Francesco Longobardi

Characterization and classification of Western Greek olive oils according to cultivar and geographical origin based on volatile compounds.

The aim of the present study was to characterize and classify olive oils from Western Greece according to cultivar and geographical origin, based on volatile compound composition, by means of Linear Discriminant Analysis. A total of 51 olive oil samples were collected during the harvesting period 2007-2008 from six regions of Western Greece and from six local cultivars. Forty-five of the samples were characterized as extra virgin olive oils. The analysis of volatile compounds was performed by Headspace Solid Phase Microextraction-Gas Chromatography/Mass Spectrometry (HS-SPME-GC/MS). Fifty-three (53) different volatile compounds were tentatively identified and semi-quantified. Using selected volatile compound composition data (selection was based on the application of ANOVA to total volatiles to determine those variables showing substantial differences among samples of different geographical origin/cultivar), the olive oil samples were satisfactorily classified according to geographical origin (87.2%) and cultivar (74%).

Chemical characterization of exhaled breath to differentiate between patients with malignant plueral mesothelioma from subjects with similar professional asbestos exposure

Malignant pleural mesothelioma (MPM) is an aggressive tumour whose main aetiology is the long-term exposure to asbestos fibres. The diagnostic procedure of MPM is difficult and often requires invasive approaches; therefore, it is clinically important to find accurate markers for MPM by new noninvasive methods that may facilitate the diagnostic process and identify patients at an earlier stage. In the present study, the exhaled breath of 13 patients with histology-established diagnosis of MPM, 13 subjects with long-term certified professional exposure to asbestos (EXP) and 13 healthy subjects without exposure to asbestos (healthy controls, HC) were analysed. An analytical procedure to determine volatile organic compounds by sampling of air on a bed of solid sorbent and thermal desorption GC-MS analysis was developed in order to identify the compounds capable of discriminating among the three groups. The application of univariate (ANOVA) and multivariate statistical treatments (PCA, DFA and CP-ANN) showed that cyclopentane and cyclohexane were the dominant variables able to discriminate among the three groups. In particular, it was found that cyclohexane is the only compound able to differentiate the MPM group from the other two; therefore, it can be a possible marker of MPM. Cyclopentane is the dominant compound in the discrimination between EXP and the other groups (MPM and HC); then, it can be considered a good indicator for long-term asbestos exposure. This result suggests the need to perform frequent and thorough investigations on people exposed to asbestos in order to constantly monitor their state of health or possibly to study the evolution of disease over time.

Instrumental and multivariate statistical analyses for the characterisation of the geographical origin of Apulian virgin olive oils.

In this paper, virgin olive oils (VOOs) coming from three different geographic origins of Apulia, were analysed for free acidity, peroxide value, spectrophotometric indexes, chlorophyll content, sterol, fatty acid, and triacylglycerol compositions. In order to predict the geographical origin of VOOs, different multivariate approaches were applied. By performing principal component analysis (PCA) a modest natural grouping of the VOOs was observed on the basis of their origin, and consequently three supervised techniques, i.e., general discriminant analysis (GDA), partial least squares-discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA) were used and the results were compared. In particular, the best prediction ability was produced by applying GDA (average prediction ability of 82.5%), even if interesting results were obtained also by applying the other two classification techniques, i.e., 77.2% and 75.5% for PLS-DA and SIMCA, respectively.

Non-targeted 1H NMR fingerprinting and multivariate statistical analyses for the characterisation of the geographical origin of Italian sweet cherries.

In this study, non-targeted (1)H NMR fingerprinting was used in combination with multivariate statistical techniques for the classification of Italian sweet cherries based on their different geographical origins (Emilia Romagna and Puglia). As classification techniques, Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Linear Discriminant Analysis (LDA) were carried out and the results were compared. For LDA, before performing a refined selection of the number/combination of variables, two different strategies for a preliminary reduction of the variable number were tested. The best average recognition and CV prediction abilities (both 100.0%) were obtained for all the LDA models, although PLS-DA also showed remarkable performances (94.6%). All the statistical models were validated by observing the prediction abilities with respect to an external set of cherry samples. The best result (94.9%) was obtained with LDA by performing a best subset selection procedure on a set of 30 principal components previously selected by a stepwise decorrelation. The metabolites that mostly contributed to the classification performances of such LDA model, were found to be malate, glucose, fructose, glutamine and succinate.

Electronic nose and isotope ratio mass spectrometry in combination with chemometrics for the characterization of the geographical origin of Italian sweet cherries.

Sweet cherries from two Italian regions, Apulia and Emilia Romagna, were analysed using electronic nose (EN) and isotope ratio mass spectrometry (IRMS), with the aim of distinguishing them according to their geographic origin. The data were elaborated by statistical techniques, examining the EN and IRMS datasets both separately and in combination. Preliminary exploratory overviews were performed and then linear discriminant analyses (LDA) were used for classification. Regarding EN, different approaches for variable selection were tested, and the most suitable strategies were highlighted. The LDA classification results were expressed in terms of recognition and prediction abilities and it was found that both EN and IRMS performed well, with IRMS showing better cross-validated prediction ability (91.0%); the EN-IRMS combination gave slightly better results (92.3%). In order to validate the final results, the models were tested using an external set of samples with excellent results.

Performance Assessment in Fingerprinting and Multi Component Quantitative NMR Analyses.

