pharmaceutical active ingredients (ibuprofen and epinephrine) in the solid state based on coupling near infrared spectroscopy and chemometrics is proposed. In the case of ibuprofen, in the absence of excipients, the method resulted in an accurate prediction of the enantiopurity of the validation samples (RMSEP

In this paper, the potential of coupling mid- and near-infrared spectroscopic fingerprinting techniques and chemometric classification methods for the traceability of extra virgin olive oil samples from the PDO Sabina was investigated. To this purpose, two different pattern recognition algorithm representative of the discriminant (PLS-DA) and modeling (SIMCA) approach to classification were employed. Results obtained after processing the spectroscopic data by PLS-DA evidenced a rather high classification accuracy, NIR providing better predictions than MIR (as evaluated both in cross-validation and on an external test set). SIMCA confirmed these results and showed how the category models for the class Sabina can be rather sensitive and highly specific. Lastly, as samples from two harvesting years (2009 and 2010) were investigated, it was possible to evidence that the different production year can have a relevant effect on the spectroscopic fingerprint. Notwithstanding this, it was still possible to build models that are transferable from one year to another with good accuracy.

Artificial neural networks (ANNs) are non-linear computational tools suitable to a great host of practical application due to their flexibility and adaptability. However, their application to the resolution of chemometric problems is relatively recent (early 1990s). In this communication, different artificial neural networks architectures are presented and their application to different kinds of chemometric problems (mainly classification and regression) is discussed by means of examples taken from the authors' experience, stressing the pros and cons of ANNs with respect to traditional chemometric techniques.

In this paper, an example of the application of two chemometric class-modeling tools (SIMCA and UNEQ) to the pattern recognition of Italian extra virgin oils from three different Protected Denominations of Origin is reported. In particular, 200 oil samples from three different PDOs of Sicily (Monte Etna, Valli Trapanesi and Monti Iblei; harvests 2002 and 2003) have been considered. The models built using the whole data set (22 chemical and physico-chemical indices were determined on each sample) resulted in 87% (SIMCA) or 77% (UNEQ) predictive ability, as evaluated by leave-one-out cross-validation. Therefore, SIMCA seems to perform better on the complete data set. A further investigation on the subsets from each of the two production years has shown that the 2003 data (mainly from the category Valli Trapanesi) are significantly different from the 2002 ones. Interestingly, when performing class-modeling on each of these two subsets, UNEQ provides better (or at least comparable) results than SIMCA.

This work is the logical consecution of our previous investigation on the classification of “monocultivar” olive oils, in which 153 samples from the five cultivars Carboncella, Frantoio, Leccino, Moraiolo and Pendolino [harvested from 1997 to 1999, in the same geographical area (Sabina, Lazio)] were discriminated according to their variety, using Linear Discriminant Analysis (LDA) and Back-propagation Artificial Neural Network (BP-ANN). This study has been now extended to include 50 new samples from three (Frantoio, Leccino, Moraiolo) of the previously examined cultivars and 373 samples from other nine olive varieties (Minuta, Moraiolo, Nocellara del Belice, Nociara, Ortice, Ortolana, Ottobratica, Peranzana, Racioppella and Sinopolese). These new samples were harvested from 1996 to 2000 in six Italian regions (Calabria, Campania, Lazio, Molise, Puglia and Sicilia). Kennard-Stone algorithm was used to partition the samples into the training and test sets and the value of Fisher F-ratio was computed to identify the most discriminating indices in order to reduce the number of input variables. A first study, restricted to the original five cultivars only, showed that 12 variables are necessary in the best LDA model, which was able to correctly recognize 92.7% of the training samples and to correctly predict 90.6% of the test set. On the other hand, the first seven variables only were necessary to obtain a null prediction error over the test and validation set samples using BP-ANN. In a successive stage, ANNs have been used to extend the study to all the 14 cultivars (576 samples). In this case, the first 16 variables according to the value of Fisher F-ratio were included in the best classification model. This model was able to correctly recognize all the samples in the training set (RMS

In this work a supervised chemometric approach to the discrimination of Italian honey samples from different floral origin is presented. The analytical data of 73 Italian honey samples from six varieties (chestnut, eucalyptus, heather, sulla, honeydew, and wildflower) have been processed by Linear Discriminant Analysis (LDA), using two different variable selection procedures (Fisher F-based and stepwise LDA). Three and two variables, respectively have been necessary to obtain a 100% predictive ability as evaluated by cross-validation. Successively, a class modeling approach has been followed, using UNEQ. The resulting models showed 100% sensitivity and specificity.

