Improved identification of pollution source attribution by using PAH ratios combined with multivariate statistics.

Polycyclic aromatic hydrocarbons (PAHs) are contaminants introduced by different pathways in the marine ecosystem, affecting both aquatic and sediment bodies. Identification of their sources is of vital importance for protecting the marine ecosystem. The attribution of the pollution sources is usually made by using diagnostic molecular ratios of PAHs isomers. The reliability of this approach diminishes when PAHs contents are measured far from their original source, for example in water bodies or in bottom sediments. Conventionally the source attribution is based on time consuming univariate methods. In the present work coupling of molecular ratios with advanced supervised statistical techniques was used to increase the accuracy of the PAH source attribution in bottom sediments. Data on PAHs distribution within 5 port areas, with known pattern port activity, were collected. Evaluation of multiple PAHs ratios at once by means of supervised OPLS-DA technique was performed. A robust descriptive and predictive model was set up and successfully validated. The proposed methodology helps identify PAH transport pathways, highlighting interactions between pollution patterns, port activities and coastal land-use supporting decision makers in defining monitoring and mitigation procedures.

A non-targeted metabolomics study on Xylella fastidiosa infected olive plants grown under controlled conditions.

In the last decade, the bacterial pathogen Xylella fastidiosa has devastated olive trees throughout Apulia region (Southern Italy) in the form of the disease called "Olive Quick Decline Syndrome" (OQDS). This study describes changes in the metabolic profile due to the infection by X. fastidiosa subsp. pauca ST53 in artificially inoculated young olive plants of the susceptible variety Cellina di Nardò. The test plants, grown in a thermo-conditioned greenhouse, were also co-inoculated with some xylem-inhabiting fungi known to largely occur in OQDS-affected trees, in order to partially reproduce field conditions in terms of biotic stress. The investigations were performed by combining NMR spectroscopy and MS spectrometry with a non-targeted approach for the analysis of leaf extracts. Statistical analysis revealed that Xylella-infected plants were characterized by higher amounts of malic acid, formic acid, mannitol, and sucrose than in Xylella-non-infected ones, whereas it revealed slightly lower amounts of oleuropein. Attention was paid to mannitol which may play a central role in sustaining the survival of the olive tree against bacterial infection. This study contributes to describe a set of metabolites playing a possible role as markers in the infections by X. fastidiosa in olive.

Development of a food class-discrimination system by non-targeted NMR analyses using different magnetic field strengths.

Non-targeted NMR-based approach has received great attention as a rapid method for food product authenticity assessment. The availability of a database containing many comparable NMR spectra produced by different spectrometers is crucial to develop functional classifiers able to discriminate rapidly the commodity class of a given food product. Nevertheless, variability in spectrometer features may hamper the production of comparable spectra due to inherent variations in signal resolution. In this paper, we report on the development of a class-discrimination model for grape juice authentication by application of non-targeted NMR spectroscopy. Different approaches for the pre-treatment of data will be described along with details about the model validation. The developed model performed excellently (95.4-100% correct predictions) even when it was tested against 650 spectra produced by 65 spectrometers with different configurations (magnetic field strength, manufacturer, age). This study may boost the use of non-targeted NMR methods for food control.

A community-built calibration system: The case study of quantification of metabolites in grape juice by qNMR spectroscopy.

Nuclear Magnetic Resonance (NMR) is an analytical technique extensively used in almost every chemical laboratory for structural identification. This technique provides statistically equivalent signals in spite of using spectrometer with different hardware features and is successfully used for the traceability and quantification of analytes in food samples. Nevertheless, to date only a few internationally agreed guidelines have been reported on the use of NMR for quantitative analysis. The main goal of the present study is to provide a methodological pipeline to assess the reproducibility of NMR data produced for a given matrix by spectrometers from different manufacturers, with different magnetic field strengths, age and hardware configurations. The results have been analyzed through a sequence of chemometric tests to generate a community-built calibration system which was used to verify the performance of the spectrometers and the reproducibility of the predicted sample concentrations.

Cultivar classification of Apulian olive oils: Use of artificial neural networks for comparing NMR, NIR and merceological data.

The development of an efficient and accurate method for extra-virgin olive oils cultivar and origin authentication is complicated by the broad range of variables (e.g., multiplicity of varieties, pedo-climatic aspects, production and storage conditions) influencing their properties. In this study, artificial neural networks (ANNs) were applied on several analytical datasets, namely standard merceological parameters, near-infra red data and 1H nuclear magnetic resonance (NMR) fingerprints, obtained on mono-cultivar olive oils of four representative Apulian varieties (Coratina, Ogliarola, Cima di Mola, Peranzana). We analyzed 888 samples produced at a laboratory-scale during two crop years from 444 plants, whose variety was genetically ascertained, and on 17 industrially produced samples. ANN models based on NMR data showed the highest capability to classify cultivars (in some cases, accuracy>99%), independently on the olive oil production process and year; hence, the NMR data resulted to be the most informative variables about the cultivars.

Robustness of NMR-based metabolomics to generate comparable data sets for olive oil cultivar classification. An inter-laboratory study on Apulian olive oils.

Nuclear Magnetic Resonance (NMR) spectroscopy is emerging as a powerful technique in olive oil fingerprinting, but its analytical robustness has to be proved. Here, we report a comparative study between two laboratories on olive oil (1)H NMR fingerprinting, aiming to demonstrate the robustness of NMR-based metabolomics in generating comparable data sets for cultivar classification. Sample preparation and data acquisition were performed independently in two laboratories, equipped with different resolution spectrometers (400 and 500 MHz), using two identical sets of mono-varietal olive oils. Partial Least Squares (PLS)-based techniques were applied to compare the data sets produced by the two laboratories. Despite differences in spectrum baseline, and in intensity and shape of peaks, the amount of shared information was significant (almost 70%) and related to cultivar (same metabolites discriminated between cultivars). In conclusion, regardless of the variability due to operator and machine, the data sets from the two participating units were comparable for the purpose of classification.

Renal Cell Carcinoma: A Study through NMR-Based Metabolomics Combined with Transcriptomics.

Renal cell carcinoma (RCC) is a heterogeneous cancer often showing late symptoms. Until now, some candidate protein markers have been proposed for its diagnosis. Metabolomics approaches have been applied, predominantly using Mass Spectrometry (MS), while Nuclear Magnetic Resonance (NMR)-based studies remain limited. There is no study about RCC integrating NMR-based metabolomics with transcriptomics. In this work, ¹H-NMR spectroscopy combined with multivariate statistics was applied on urine samples, collected from 40 patients with clear cell RCC (ccRCC) before nephrectomy and 29 healthy controls; nine out of 40 patients also provided samples one-month after nephrectomy. We observed increases of creatine, alanine, lactate and pyruvate, and decreases of hippurate, citrate, and betaine in all ccRCC patients. A network analysis connected most of these metabolites with glomerular injury, renal inflammation and renal necrosis/cell death. Interestingly, intersecting metabolites with transcriptomic data from CD133+/CD24+ tumoral renal stem cells isolated from ccRCC patients, we found that both genes and metabolites differentially regulated in ccRCC patients belonged to HIF-α signaling, methionine and choline degradation, and acetyl-CoA biosynthesis. Moreover, when comparing urinary metabolome of ccRCC patients after nephrectomy, some processes, such as the glomerular injury, renal hypertrophy, renal necrosis/cell death and renal proliferation, were no more represented.