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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

An NMR and chemometric analytical approach to classify beers according to their brand identity was developed within the European TRACE project (FP6-2003-FOOD-2-A, contract number: 0060942). Rochefort 8 Trappist beers (47 samples), other Trappist beers (76 samples) and non-Trappist beers (110 samples) were analyzed by 1 H NMR spectroscopy. Selected NMR signals were measured and used to build classification models. Three different classification problems were identified, namely Trappist versus non-Trappist, Rochefort versus Non-Rochefort, and Rochefort 8 versus non-Rochefort 8. In all the three cases, both a discriminant and a modeling approaches were followed, using partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA), respectively, leading to very high classification accuracy as evaluated by external validation. Information regarding chemical composition was also obtained: Trappist beers contain a higher amount of formic and pyruvic acids and a lower amount of acetic acid and alanine with respect to non-Trappist ones. Rochefort beers turned out to have also a higher content of propanol and isopentanol with respect to non-Rochefort samples. Finally, Rochefort 8, shows the highest content of pyruvic acid and the lowest content of gallic, fumaric, acetic acids, adenosine, uridine, 2-phenylethanol, GABA, and alanine.

The effect of roasting conditions on the NIR and HPLC profiles of coffee samples was systematically evaluated by ANOVA-simultaneous component analysis (ASCA), also as a function of the varietal origin (Arabica or Robusta) of the beans. With respect to previously published paper, the use of ASCA allowed not to limit the characterization to individual analytes or specific family of compounds, by extending it to whole instrumental profiles, thus providing a holistic characterization of the roasting process, also in terms of how it changes depending on the species of the raw material. In particular, it was evidenced that both the species and the roasting time have a significant effect on the fingerprints and that, only in the case of NIR spectra, their interaction is significant as well. Moreover, it was possible to highlight what are the main variations in the experimental profiles ascribable to the effects of the controlled factors and in particular, in the case of roasting time, to discover a major longitudinal monotonic trend which is accompanied by a minor one that can be associated to the formation of intermediates. In a successive stage, based on the outcomes of ASCA, partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA) have been used to build classification models to authenticate the varietal origin of coffee beans by NIR spectroscopy. PLS-DA resulted in about 98% correct classification rate on the test set (100% for Arabica and 95% for Robusta), while sensitivity and specificity values almost always above 90% were achieved with SIMCA (93% and 96%, respectively, for Arabica and 77% and 96% for Robusta).

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.

The oil extracted from the seeds of niger (Guizotia Abyssinica), collected from 6 different regions of Ethiopia and India, was characterized in terms of its fatty acid, sterol and triglyceride distribution and of its total tocopherol content. Where available, the results have been compared with those reported in the literature or with data on oils from the same botanical family (Compositae). The analytical data have then been elaborated by supervised pattern recognition techniques (Linear Discriminant Analysis (LDA) and Artifical Neural Network (ANN)) in order to authenticate the geographical origin of the samples. Eight and 11 variables were necessary to achieve a complete discrimination respectively of the country and of the region of origin of the oils under exam, when using LDA, whereas ANN required a smaller number of experimental variables (4 and 6), due to its non-linearity.

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.

This paper is concerned with the structural data obtained for two amorphous binuclear complexes of iron(III) and aluminum(III) with chromium(III)-diethylentriaminepentaacetic acid (chromium(III)-DTPA, CrL(2)(-)) using the energy-dispersive X-ray diffraction technique. Fe(OH)CrL(H(2)O)(6) and Al(OH)CrL(H(2)O)(6) are binuclear complexes, the metals ions being bridged via oxygen atoms. The metal ions are all octahedrally coordinated.

Supervised pattern recognition (classification) techniques, i.e., the family of chemometric methods whose aim is the prediction of a qualitative responseon a set of samples, represent a very important assortment of tools for solving problems in several areas of applied analytical chemistry. This paper describes the theory behind the chemometric classificationtechniques most frequently used inanalytical chemistry together with some examples of their application to real-world problems.

