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Browsing by study line "Advanced Spectroscopy in Chemistry"

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  • Komarczuk, Elise (2022)
    The most common route to administer drugs is oral drug delivery. However, the effectiveness of a drug or bioavailability depends mainly on the drug solubility and many drugs or drug candidates are poorly water-soluble. This is the case of indomethacin, a nonsteroidal anti-inflammatory drug (NSAID), widely used against arthritis. The drug solubility and hence the bioavailability can be improved by formulation. The formulation can be prepared with an amphiphilic compound, for instance amphiphilic block copolymers like Poly(2-oxazoline)s that have proven to be suitable candidates because they are biocompatible and their solubility and solubilization capacity can be widely modulated. Since after oral administration, the drug will be absorbed in the intestine, the intestinal fluid plays a crucial role in the solubilization, but this is currently poorly understood. Therefore, drug interactions studies are made in solution mimicking fed state intestinal fluid (FeSSIF-V2) composed of lipids (fatty acids FA and lecithin LC) and bile salts (taurocholic acid, TC). Subject of this study was the investigation of the interaction between indomethacin with poly(2-oxazoline) ABA triblock copolymers, (P2), comprising poly(2-methyl-2-oxazoline) as hydrophilic blocks and poly(2-butyl-2-oxazoline) as hydrophobic blocks, and FeSSIF-V2 were carried out using the different NMR techniques such as Diffusion-Ordered NMR spectroscopy (DOSY), nuclear Overhauser effect spectroscopy (NOESY) and 1H-NMR regarding the changes in chemical shift, the changes of the intensities and the integrals Indomethacin alone, polymer P2 alone and P2-Indomethacin formulations were dissolved in FeSSIF-V2. The changes in chemical shift proved that interactions exist between the drug, the formulation and the FeSSIF-V2. It was found (with the changes in chemical shifts, confirmed by DOSY) that the indomethacin interacts with the bile salts (TC). Also the DOSY experiment showed that the polymer P2 interacts with the bile salts (TC) at low concentration and with the lipids at a polymer concentration greater than 0.3 wt%. The same experiment was done using the P2-Indomethacin formulations and at the concentration of 0.3 wt% again the polymer aggregates were going from interacting with the bile salts (TC) to merging with the lipid aggregates, presenting a significant increase of hydrodynamic diameter (from 3.5 nm to 6.2 nm).
  • Bortolussi, Federica (2022)
    The exploration of mineral resources is a major challenge in a world that seeks sustainable energy, renewable energy, advanced engineering, and new commercial technological devices. The rapid decrease in mineral reserves shifted the focus to under-explored and low accessibility areas that led to the use of on-site portable techniques for mineral mapping purposes, such as near infrared hyperspectral image sensors. The large datasets acquired with these instruments needs data pre-processing, a series of mathematical manipulations that can be achieved using machine learning. The aim of this thesis is to improve an existing method for mineralogy mapping, by focusing on the mineral classification phase. More specifically, a spectral similarity index was utilized to support machine learning classifiers. This was introduced because of the inability of the employed classification models to recognize samples that are not part of a given database; the models always classified samples based on one of the known labels of the database. This could be a problem in hyperspectral images as the pure component found in a sample could correspond to a mineral but also to noise or artefacts due to a variety of reasons, such as baseline correction. The spectral similarity index calculates the similarity between a sample spectrum and its assigned database class spectrum; this happens through the use of a threshold that defines whether the sample belongs to a class or not. The metrics utilized in the spectral similarity index were the spectral angler mapper, the correlation coefficient and five different distances. The machine learning classifiers used to evaluate the spectral similarity index were the decision tree, k-nearest neighbor, and support vector machine. Simulated distortions were also introduced in the dataset to test the robustness of the indexes and to choose the best classifier. The spectral similarity index was assessed with a dataset of nine minerals acquired from the Geological Survey of Finland retrieved from a Specim SWIR camera. The validation of the indexes was assessed with two mine samples obtained with a VTT active hyperspectral sensor prototype. The support vector machine was chosen after the comparison between the three classifiers as it showed higher tolerance to distorted data. With the evaluation of the spectral similarity indexes, was found out that the best performances were achieved with SAM and Chebyshev distance, which maintained high stability with smaller and bigger threshold changes. The best threshold value found is the one that, in the dataset analysed, corresponded to the number of spectra available for each class. As for the validation procedure no reference was available; because of this reason, the results of the mine samples obtained with the spectral similarity index were compared with results that can be obtained through visual interpretation, which were in agreement. The method proposed can be useful to future mineral exploration as it is of great importance to correctly classify minerals found during explorations, regardless the database utilized.
  • Mandoda, Purvi (2022)
    Legumes and grains are grown worldwide, with the rise of consumption the importance of identification of metabolites like phenolic compounds within them are just as essential. Phenolic compounds are secondary metabolites with multiple beneficial properties such as antimicrobial, antioxidant and anti-inflammatory. Using Py-GC/MS (pyrolysis-gas chromatography/mass spectrometry) as a faster method of identification of phenolic compounds are the basis of this investigation. A total phenolic analysis using Folin-Ciocalteu analysis has taken place to determine the presence of phenolic compounds with the eight samples – wheat, barley, oats, pigeon pea, chickpea, fava beans, green peas, and potato peels. UPLC coupled with a PDA and FLR detector will be another instrument used to determine the types of phenolic compounds present in the eight samples. Py-GC/MS was able to identify compounds with the phenol moiety but not phenolic compounds of interest. The total phenolic content analysis was able to establish that phenolic compounds were present in all eight samples. Ferulic acid, gallic acid, vanillic acid and 3,4- dihydroxyphenylacetic acid were some of the phenolic compounds identified within the eight samples, using the UPLC chromatograms and measured standards.