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Promoting and protecting the market of typical national territory’s products are fundamental from a commodity point of view. Often, high-added value foods are subjected to fraud. Chestnuts have extraordinary nutritional and organoleptic qualities and Italy is one of the biggest producers of this product. The purpose of the present study is to develop an analytical method suitable to authenticate the chestnut of Vallerano a PDO agro-food produced in Central-Italy. A total of 441 chestnuts were analyzed (323 PDO and 118 harvested in other Italian territories) by near infrared spectroscopy (NIR) and then classified using two different approaches: a discriminant one, partial least squares-discriminant analysis (PLS-DA), and a class-modelling one, soft independent modelling by class analogy (SIMCA). Both strategies led to very high prediction capability in external validation on a test set (classification accuracy in one case, and sensitivity and specificity in the other). Eventually, both the proposed approaches resulted suitable for a rapid and non-destructive authentication of this valuable product. In particular, the combination of NIR (collected on the hilum) and PLS-DA provided the best results, reaching 97.0% of total correct classification rate on the validation set.

Darjeeling black tea is a worldwide known tea variety which is currently part of the register of protected designations of origin (PDO) and protected geographical indications (PGI) as established by Commission Implementing Regulation (EU) No 1050/2011 of 20 October 2011. Therefore, preventing frauds against this product became increasingly important in order to protect producers and consumers from possible economic losses. Starting from this assumption, the present work aims at two different goals: the first one is to develop a rapid, non-destructive and relatively cheap method to distinguish PGI Darjeeling tea from other kinds of black teas, and the second one is to test a non-invasive approach suitable to detect adulterated Darjeeing tea samples. To achieve this goals, NIR spectroscopy has been coupled with two different classifiers: Partial Least Squares-Discriminant Analysis (PLS-DA) and Soft Independent Modelling of Class Analogies (SIMCA). Both provided satisfactory results in discriminating PGI samples from the other teas and from the adulterated Darjeeling.

Rice is one of the most widely consumed cereals, and it represents a staple food for several populations all over the world. One of the main peculiarities of this food commodity is that it can be stored for relatively long periods, maintaining its nutritional properties and its organoleptic attributes. Nevertheless, it is not uncommon that pests infest granaries, altering the characteristics of cereals, making them less valuable and/or un-edible. Avoiding the presence of insects in storehouses is a task difficult to accomplish; consequently, different methods for detecting pests’ infestation in food commodities have been developed. In general, they are based on physical sensors, or they exploit analytical techniques such as high-performance liquid chromatography, nuclear magnetic resonance spectroscopy, enzyme-linked immunosorbent assays or other bio-chemical devices to detect insects’ byproducts. Despite these approaches providing accurate results, they all present the same inconvenience: they are destructive. In the light of these considerations, the present work aims at developing a non-destructive NIR-based strategy to detect the presence of Plodia interpunctella, one of the most common intruders of granaries, in rice parcels. In order to achieve this goal, 1525 samples of rice have been analysed by NIR spectroscopy and then partial least squares discriminant analysis was used to discriminate the edible grains from the infested ones. The proposed methodology provided extremely good results, properly assigning 484 test samples over 500.

Walnuts have been widely investigated because of their chemical composition, which is particularly rich in unsaturated fatty acids, responsible for different benefits in the human body. Some of these fruits, depending on the harvesting area, are considered a high value-added food, thus resulting in a higher selling price. In Italy, walnuts are harvested throughout the national territory, but the fruits produced in the Sorrento area (South Italy) are commercially valuable for their peculiar organoleptic characteristics. The aim of the present study is to develop a non-destructive and shelf-life compatible method, capable of discriminating common walnuts from those harvested in Sorrento (a town in Southern Italy), considered a high quality product. Two-hundred-and-twenty-seven walnuts (105 from Sorrento and 132 grown in other areas) were analyzed by near-infrared spectroscopy (both whole or shelled), and classified by Partial Least Squares-Discriminant Analysis (PLS-DA). Eventually, two multi-block approaches have been exploited in order to combine the spectral information collected on the shell and on the kernel. One of these latter strategies provided the best results (98.3% of correct classification rate in external validation, corresponding to 1 misclassified object over 60). The present study suggests the proposed strategy is a suitable solution for the discrimination of Sorrento walnuts.

