6 resultados para Farms -- Recreational use -- Quality control

em AMS Tesi di Dottorato - Alm@DL - Università di Bologna


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The Gaia space mission is a major project for the European astronomical community. As challenging as it is, the processing and analysis of the huge data-flow incoming from Gaia is the subject of thorough study and preparatory work by the DPAC (Data Processing and Analysis Consortium), in charge of all aspects of the Gaia data reduction. This PhD Thesis was carried out in the framework of the DPAC, within the team based in Bologna. The task of the Bologna team is to define the calibration model and to build a grid of spectro-photometric standard stars (SPSS) suitable for the absolute flux calibration of the Gaia G-band photometry and the BP/RP spectrophotometry. Such a flux calibration can be performed by repeatedly observing each SPSS during the life-time of the Gaia mission and by comparing the observed Gaia spectra to the spectra obtained by our ground-based observations. Due to both the different observing sites involved and the huge amount of frames expected (≃100000), it is essential to maintain the maximum homogeneity in data quality, acquisition and treatment, and a particular care has to be used to test the capabilities of each telescope/instrument combination (through the “instrument familiarization plan”), to devise methods to keep under control, and eventually to correct for, the typical instrumental effects that can affect the high precision required for the Gaia SPSS grid (a few % with respect to Vega). I contributed to the ground-based survey of Gaia SPSS in many respects: with the observations, the instrument familiarization plan, the data reduction and analysis activities (both photometry and spectroscopy), and to the maintenance of the data archives. However, the field I was personally responsible for was photometry and in particular relative photometry for the production of short-term light curves. In this context I defined and tested a semi-automated pipeline which allows for the pre-reduction of imaging SPSS data and the production of aperture photometry catalogues ready to be used for further analysis. A series of semi-automated quality control criteria are included in the pipeline at various levels, from pre-reduction, to aperture photometry, to light curves production and analysis.

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Virgin olive oil(VOO) is a product characterized by high economic and nutritional values, because of its superior sensory characteristics and minor compounds (phenols and tocopherols) contents. Since the original quality of VOO may change during its storage, this study aimed to investigate the influence of different storage and shipment conditions on the quality of VOO, by studying different solutions such as filtration, dark storage and shipment inside insulated containers to protect it. Different analytical techniques were used to follow-up the quality changes during virgin olive oil storage and simulated shipments, in terms of basic quality parameters, sensory analysis and evaluation of minor components (phenolic compounds, diglycerides, volatile compounds). Four main research streams were presented in this PhD thesis: The results obtained from the first experimental section revealed that the application of filtration and/or clarification can decrease the unavoidable quality loss of the oil samples during storage, in comparison with unfiltered oil samples. The second section indicated that the virgin olive oil freshness, evaluated by diglycerides content, was mainly affected by the storage time and temperature. The third section revealed that fluctuation in temperature during storage may adversely affect the virgin olive oil quality, in terms of hydrolytic rancidity and oxidation quality. The fourth section showed that virgin olive oil shipped inside insulated containers showed lower hydrolytic and oxidation degradation than those without insulation cover. Overall, this PhD thesis highlighted that application of adequate treatment, such as filtration or clarification, in addition to a good protection against other external variables, such as temperature and light, will improve the stability of virgin olive oil during storage.

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This thesis presents SEELF (Sustainable EEL fishery) Index, a methodology for evaluation of European eel (Anguilla anguilla) for the implementation of an effective Eel Management Plan, as defined by EU Regulation No.1100/2007. SEELF uses internal and external indices, age and blood parameters, and selects suitable specimen for restocking; it is also a reliable tool for eel stock management. In fact, SEELF Index, was developed in two versions: SEELF A, to be used in field operations (catch&release, eel status monitoring) and SEELF B to be used for quality control (food production) and research (eel status monitoring). Health status was evaluated also by biomarker analysis (ChE), and data were compared with age of eel. Age determination was performed with otolith reading and fish scale reading and a calibration between the two methods was possible. The study area was the Comacchio lagoon, a brackish coastal lagoon in Italy, well known as an example of suitable environment for eel fishery, where the capability to use the local natural resources has long been a key factor for a successful fishery management. Comacchio lagoon is proposed as an area where an effective EMP can be performed, in agreement with the main features (management of basins, reduction of mortality due to predators,etc.) highlighted for designation of European Restocking Area (ERA). The ERA is a new concept, proposed as a pillar of a new strategy on eel management and conservation. Furthermore, the features of ERAs can be useful in the framework of European Scale Eel Management Plan (ESEMP), proposed as a European scale implementation of EMP, providing a more effectiveness of conservation measures for eel management.