Beverages and yogurts containing pineapple fruit or juice were analyzed in order to assess the variability of concentration levels of allyl hexanoate, present as added flavoring. Average concentrations in products with pineapple as the main fruit ingredient were in fair agreement with average use level data reported from industry surveys for the relevant food categories. Dietary exposure to allyl hexanoate due to the consumption of these products was estimated by screening techniques developed by the European Food Safety Authority, using use level data provided by the industry: estimates were in the same order of magnitude as the estimates obtained for regular consumers who would be loyal to the pineapple yogurt and beverage products containing the highest observed concentration of the substance of interest.

Quantitation of tr-cinnamaldehyde, safrole and myristicin was carried out in 70 samples of cola-flavoured soft drinks purchased in eight European countries with the purpose of assessing the variability in the levels of these substances. Results indicated a limited variability in the content of the three substances: the ratio between the 90th and the 10th percentile concentration amounted to 21, 6 and 13 for tr-cinnamaldehyde, safrole and myristicin, respectively. The uncertainty in the assessment of dietary exposure to these substances due to the variability of their level in cola-flavoured drinks was low. Based on these analytical data and on refined food consumption data, estimates of exposure to safrole associated to cola drink consumption, along with Margin of Exposure (MOE) values, were obtained. For high consumers of cola-flavoured soft drinks in certain age groups, within some European countries, MOE values lower than 10,000 resulted, MOE values of 10,000 or higher having been stated by the EFSA as a quantitative criterion to identify low concern from a public health point of view and low priority for risk management actions. The lowest MOE values, from 1900 to 3000, were observed for children and teen agers in the United Kingdom and Ireland.

In this study, near-infrared spectroscopy coupled to chemometrics is used to build an analytical protocol to authenticate the origin of pistachio nuts (Pistacia vera L.), a high value-added food product. In particular, 483 samples from six different origins (Sicily, India, Iran, Syria, Turkey and U.S.A.) were analyzed by NIR spectroscopy. Spectra were recorded on half seeds cut longitudinally in reflectance mode. Spectral data were then processed by chemometrics to build classification models by SIMCA and PLS-DA. The discriminant approach resulted in classification accuracies higher than 90% for most of the classes. On the other hand, SIMCA built class-models with high sensitivity and specificities, the only exception being the two categories Turkey and Iran, whose heterogeneity resulted in a poorer specificity (anyway higher than 80%). In particular, the results obtained for the samples coming from Bronte (Sicily), the only PDO pistachio production in Europe – 95.5% non-error rate in PLS-DA, 90% sensitivity and 97% specificity in SIMCA, as evaluated on the external test set – are very promising from the viewpoint of the authentication of this product. In general, the results show that the coupling of NIR spectroscopy to chemometric classification techniques can be a valuable tool for tracing the origin of pistachio nuts, providing a reliable authentication in a rapid, relatively cheap and non-invasive way.