Background: The investigation of human bones unearthed from necropolises is a useful tool to enhance our knowledge about ancient cultures. In the present study, the possibility of using the activation energy (EA) values of thermogravimetric degradation processes coupled with exploratory analysis methods in order to investigate human remains, has been tested. Methods: Several human bones from four distinct necropolises have been analyzed by thermogravimetry and then thirteen different approaches have been used to estimate their activation energy of the degradation processes of carbonate and collagen. The entire set of data has been analyzed by Principal Component Analysis (PCA) in order to draw some preliminary considerations over the remains. Results: PCA analysis highlighted the possibility of recognizing grouping tendencies related to the funeral ritual bodies underwent and/or their age. Additionally, in the second part of the work, where the focus is on the activation energies of collagen and carbonates degradation processes estimated by the method which was considered the most reliable (i.e., the Arrhenius formula with the third order decay), some tentative considerations about a trend in cremation temperatures are drawn. Conclusion: The estimation of values from thermogravimetric signals combined with chemometrics is a useful tool for the investigation of bone samples, which allow obtaining additional info about trends and/or grouping tendencies in complex systems as human remains.

Pasta is a typical Italian food item obtained by durum wheat semolina/flour well-known and widely consumed all over the world. Since 2013, Gragnano Pasta, a typical aliment produced in a specific area in the South of Italy, has been awarded with the P.G.I. mark, remarking the high value of this product. Due to its peculiarity and its market value, it is important to characterize and authenticate the Gragnano Pasta. Considering this rationale, the present study aims at developing a non-destructive analytical methodology suitable for this goal. Consequently, the possibility of coupling Near Infrared spectroscopy (NIR) with two different classifiers has been tested. In particular, 949 samples of pasta were analysed, and then classified into categories Gragnano and non-Gragnano by Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogies (SIMCA). In order to externally validate models, samples were divided into a training and a test set of 749 and 200 objects, respectively. Both approaches provided excellent results; PLS-DA correctly classified all the Gragnano samples (and it misclassified only 1 object belonging to the other category), while SIMCA analysis (modelling Class Gragnano) led to 96.57% sensitivity and 100% specificity.

The aim of the present study is to authenticate Grappa spirits and to develop a non-destructive methodology which would allow detecting possible adulteration (by less valuable spirits) on this product. Grappa is an Italian alcoholic drink obtained by distillation of grape marks which has recently received the Geographical Indication (GI) label. As a high added-value product, it is relevant to develop methodologies which allow its authentication and detecting possible frauds (e.g., adulterations); and, whether feasible, it would be suitable to achieve these goals through non-destructive approaches (in order to minimize the economic loss). Mid Infrared (MIR) and Near Infrared (NIR) spectroscopies have been used for the authentication and the characterization of the spirits under investigation. The present work is conceptually divided into two parts: the first one, centered on the authentication of grappa spirits, focused on distinguishing them from other Italian distillates, and a second one aimed at developing an analytical methodology suitable to discern between pure and adulterated grappas. Both classification problems have been investigated by PLS-DA and by three multi-block strategies, i.e., Multi-Block Partial Least Squares (MB-PLS), Sequential and Orthogonalized Partial Least Squares (SO-PLS) and Sequential and Orthogonalized Covariance Selection (SO-CovSel) in order to test whether a data-fusion approach would lead to an improvements of the classification rates. The best results (in terms of predictions) were provided by multi-block strategies; in particular, they provided 100 % of correct classification when applied to discriminate pure and adulterated samples, suggesting these methodologies are definitely suitable for the proposed purpose.

Common hazelnuts are widely present in human diet all over the world, and their beneficial effects on the health have been extensively investigated and demonstrated. Different in-depth researches have highlighted that the harvesting area can define small variations in the chemical composition of the fruits, affecting their quality. As a consequence, it has become relevant to develop methodologies which would allow authenticating and tracing hazelnuts. In the light of this, the present work aims to develop a non-destructive method for the authentication of a specific high-quality Italian hazelnut, "Nocciola Romana," registered with a protected designation of origin (PDO). Thus, different samples of this fruit have been analyzed by near-infrared (NIR) spectroscopy and then classification models have been built, in order to distinguish between the PDO fruits and the hazelnuts not coming from the designated region. In particular, two different classification approaches have been tested, a discriminant one, partial least squares-discriminant analysis, and a class-modeling one, soft independent modeling of class analogies. Both methods led to very high prediction capability in external validation on a test set (classification accuracy in one case, and sensitivity and specificity in the other, all higher than 92%), suggesting that the proposed methodologies are suitable for a rapid and non-destructive authentication of the product.