An interlaboratory comparison (ILC) was organized with the aim to set up quality control indicators suitable for multicomponent quantitative analysis by nuclear magnetic resonance (NMR) spectroscopy. A total of 36 NMR data sets (corresponding to 1260 NMR spectra) were produced by 30 participants using 34 NMR spectrometers. The calibration line method was chosen for the quantification of a five-component model mixture. Results show that quantitative NMR is a robust quantification tool and that 26 out of 36 data sets resulted in statistically equivalent calibration lines for all considered NMR signals. The performance of each laboratory was assessed by means of a new performance index (named Qp-score) which is related to the difference between the experimental and the consensus values of the slope of the calibration lines. Laboratories endowed with a Qp-score falling within the suitable acceptability range are qualified to produce NMR spectra that can be considered statistically equivalent in terms of relative intensities of the signals. In addition, the specific response of nuclei to the experimental excitation/relaxation conditions was addressed by means of the parameter named NR. NR is related to the difference between the theoretical and the consensus slopes of the calibration lines and is specific for each signal produced by a well-defined set of acquisition parameters.

Effects of different vinification technologies on physical and chemical characteristics of Sauvignon blanc wines.

In order to investigate the effects of cryomaceration and reductive vinification on chemical and physical indices and on antioxidant compounds of Sauvignon blanc wines, four wine-making procedures were applied: traditional white vinification, skin cryomaceration, vinification in a reductive environment, and a combination of the last two procedures. Significant differences were highlighted by both conventional analyses and NMR spectroscopy. The strongest changes were for organic acid concentrations (tartaric, in particular) and phenolic content. Cryomaceration caused a strong precipitation of tartaric acid, which may be desired if grapes have high acidity values. Cryomaceration protected those flavans reactive with vanillin from the action of oxidative enzymes. Vinification in a reductive environment, alone or combined with a cryomaceration step, gave wines with the highest solids content and caused a greater extraction of phenolic compounds from skins compared to traditional winemaking or cryomaceration alone, due to SO(2) solubilisation. Grape oenological expression can be strongly affected by the application of the investigated wine-making procedures.

Food coloring agents and plant food supplements derived from Vitis vinifera: a new source of human exposure to ochratoxin A.

Grape pomaces are increasingly being used as starting material in the industrial production of plant food supplements (PFS), food coloring, and tartrates, but they are at risk of ochratoxin A (OTA) contamination, a mycotoxin with nephrotoxic and carcinogenic effects. We analyzed 24 commercial PFS and 13 food coloring samples derived from Vitis vinifera, mainly pomaces, using a HPLC-FLD method for OTA determination. OTA was found in 75% of PFS samples and 69% of food coloring samples at levels of <1.16-20.23 μg/kg and <1.16-32.00 μg/kg, respectively. The four commercial leavening agents containing tartrates were found to be negative for OTA. All eight samples collected in two distilleries that use grape pomaces and wine lees to produce tartrates and other byproducts contained OTA at levels of <1.16-240.93 μg/kg. The high incidence of OTA contamination in PFS and food coloring agents derived from V. vinifera suggests that maximum permitted level(s) should be established for this mycotoxin in these products.

Determination of ochratoxin A in wine by means of immunoaffinity and aminopropyl solid-phase column cleanup and fluorometric detection.

A new analytical method for the determination of ochratoxin A (OTA) in red wine has been developed by using a double-extract cleanup and a fluorometric measurement after spectral deconvolution. Wine samples were diluted with a solution containing 1% polyethylene glycol and 5% sodium hydrogencarbonate, filtered, and purified by immunoaffinity and aminopropyl solid-phase column. OTA contents in the purified extract were determined by a spectrofluorometer (excitation wavelength, 330 nm; emission wavelength, 470 nm) after deconvolution of fluorescence spectra. Average recoveries from wine samples spiked with OTA at levels ranging from 0.5 to 3.0 ng/mL were 94.5-105.4% with relative standard deviations (RSD) of <15% (n = 4). The limit of detection (LOD) was 0.2 ng/mL, and the total time of analysis was 30 min. The developed method was tested on 18 red wine samples (naturally contaminated and spiked with OTA at levels ranging from 0.4 to 3.0 ng/mL) and compared with AOAC Official Method 2001.01, based on immunoaffinity column cleanup and HPLC with fluorescence detector. A good correlation (r(2) = 0.9765) was observed between OTA levels obtained with the two methods, highlighting the reliability of the proposed method, the main advantage of which is the simple OTA determination by a benchtop fluorometer with evident reductions of cost and time of analysis.

An electronic nose in the discrimination of obese patients with and without obstructive sleep apnoea.

Exhaled breath contains thousands of volatile organic compounds (VOCs) in gaseous form, which may be used as markers of airway inflammation and lung disease. Electronic noses enable quick and real-time pattern analysis of VOC spectra. It has been shown that the exhaled breath of patients with obstructive sleep apnoea (OSA) differs from that of non-obese controls. We aimed to assess the influence of obesity in the composition of exhaled VOCs by comparing obese subjects with and without OSA. Moreover, we aimed to identify the discriminant VOCs in the two groups.19 obese patients with established OSA (OO; age 51.2 ± 6.8; body mass index (BMI) 34.3 ± 3.5), 14 obese controls without OSA (ONO; age 46.5 ± 7.6; BMI 33.5 ± 4.1) and 20 non-obese healthy controls (HC; age 41.1 ± 12.6; BMI 24.9 ± 3.8) participated in a cross-sectional study. Exhaled breath was collected by a previously described method and sampled by using an electronic nose (Cyranose 320) and by gas chromatography-mass spectrometry (GC-MS) analysis. Breathprints were analyzed by canonical discriminant analysis on principal component reduction. Cross-validation accuracy (CVA) was calculated. Breathprints from the HC group were separated from those of OO (CVA = 97.4%) and ONO (CVA = 94.1%). Breathprints from OO were moderately separated from those of ONO (CVA = 67.6%).The presence of OSA alters the exhaled VOC pattern in obese subjects. The incomplete separation of breathprints between OO and ONO may be due to the same underlying inflammation caused by obesity.