Introduction I n recent years, food authentication and food traceability have emerged as issues of primary and common concern for both consumers and producers. Because of this, the number of researchers who tackle these problems by combining chemometrics coupled with a fingerprinting technique continues to increase. This kind of approach, indeed, has the undeniable advantage of allowing, in many cases, the use of rapid and relatively inexpensive analytical techniques that can, nonetheless, lead to reliable and accurate results. Among these analytical methods, an important role is played by the spectroscopic techniques and in particular in the infrared wavelength ranges. These are often chosen as the ideal analytical methods for the development of food quality control methodologies because of their inherent characteristics of non-destructivity, speed and cheapness of analysis, as well as not needing any sample pre-treatment. However, food quality control, in particular when traceability and authentication are concerned, is a complex problem for the chemist, and sometimes the acquisition of a single fingerprinting signal and the subsequent construction of a chemometric classification model is not enough to develop a methodology reliable enough for the intended purpose. In such cases, it is often possible to rely on the possibility of acquiring more than one single signal for each sample, and then to use the combined information from the two (or more) techniques to develop the final chemometric model. In this way, it is possible, sometimes, to benefit from the specific advantages and characteristics of the different techniques to create a more reliable and stable final model, in particular whenever there is complementarity between the information provided by the techniques employed. In these cases, however, the problem of how to combine the different information coming from the various analytical methods, namely the data fusion issue, is of major importance for the accuracy and reliability of the final results of the proposed methodology. In this context, the use of near infrared (NIR) and mid infrared (MIR) spectroscopy to solve authentication or traceability issues has already been described by many researchers, showing the great potential that both these techniques have in the field of food quality control analysis. Moreover, the peculiar characteristics of NIR and MIR, being different from one another, make them perfectly adaptable to being used together. Due to its greater molecular selectivity, the use of MIR spectroscopy often provides easier spectral interpretation than NIR, and it can sometimes be more related to some compound that is typical of the investigated food. NIR spectroscopy is more useful for its peculiarity of giving an overall fingerprint of the entire sample and it is commonly applied for the analysis of foods with excellent results. In this work, the problem of the traceability of extra virgin olive oils coming from the PDO Sabina (Italy), already tackled by the same authors by means of MIR and NIR spectroscopy separately, is handled by means of different strategies of data fusion, in order to improve the results obtained from the two techniques and to demonstrate the synergistic effect of coupling information obtained from the two wavelength regions.

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.

Publisher Summary This chapter presents the description of some chemometric approaches to the prediction and verification of the botanical and geographical origin of extra virgin oil samples together with some examples taken from the authors' experience. It focuses on the geographical and varietal origin. When the complexity of the problem increases, it is necessary also that the complexity and non-linearity of the model used increases accordingly. One of the examples involving the use of class-modeling techniques has shown how these methods are more suited to use in the problems involving the authentication of Protected Denomination of Origin (PDO) products. They not only provide the usual non-error rate, but also additional figures of merit like sensitivity and specificity that can help understand which class is more likely to be confounded with which. Moreover, they allow the identification of samples not fitting any model.

Enantiomers are isomers that are the mirror image of one another and that, except for the property of rotating the plane-polarized light in opposite direction, share common physical and chemical properties. However, quite often it can happen that their pharmacological properties are significantly different so that one enantiomer is active while the other one doesn't produce any effect or, in some cases, can result even noxious. It is evident, therefore, that the possibility of determining the excess of the most abundant enantiomer in a mixture (enantiomeric excess) is of capital importance, especially in the quality control of drugs. In this paper, the possibility of quantifying the enantiomeric excess in mixtures of two pharmacologically important active principles (namely mandelic acid and ketoprofen) by means of multivariate calibration of IR data is explored. Since the measurements are carried out on KBr pellets, different background correction and data pre-treatment techniques have been used. Pretreated data have then been analyzed by Partial Least Squares Regression (PLS) and Multivariate Curve Resolution (MCR), considering at first the whole spectrum and later just a more limited spectral region selected by a sequential application of backward interval PLS and genetic algorithms (bi-PLS-GA). Results have proved to be significantly accurate both by internal and external validation.

Complex formation by chromium(III) with diethylentriaminepentaacetic acid (DTPA) has been studied by potentiometry and spectrophotometry. In equimolar solutions or with a ligand excess, complexation is practically complete at pH > 1 and protonation equilibria have been calculated. Molar absorbtivities of the different species have been evaluated. Equilibrium constants for complex formation have been calculated in 1 mol dm−3 (Na,H)ClO4 at 20°C.

The possibility of using two different artificial neural networks architectures (multi-layer feed-forward, MLF-NN, and counterpropagation, CP-NN) for the classification of 255 durum wheat samples from Sicily (Italy) was investigated and the performances of the optimal models were compared both among each others and to those resulting from the application of traditional chemometric pattern recognition techniques. When considering predictive ability over an independent test set, counterpropagation NN performed best, being able to correctly predict about 82% of the external validation samples (the corresponding predictive ability for MLF-NN, LDA and QDA was 72.0%, 50.9% and 52.7%, respectively.