A chromatographic procedure (HPLC-DAD) using a relatively rapid gradient has been combined with a chemometric curve deconvolution method, multivariate curve resolution-alternating least squares (MCR-ALS), in order to quantify caffeine and chlorogenic acid in green coffee beans. Despite that the HPLC analysis (at these specific operating conditions) presents some coeluting peaks, MCR-ALS allowed their resolution and, consequently, the creation of a calibration curve to be used for the quantification of the analytes of interest; this procedure led to a high accuracy in the quantification of caffeine and chlorogenic acid present in the samples. In a second part of this study, the possibility of classifying the green coffee beans on the basis of their cultivar (Arabica or Robusta), by partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA), has been explored. SIMCA resulted in 100% of sensitivity and specificity for the Arabica class, while for the Robusta, it reached 66.7% of sensitivity and 100% of specificity, or 100% of sensitivity and 100% of specificity, depending on the extraction procedure followed prior to the chromatographic analysis; PLS-DA achieved 100% of correct classification independently of the procedure used for the extraction.

Anthracene bioconversion was achieved by immobilized enzyme technology. An oxidation yield of 0.7 mg/L of polycyclic aromatic hydrocarbons reached 60% following 24 hours of incubation with laccase from Trametes versicolor covalently immobilized on glutaraldehyde activated chitosan at the optimal pH of 5 in the presence of diammonium 2, 2-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) as the radical mediator. High-performance liquid chromatography indicated that the main product of anthracene oxidation was 9, 10-anthraquinone which is less toxic than its precursor. Highly porous 3 mm diameter chitosan macrobeads were synthesized by precipitation in alkaline solution. Support activation with glutaraldehyde was confirmed by elemental analysis, thermogravimetry, and infrared spectroscopy. The bioreactor system was characterized for kinetic parameters obtaining a Michaelis Menten constant of 0.13 mM and a maximum rate of 0.0011 µmol/min/mg, thermal stability and reuse. The protein and glutaraldehyde...

Among grains, rice is one of the most widely consumed cereals in the world; it represents a staple food in great part of Asia and Africa, and it is also broadly diffused in America and Europe. One of the main issues of storing rice is to protect it from animal attacks; in particular, it is prone to insect infestation. Despite all the attempts made to avoid it (developing new physical barriers, traps and repellants), often food pests manage to break into granary and parcels, contaminating stored commodities. As a consequence, possible infestations must be continuously checked by producers and/or retailers. Different methods have been developed to detect insects in stored commodities, and, despite some of them demonstrated to perform well, they present the substantial limitation of being destructive. This latter characteristic undoubtedly leads to an obvious loss of product (and consequently, of profit), affecting farmers, retailers, and, finally, consumers. For this reason, the aim of the present work is to develop a methodology for the identification of insect infestation in stored rice by NIR spectroscopy coupled with discriminant and modeling classification methods. In particular, among all the different pests possibly present in granaries, the focus has been on detection of the Indian-meal moth (Plodia interpunctella), because it is considered one of the most common infesting insects. Different samples of rice, both infested and edible, coming from different farmers located in six different Countries (Cambodia, India, Italy, Pakistan, Suriname and Thailand) have been analyzed by NIR spectroscopy. Consequently, two different classification methods, Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA) have been applied in order to distinguish among infested and edible samples. In particular, PLS-DA allows correctly classifying 95.6% of the edible 97.5% of the contaminated samples (on the external validation set), whereas the SIMCA model, built only for the category of non-contaminated individuals, resulted highly specific (about 97%) but poorly sensitive on the test specimens. This latter approach (SIMCA) provided better predictions (in particular, in terms of sensitivity) when separate individual models were built subdividing samples in agreement with their country of origin.

International audience; Near infrared spectroscopy and thermogravimetry have been coupled with chemometric exploratory methods in order to investigate ancient (pre-Roman/Roman) human bones from two different necropolises in Central-South Italy (Cavo degli Zucchi and Elea Velia). These findings have been investigated by principal component analysis and they have also been compared with ancient human bones from two Sudanese necropolises (Saggai and Geili). Samples coming from African and European necropolises, mainly differ in two aspects: the burial procedures and their historical period. The ritual applied in the European region involved cremation, while the one applied in the African necropolises did not. Bones from Italian sites (Cavo degli Zucchi and Elea Velia) are Pre-Roman/Roman while the others (from middle Nile) come from the Prehistoric, Meroitic, and Christian Sudanese age. Near infrared spectroscopy and thermogravimetric measures have been analysed either individually or by a mid-level data-fusion approach. Principal component analysis of the near infrared spectroscopy data allowed differentiation between burnt and unburnt samples, while from the scores plots extracted from the principal component analysis model based on the entire derived thermograms, it was possible to recognize the different clusters related to the various dating of samples. The data-fusion analysis led to considerations similar to those obtained from the model based on thermogravimetry data. Finally, instead of inspecting the entire thermogravimetry curves, principal component analysis was carried out on carbonates, total collagen and water losses only. In this case, the data-fusion approach has led to extremely interesting results; in fact, this model clearly shows that samples group in separate clusters in agreement with their age and the different burial rituals.