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Nanotechnologies are rapidly expanding because of the opportunities that the new materials offer in many areas such as the manufacturing industry, food production, processing and preservation, and in the pharmaceutical and cosmetic industry. Size distribution of the nanoparticles determines their properties and is a fundamental parameter that needs to be monitored from the small-scale synthesis up to the bulk production and quality control of nanotech products on the market. A consequence of the increasing number of applications of nanomaterial is that the EU regulatory authorities are introducing the obligation for companies that make use of nanomaterials to acquire analytical platforms for the assessment of the size parameters of the nanomaterials. In this work, Asymmetrical Flow Field-Flow Fractionation (AF4) and Hollow Fiber F4 (HF5), hyphenated with Multiangle Light Scattering (MALS) are presented as tools for a deep functional characterization of nanoparticles. In particular, it is demonstrated the applicability of AF4-MALS for the characterization of liposomes in a wide series of mediums. Afterwards the technique is used to explore the functional features of a liposomal drug vector in terms of its biological and physical interaction with blood serum components: a comprehensive approach to understand the behavior of lipid vesicles in terms of drug release and fusion/interaction with other biological species is described, together with weaknesses and strength of the method. Afterwards the size characterization, size stability, and conjugation of azidothymidine drug molecules with a new generation of metastable drug vectors, the Metal Organic Frameworks, is discussed. Lastly, it is shown the applicability of HF5-ICP-MS for the rapid screening of samples of relevant nanorisk: rather than a deep and comprehensive characterization it this time shown a quick and smart methodology that within few steps provides qualitative information on the content of metallic nanoparticles in tattoo ink samples.

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This Ph.D. project aimed to the development and improvement of analytical solutions for control of quality and authenticity of virgin olive oils. According to this main objective, different research activities were carried out: concerning the quality control of olive oil, two of the official parameters defined by regulations (free acidity and fatty acid ethyl esters) were taken into account, and more sustainable and easier analytical solutions were developed and validated in-house. Regarding authenticity, two different issues were faced: verification of the geographical origin of extra virgin (EVOOs) and virgin olive oils (VOOs), and assessment of soft-deodorized oils illegally mixed with EVOOs. About fatty acid ethyl esters, a revised method based on the application of off-line HPLC-GC-FID (with PTV injector), revising both the preparative phase and the GC injector required in the official method, was developed. Next, the method was in-house validated evaluating several parameters. Concerning free acidity, a portable system suitable for in-situ measurements of VOO free acidity was developed and in-house validated. Its working principle is based on the estimation of the olive oil free acidity by measuring the conductance of an emulsion between a hydro-alcoholic solution and the sample to be tested. The procedure is very quick and easy and, therefore, suitable for people without specific training. Another study developed during the Ph.D. was about the application of flash gas chromatography for volatile compounds analysis, combined with untargeted chemometric data elaborations, for discrimination of EVOOs and VOOs of different geographical origin. A set of 210 samples coming from different EU member states and extra-EU countries were collected and analyzed. Data were elaborated applying two different classification techniques, one linear (PLS-DA) and one non-linear (ANN). Finally, a preliminary study about the application of GC-IMS (Gas Chromatograph - Ion Mobility Spectrometer) for assessment of soft-deodorized olive oils was carried out.

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In pursuit of aligning with the European Union's ambitious target of achieving a carbon-neutral economy by 2050, researchers, vehicle manufacturers, and original equipment manufacturers have been at the forefront of exploring cutting-edge technologies for internal combustion engines. The introduction of these technologies has significantly increased the effort required to calibrate the models implemented in the engine control units. Consequently the development of tools that reduce costs and the time required during the experimental phases, has become imperative. Additionally, to comply with ever-stricter limits on 〖"CO" 〗_"2" emissions, it is crucial to develop advanced control systems that enhance traditional engine management systems in order to reduce fuel consumption. Furthermore, the introduction of new homologation cycles, such as the real driving emissions cycle, compels manufacturers to bridge the gap between engine operation in laboratory tests and real-world conditions. Within this context, this thesis showcases the performance and cost benefits achievable through the implementation of an auto-adaptive closed-loop control system, leveraging in-cylinder pressure sensors in a heavy-duty diesel engine designed for mining applications. Additionally, the thesis explores the promising prospect of real-time self-adaptive machine learning models, particularly neural networks, to develop an automatic system, using in-cylinder pressure sensors for the precise calibration of the target combustion phase and optimal spark advance in a spark-ignition engines. To facilitate the application of these combustion process feedback-based algorithms in production applications, the thesis discusses the results obtained from the development of a cost-effective sensor for indirect cylinder pressure measurement. Finally, to ensure the quality control of the proposed affordable sensor, the thesis provides a comprehensive account of the design and validation process for a piezoelectric washer test system.