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.

In this paper our previous researches dealing with compatibility, thermoanalytical characterization, the kinetics of thermal degradation of acetaminophen, either pure or contained in some commercial pharmaceutical formulations, have found applications outlets. In a previous investigation the possible interactions between acetaminophen and four excipients contained in the commercial pharmaceutical formulations were tested. As a continuation of this research in the present study an analytical method based on differential scanning calorimetry (DSC) was applied to determine the acetaminophen content of four commercial pharmaceutical formulations. For a fifth drug it was shown that the method is not applicable owing to observed incompatibility with one of the excipients. Finally, the analytical results obtained were compared with those derived from two UV spectrophotometric methods (one, i.e., “direct method,” recommended by the Pharmacopeia and the other based on the first-order derivative UV spectra).

The problem of authenticating extra virgin olive oil varieties is particularly important from the standpoint of quality control. After having shown in our previous works the possibility of discriminating oils from a single variety using chemometrics, in this study a combination of two different neural networks architectures was employed for the resolution of simulated binary blends of oils from different cultivars. In particular, a Kohonen self-organizing map was used to select the samples to include in the training, test and validation sets, needed to operate the successive calibration stage, which has been carried out by means of several multilayer feed-forward neural networks. The optimal model resulted in a validation Q 2 in the range 0.91–0.96 (10 data sets), corresponding to an average prediction error of about 5–7.5%, which appeared significantly better than in the case of random or Kennard–Stone selection.

A simple, fast and relatively inexpensive spectrophotometric method for the identification and the quantification of the individual components of the Italian general denaturant in alcohol samples is proposed. In particular, it is shown that bitrex (a quaternary ammonium salt), whose UV spectrum is completely masked by those of the other denaturant components, can be identified using its reaction with disulphine blue VN-150 (an anionic dye indicator), which leads to the formation of an intensely colored ion-association complex (mole ratio 1:1), easily extractable in chloroform. As far as the quantitative detection is involved, it is however necessary to shake the chloroform phase in the presence of 1 mol L(-1) NaClO(4) aqueous solution because of the fast adsorption of the ion pair on the walls of the glass cell. Perchlorate anion, due to mass action, substitutes the anionic dye indicator in the association complex: as a consequence, disulphine blue passes to the aqueous phase, where its absorbance at lambda=640 nm is measured. On the other hand, C.I. Reactive Red 24 dye is easily identifiable from the visible spectrum of the product without any further pretreatment: its concentration can be determined measuring the absorbance at lambda=542 nm. Thiophene, being significantly more concentrated than the other components, can be identified from the UV spectrum of a 1:100 diluted solution of the alcohol sample and quantitatively determined measuring the absorbance at lambda=230 nm. Lastly, methyl ethyl ketone (MEK) can be identified from the UV spectrum of a 1:5 diluted solution of the alcohol sample and quantitatively determined measuring the absorbance at lambda=273 nm. However, more accurate results can be obtained using a multiwavelength analysis in the range 220-250 and 250-310 nm for the determination of thiophene and MEK, respectively. Validation on standard denatured alcohol samples has proven the method to be both accurate and sufficiently precise (within- and between-days repeatability

Two chemometric class-modeling techniques (SIMCA and UNEQ) have been used to authenticate the origin of CDO wine samples from Italy. While SIMCA modeling was performed using all variables, UNEQ requires a preliminary variable selection, leading to 4 variables only (Cu, Zn, antocyans and SO 2 ) being included in the models. Both techniques provided highly sensitive and specific category models and resulted also in a very reliable classification of samples (only one or two samples misclassified in SIMCA and UNEQ, respectively). A further investigation on the stability of the models with respect to the wine production year was carried out, showing that while SIMCA models failed to classify most of the samples produced in years different from those used in the modeling phase, quite all of the same samples were accepted by the corresponding UNEQ models. However, when SIMCA modeling was repeated using only the same 4 variables than UNEQ, comparable results were obtained, suggesting an effect of the choice of the experimental variables in the classification outcomes.