Avola almond is part of the “Traditional Italian Agri-food Product” (PAT) list, as established by The Italian Ministry of agricultural food, forestry and tourism policies; this endorsement testifies its status as a high added-value product, and, consequently, it highlights the need of analytical methodologies suitable for its authentication. For these reasons, in the present study, the possibility of developing a non-destructive approach, aimed at distinguishing almonds cultivated in the Avola area from others presenting a different geographical origin, has been investigated. To fulfil this purpose, 227 almonds, cultivated in the Avola area or in other Italian territories, have been analysed by near infrared (NIR) spectroscopy coupled with Partial Least Squares-Discriminant Analysis (PLS-DA) and Soft Independent Modelling of Class Analogies (SIMCA). The two tested approaches achieved satisfactory results (in external validation) indicating both of them would represent a suitable tool for the purpose of the study.

Recently proposed innovative methods for dating archaeological finds reported in the literature are reviewed, together with the researches carried out in this field by our team, using several instrumental techniques (sensor, biosensor, electrochemical, thermal-analytical, and so on). In this framework, particular attention was then focused on examining the main currently adopted methods for bones dating. Lastly, the possibility of using thermogravimetry coupled to chemometrics for differentiating, quickly and inexpensively, finds of very ancient human fossil bones (several thousand years BC), from less ancient ones (few hundreds of years before and after Christ) is illustrated, together with a thorough discussion of the criteria (i.e., the collagen-carbonates content ratio, or the different types of carbonate) this approach is based on.

The results of the simultaneous application of thermogravimetry and chemometrics, which allowed differentiating, quickly and inexpensively, finds of very ancient human fossil bones (~ 6000 BC), from less ancient ones (some hundreds of years before and after Christ) are here shown. The main purpose of the present research was to highlight if the selection of one or another anatomical part of a fossil skeleton could influence the differentiation of the antiquity of bone finds provided by the chemometric–thermogravimetric approach. The results obtained have shown that the variability related to the selection of one or another anatomical part of the skeleton is practically irrelevant, if compared to that related to the ancientness of the skeleton investigated.

The determination of 5‑hydroxymethylfurfural (HMF) content in honey samples of different botanical origin by a NIR-chemometrics-based approach is reported. Spectral regions, statistical models, scatter and derivative correction together with smoothing pre-treatment were examined and selected on the basis of correlation coefficient and root mean square errors for both the calibration (R2c and RMSEC) and cross-validation (R2v and RMSECV). Prediction ability was tested on an external set of honey samples and the best results were achieved with a Partial Least Square regression approach coupled with a multiplicative signal correction of the scattering in the spectral region 4252–4848 cm1. The correlation coefficient in prediction (R2 = 0.98) as the residual predictive deviation (RPD = 3.3) suggest that the proposed model proves to be a powerful tool to fulfil quality goals in such a complex matrix as honey.

Combined effects of oxygen level reduction in the malaxation headspace and storage time up to 6 months on the volatile composition of a monovarietal extra-virgin olive oil (EVOO), obtained from cv. Carboncella olives, were investigated by applying a full factorial design approach (4 oxygen levels × 4 storage times) on EVOOs extracted on an industrial scale in two mills, equipped with "two-phase" and "three-phase" centrifugation systems, respectively. The outcoming data were analysed by the chemometric technique called ANOVA-simultaneous component analysis (ASCA). Both reduction of oxygen malaxation levels and storage time significantly affected the volatile profile of the extracted EVOOs. Reduction of oxygen malaxation levels hindered the formation of lipoxygenase derived volatiles (hexanal, 1-hexanol, (Z)-2-hexenal, (E)-2-hexen-1-ol, (Z)-2-penten-1-ol, 2,4-hexadienals), whereas prolonged storage times were associated with increased levels of autoxidation products (octane, hexanal, C10 hydrocarbons) and other compounds that could originate from exogenous microbial activity (1-octen-3-ol, 6-methyl-5-hepten-2-one, benzaldehyde, methyl salicylate).