A comparison was performed of the analytical results obtained by applying a well-known enzymatic-spectrophotometric method for the determination of choline containing phospholipids in biological human fluids (sera, bile and amniotic fluids) following the standard procedure method, or else using the first or second derivative methods. In terms of result reproducibility the comparison was extended to include also a biosensor-based method developed in recent years by the present authors. Some advantages are associated with using the first derivative method in the case of serum samples containing blood traces, especially via the 'Gran's plot' method.

Supervised pattern recognition appears to be a useful tool to authenticate foodstuffs according to their geographical or varietal origin, when a set of samples whose classification is known a priori are available. In this work, linear discriminant analysis and artificial neural networks trained by the back-propagation algorithm have been used to discriminate rice bran oils manufactured in three different countries (Italy, Thailand and Switzerland) according to their geographical origin. The variables to be included in the mathematical models have been chosen by means of Fisher F-ratio value among the chemical indices routinely determined on vegetable oils (particularly fatty acids, triglycerides and sterol composition). The prediction ability of all the classifiers was 100% as evaluated by cross-validation.

A sensitive, simple, rapid and precise method for the simultaneous determination of fosinopril (FOS) and hydrochlorothiazide (HCT) in pharmaceutical formulations is presented. These active ingredients are extracted in aqueous solution and measured by multiwavelength UV spectrophotometry using the program QUEST. HCT acts as an internal standard to verify the accuracy of the analysis. Some aspects of the chemical, spectroscopic and thermoanalytical behaviour of FOS are also reported.

The reactions of chromium(III)—DTPA “chelating agent” with manganese(II), iron(II), cobalt(II), nickel(II), copper(II) and zinc(II) have been investigated in aqueous solution by potentiometric methods. The heterobinuclear chelates with 1:1:1 metal(II):chromium(III):DTPA molar ratio have been precipitated and characterized by spectroscopic and thermoanalytical techniques. All the metal ions are six-coordinated with an octahedral configuration (except the four-coordinated copper(II)) and are covalently bound to the free iminodiacetic group of the chromium(III)—DTPA complex. The thermal stability depends on the furnace atmosphere (N 2 or O 2 ), whereas the nature of the metal(II) ion is less important because the anhydrous complexes begin to decompose at about the same temperature. Exceptions to this rule are the copper(II) and iron(II) complexes, which are less stable.

Captopril (H2L) reacts with cobalt(II) in aqueous solution at a neutral pH forming a deep red complex. The reaction rate depends on the temperature, pH, metal ion and ligand concentrations. A solid compound precipitates by acidification of the solution. The chemical, potentiometric, and spectroscopic analyses of the solid complex give the simplest formula, Co(HL)2·H2O, and suggest that the sequence of the bonds is S-Co-S, with the carboxylic group of the ligand remaining uncoordinated. The thermal behaviour of the captopril and its cobalt complex was studied by thermogravimetry, differential thermal analysis and differental scanning calorimetry in dynamic nitrogen and oxygen atmospheres.

The spectroscopic characteristics (IR and diffuse reflectance) and thermal properties (TG, DSC) of the solid compounds obtained by the direct reaction of pyrazine-2-carboxylic acid with manganese(II), iron(II), cobalt(II), nickel(II), copper(II) and zinc(II) are reported and discussed. The stoichiometry and thermal stability order of the compounds obtained are determined and their structures hypothesized.

The thermal behaviour of [Co(en)n(phen)m]Cl3 complexes has been studied using thermogravimetry (TG), differential thermogravimetry (DTG) and differential thermal analysis (DTA) in air, nitrogen and oxygen atmospheres. The effect of the stoichiometry of the complexes and that of the gas atmosphere in the furnace chamber on the thermal decomposition reaction is evidenced and discussed. The following thermal stability order has been found [Co(en)3]Cl3 ≌ [Co(en)2(phen)]Cl3 > [Co(en)(phen)2]Cl3 ⪢ [Co(phen)3]Cl3