In this paper, a class-modeling technique based on Kohonen artificial neural networks is presented. In particular, in order for the Kohonen self-organizing map to operate as a class-modeling device, two main issues are identified: integrating the training set (composed of samples from a single category) with a set of uniformly distributed random vectors and computing a suitable probability distribution associated to the positions on the 2D layer of neurons. Both the identified features concur in defining an opportune class space. When used to analyze a real-world data set (classification of rice varieties), the proposed technique provided comparable and in some cases better results than the traditional chemometric techniques SIMCA and UNEQ.

Unpublished analytical data obtained by recovery tests by ‘standard addition’ method, concerning lecithin determinations in human serum, bile and amniotic fluid, using new derivative enzymatic–spectrophotometric method, recently pointed out by the authors, are reported and shortly discussed.

A counterpropagation artificial neural network (CP-ANN) approach was used to classify 1779 Italian rice samples according to their variety, using physical measurements which are routinely determined for the commercial classification of the product. If compared to the classical Principal Component Analysis, the mapping based on the Kohonen network showed a significantly better representational ability, being able to separate classes which appeared quite undistinguished in the PC space. From the classification and prediction viewpoint, the optimal CP-ANN was able to correctly predict more than 90% of the test set samples.

A comparison of the results obtained by applying three spectrophotometric methods (at fixed wavelength, second-derivative and multicomponent analysis) to the determination of gamma-oryzanol in rice bran oil is reported. At fixed wavelength the results are more accurate when using isopropyl alcohol, rather than n-heptane, to dilute the oil samples, because the absorption bands of gamma-oryzanol are red-shifted and the absorbance, measured at lambda(max)=327 nm, is less affected by the interference of the oil "matrix" (lambda(max)=314 nm in n-heptane).However, to obtain accurate results also in oils with a low content of gamma-oryzanol, it is necessary to perform the analysis using second-derivative ((2)D330.365) or multicomponent (lambda=310-360 nm) methods. The first one fully removes the interference of oil matrix whilst the second, which needs a specific computational program to process the spectrophotometric data, furnishes evidence the presence of some unexpected interference in the analysis and/or standards which are not representative of the analysed samples, from the square root of the sum of the squared differences at each point between the linear combination of the standards and the unknown spectra (RMS error).Finally, some aspects of the chemical, spectroscopic (UV, IR) and thermoanalytical (TG, DSC) behaviour of gamma-oryzanol and the values of the parameters which enable "computation" of its UV spectra are reported.

The possibility has been investigated of applying derivative analysis to a classical enzymatic-spectrophotometric method for lecithin determination for the purpose of developing an analytical direct method that does not require long pretreatment of the test sample even in the case of turbid samples. Several samples of drugs and food integrators containing lecithin were thus tested using both the standard and the derivative method and then comparing the results obtained. The RSD% values of measurements on real (food or drugs) samples were always

This work has focused on discriminating extra virgin olive oils from Sabina (Lazio, Italy) by olive fruit variety (cultivar). A set of oils from five of the most widespread cultivars (Carboncella, Frantoio, Leccino, Moraiolo, and Pendolino) in this geographical area was analyzed for chemical composition using only the Official Analytical Methods, recognized for the quality control and commercial classification of this product. The obtained data set was converted into a computer-compatible format, and principal component analysis (PCA) and a method based on the Fisher F ratio were used to reduce the number of variables without a significant loss of chemical information. Then, to differentiate these samples, two supervised chemometric procedures were applied to process the experimental data: linear discriminant analysis (LDA) and artificial neural network (ANN) using the back-propagation algorithm. It was found that both of these techniques were able to generalize and correctly predict all of the samples in the test set. However, these results were obtained using 10 variables for LDA and 6 (the major fatty acid percentages, determined by a single gas chromatogram) for ANN, which, in this case, appears to provide a better prediction ability and a simpler chemical analysis. Finally, it is pointed out that, to achieve the correct authentication of all samples, the selected training set must be representative of the whole data set.