Recently FT-IR spectroscopy has enjoyed renewed interest for detection and quantification of contamination by oils and grease (OG), especially in water samples. This is due to the development of a new approach using less harmful solvents. This paper presents the development of a new FT-IR method that uses n-hexane as a substitute to Freon 113 in the extraction process. This solvent has a high solubility for the desired organic compounds, low miscibility with water and a low boiling point to facilitate its removal from the extracted material. Results regarding the calibration curves, recovery, precision, detection and quantitation limits are presented. Even if the recovery is not too high (45%), the preconcentration factor of 2 × 103 permits the detection of OG at levels >83 μg/L.

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

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.

A rapid accurate and precise method for simultaneous determination of β-glucan and protein content in naked oat samples, based on the coupling of near-infrared spectroscopy and chemometrics, is presented. In particular, three different spectroscopic approaches [near infrared reflectance (NIR) and transmittance (NIT) on flour and NIT on whole grains] and various spectral pretreatments were considered. To account for the possibility of outlying samples, a robust version of the PLS algorithm (namely partial robust M-regression) was used. All the models resulted as accurate as the reference methods, reflectance spectroscopy being the technique providing the best outcomes. Variable reduction by inclusion of the most relevant predictors only (as evaluated by VIP scores) resulted in simpler and, in one case, more parsimonious models, without loss in accuracy.

Proton transverse magnetization decay curves of rice flour starch-water samples were measured and analysed for the presence of four components in the relaxation curve. T2 values were interpreted on the basis of the diffusive and chemical exchange model that provided evidence for extra granular bulk water and three more water populations whose relaxation rate is governed by diffusive and chemical exchange with starch components. The analysis of relaxation data provided information on dynamics of water molecules as well as on the size and dispersion of diffusive domains. Furthermore, by measuring solid to liquid ratio, transverse and longitudinal relaxation curves of starch-water mixtures at increasing temperatures - from 20 to 77°C - swelling and gelatinisation processes were monitored.

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.

A microwave-assisted extraction method was optimised for the recovery of bioactive compounds from Crocus sativus L. stigmas with the use of water/ethanol mixture. HPLC-DAD was employed to evaluate the extraction parameters, in particular, solvent type and volume, and the duration of the procedure. Microwave-assisted extraction enhanced the recovery of the active principles, limiting extraction time and solvent waste. Moreover, NIR experiments were performed in order to compare spectra in pseudo-absorbance of Saffron samples with different geographical origins through the application of the chemometric techniques. Moreover, the biological evaluation of crocin 1, safranal and its semisynthetic derivatives as selective inhibitors of five isoforms of human carbonic anhydrase was also explored.

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

A novel carrier, the N‐methyl, N‐dodecyl‐ephedrinium ion, was studied for the selective transport of some aromatic amino acids [phenylalanine (Phe), histidine (His) and tryptophan (Trp)] through bulk liquid membranes. Investigations of the main parameters limiting the aided transport of the amino acids were carried out. Transport rates that resulted were affected by the amino acid lipophilicity, the pH variations of feed and receiving solutions, the concentration, and the nature of countertransport ions. Further results indicate that the carrier can interact with amino acids by ion pairing and nonionic interactions. Membrane selectivity showed to be strictly related to kinetics of the amino acid release at the membrane/receiving interface, and its results were satisfactory when the receiving solution was kept in acidic conditions. In these conditions, when a mixture of the three amino acids was used as the feed solution, the transport rate of phenylalanine was over three times higher with respect...

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.

The effect of the use of different organic fertilizers on formation of aroma compounds in cv. Golden Delicious apples was investigated. Results from the first year of the study suggested that the application of three different organic fertilizers did not reflect on marked univocal differences in the profile of aroma compounds in the fruits. Nevertheless, some effects of the fertilization were observed; therefore the use of different organic fertilizers, characterized by different efficiency in N mineralization, seemed to have the potential to significantly affect the formation of aroma compounds in apple fruits.

The effects of the application of different organic and conventional fertilisers on some flavour quality attributes (aroma volatiles, sugars and organic acids) of cv. Golden Delicious apples were investigated by an experimental field trial in two harvest years (2010 and 2012). Through a balanced randomised block design, five organic fertilisation treatments (three different fertilisers at the same nitrogen dose, increase and fractionation of dose for one of the fertiliser) were compared to each other, to a conventional treatment based on a mineral fertiliser and to a non-fertilised control. Fertilisation treatments significantly affected the level in fruits of several flavour related compounds, such as some aroma volatiles, sugars and organic acids, but few of these responses were consistent across the two harvest years and of remarkable size. Even when treatments gave place to marked differences in the soil mineral nitrogen level, this reflected in a limited impact on flavour related compounds in the fruit, the strongest effect being a 45% change in C6-aldehydes level. The different organic fertilisation treatments weakly affected the considered fruit quality attributes. Significant differences were observed for several sensory attributes between apples coming from different fertilisation treatments and characterised by a quite similar chemical profile.

The reaction of aluminum(III) with chromium(III)-DTPA “complex chelating agent” has been investigated, in aqueous solution, at 20°C in NaClO4 1.0 mol dm−3 by potentiometric methods. In acidic medium and with ligand excess three different complex species (AlCrL+, Al(OH)CrL and Al(CrL)− 2) were evidenced and, knowing the protonation constants of the chromium(III)-DTPA complex, the stability constants were calculated. The comparison of these constants with those of the corresponding complexes of aluminium(III) with iminodiacetic acid proved the coordination of aluminum(III) occurs in all cases by iminodiacetic groups. The mononuclear [H2AlL(H2O)] and the heterobinuclear [Al(OH)CrL(H2O)6] complexes were also obtained as solid compounds and were characterized by X-ray and IR spectroscopy and thermoanalytical techniques (TG and DSC).

Differential scanning calorimetry was applied to the determination of diclofenac in three 'Voltaren' formulas. The pharmaceutical products (soluble tablets, suppositories and vials) were selected in order to show that calorimetric analysis is an easy technique to perform and can be competitive with other conventional methods.

Two methods for the determination of the diclofenac salts [sodium (DS) or diethylammonium (DA)] in three pharmaceutical formulations (tablets, suppositories and gel) are presented. In the first, diclofenac salt is determined both, by measuring the absorbance of the solutions at a fixed wavelength (λ = 276 nm) and using a multi-wavelength computational program to process the spectrophotometric data in a selected range (λ = 230– 340 nm). The above method can not be applied to the analysis of gel formulations because of the opalescence of these solutions. In this case, the analysis is performed measuring the peak-to-peak amplitude in the first-derivative UV spectrum (1D 261.296). In the second method, diclofenac (DH) is precipitated in acid medium and determined by the analysis of the endothermic peak (tp = 182 °C) in the DSC curve obtained in nitrogen atmosphere. Finally, some aspects of chemical (solubility, acid-base equilibria, redox reaction), spectroscopic (UV, IR) and thermoanalytical (TG, DSC) behaviour of DS and DH and the values of the parameters which enable to calculate the UV spectrum of DS in aqueous solution are reported.

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.

Dried egg pasta is an important and traditional food in the Italian cuisine, and the eggs in pasta improve its nutritional value and organoleptic properties. For this reason the percentage of eggs present in the products sold as “egg pasta” has to always be clearly reported in the label. In this respect, the present research addresses the possibility of developing a method which would allow fast, simple and economic determination of egg content in dried egg-pasta, using near-infrared spectroscopy and chemometric analysis. However, as it is very likely that the spectroscopic fingerprint can also be affected by the manufacturing process of this product, in particular by drying temperature and time, the effect of the manufacturing process on the spectral profile of egg-pasta samples was thoroughly investigated, using experimental design coupled to a multivariate exploratory data analytical technique called ANOVA–Simultaneous Component Analysis (ASCA). Moreover, once confirmed the significance of the drying effect on spectral shape, with the aim of building a calibration model to quantify the egg content in pasta samples irrespective of the manufacturing protocol adopted, a non-linear approach based on local regression, namely LWR-PLS, was investigated. This method allowed the determination of the egg content in external validation samples with low error (RMSEP = 1.25), resulting in predictions more accurate and precise than those obtained by a global PLS model.

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.

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.

The thermal behavior of thiourea (Tu), N-methylthiourea (MeTu), N, N′-dimethylthiourea (Me2Tu) and N, N, N′, N′-tetramethylthiourea (Me4Tu) was investigated by means of conventional thermal analysis in O2 and N2 and IR spectroscopy of the residues on heating in air at various temperatures.

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.