A calorimetric (DSC) method for the analysis of starches and food-related compounds, based on the determination of onset and peak temperature (To, Tp) and the apparent enthalpy change (ΔH) of the starch gelatinization process is described. To set up the best experimental conditions to apply, the parameters that could affect the calorimetric response, such as temperature range of thermal analysis, water/starch ratio in the mixture, heating rate, size and weight of sample, time-temperature condition, were investigated. The method was applied to characterise starches, semolina and alimentary pasta.

The reactions of diethylenetriaminepentaacetic acid (DTPA=H5L) or chromium(III)-DTPA, a ‘chelating agent’, with oxovanadium(IV) were investigated in aqueous solution by potentiometric methods. Homo- and hetero-binuclear species were evidenced as well as mixed complexes with hydrogen or hydroxide ions. The stability constants for these equilibria were calculated in 1.0 mol 1−1 NaClO4 solution at 20‡C. The mononuclear (VO)H3L·H2O and the heterobinuclear (VO)CrL·5H2O complexes were also obtained as solid compounds and were characterized by IR spectroscopy and thermoanalytical techniques (TG and DSC).

Differential scanning calorimetry (DSC) has been used to investigate drug-excipient interactions and, in consequence, their compatibility. In particular, binary mixtures of nefazodone with magnesium stearate and lactose, and fosinopril with the same excipients were prepared and analysed, after proper conditioning. The thermoanalytical results were compared with those obtained by spectroscopic (UV, IR) and chromatographic (HPLC) analysis. We believe that changes in DSC runs cannot always be a sufficient condition to prove that some interaction occurs between drug and excipient during storage at room temperature. Some data on the spectroscopic characteristics of nefazodone and fosinopril drugs and their thermal behaviour are also reported.

The determination of the moisture and ash contents in wheat mills was developed using multistep programmed thermogravimetric analysis. Preliminary research to determine the most suitable analytical conditions (carrier gas and flow rate, furnace temperature, isothermal time, type and weight of sample and size of particles) was also carried out. Commercial flours milled from soft and hard wheats with a wide range of fibre content were analysed using both the proposed method and the Italian official methods. The same results were obtained from all these methods. Nevertheless, the thermogravimetric method has some advantages: the analysis takes place on the same sample; it requires very small samples; and it is easy and rapid.

The reactions between copper(II) or nickel(II) with thecis-pyridine-2-aldoxime complexes of the same metal ions have been investigated by spectrophotometric measurements. The homo and heterobinuclear chelates involved have been isolated as the chlorides and characterized by spectroscopic and thermoanalytical techniques. The first decomposition step is not dependent on the furnace atmosphere (N2 or O2) and is accompanied by a large exothermic effect (DSC curves in N2). TG curves show that binuclear complexes are generally slightly less stable than the respective mononuclear complexes but, in the case of [Cu(NiL2)Cl2(H2O)2], the complexation of copper(II) gives rise a drastic decrease of its thermal stability.

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

The thermal properties of some complexes of copper(II) with pyridine-2-aldoxime (HPAO), where the ligand appears either as the ion (PAO−) or as a neutral molecule, were determined in vacuo and in dynamic nitrogen and oxygen gas atmospheres. The study was carried out by thermoanalytical (TG, DTG. DTA), spectroscopic and spectrometric (UV-visible, IR, diffuse reflectance, mass) techniques.

Copper(II) complexes of 3- and 4-pyridinealdoxime have been prepared and analyzed by spectroscopic (ir and diffuse reflectance) and thermoanalytical (TG, DTG and DTA) techniques. For both these systems, only species with metal: organic ligand: chloride ratio of 1∶2∶2 were found, and the organic ligand appears as a neutral molecule. The spectral data suggest a polymeric structure where the copper(II) ions are in a distorted octahedral environment.