Clustering of variables around latent components: an application in consumer science

The present work proposes a method based on CLV (Clustering around Latent Variables) for identifying groups of consumers in L-shape data. This kind of datastructure is very common in consumer studies where a panel of consumers is asked to assess the global liking of a certain number of products and then, preference scores are arranged in a two-way table Y. External information on both products (physicalchemical description or sensory attributes) and consumers (socio-demographic background, purchase behaviours or consumption habits) may be available in a row descriptor matrix X and in a column descriptor matrix Z respectively. The aim of this method is to automatically provide a consumer segmentation where all the three matrices play an active role in the classification, getting homogeneous groups from all points of view: preference, products and consumer characteristics. The proposed clustering method is illustrated on data from preference studies on food products: juices based on berry fruits and traditional cheeses from Trentino. The hedonic ratings given by the consumer panel on the products under study were explained with respect to the product chemical compounds, sensory evaluation and consumer socio-demographic information, purchase behaviour and consumption habits.

Theoretical and Experimental Study of the Magnetic Separation of Pollutants from Wastewater

This Thesys reports the study of a HGMS (High GradientMagnetic Separation) process for the treatment of industrialwastewaters that considers an assisted chemical-physical pre-treatment for the removal of heavy metals through the bound by adsorption with added iron-oxide particulate matter (hematite). The considered filter, constituted by ferromagnetic stainless steel wool and permanent magnets, is studied with a new approach based on a statistical analysis that requires the study of the trajectories of the particles. Experimental activity on a laboratory device has been carried out in order to test the model.

Comparison of affinity chromatography supports for separation of protein

Chromatography is the most widely used technique for high-resolution separation and analysis of proteins. This technique is very useful for the purification of delicate compounds, e.g. pharmaceuticals, because it is usually performed at milder conditions than separation processes typically used by chemical industry. This thesis focuses on affinity chromatography. Chromatographic processes are traditionally performed using columns packed with porous resin. However, these supports have several limitations, including the dependence on intra-particle diffusion, a slow mass transfer mechanism, for the transport of solute molecules to the binding sites within the pores and high pressure drop through the packed bed. These limitations can be overcome by using chromatographic supports like membranes or monoliths. Dye-ligands are considered important alternatives to natural ligands. Several reactive dyes, particularly Cibacron Blue F3GA, are used as affinity ligand for protein purification. Cibacron Blue F3GA is a triazine dye that interacts specifically and reversibly with albumin. The aim of this study is to prepare dye-affinity membranes and monoliths for efficient removal of albumin and to compare the three different affinity supports: membranes and monoliths and a commercial column HiTrapTM Blue HP, produced by GE Healthcare. A comparison among the three supports was performed in terms of binding capacity at saturation (DBC100%) and dynamic binding capacity at 10% breakthrough (DBC10%) using solutions of pure BSA. The results obtained show that the CB-RC membranes and CB-Epoxy monoliths can be compared to commercial support, column HiTrapTM Blue HP, for the separation of albumin. These results encourage a further characterization of the new supports examined.

Analytical challenges in the fields of Nanobioscience and Nanobiotecnology: integrated methods for separation and characterization

Nano(bio)science and nano(bio)technology play a growing and tremendous interest both on academic and industrial aspects. They are undergoing rapid developments on many fronts such as genomics, proteomics, system biology, and medical applications. However, the lack of characterization tools for nano(bio)systems is currently considered as a major limiting factor to the final establishment of nano(bio)technologies. Flow Field-Flow Fractionation (FlFFF) is a separation technique that is definitely emerging in the bioanalytical field, and the number of applications on nano(bio)analytes such as high molar-mass proteins and protein complexes, sub-cellular units, viruses, and functionalized nanoparticles is constantly increasing. This can be ascribed to the intrinsic advantages of FlFFF for the separation of nano(bio)analytes. FlFFF is ideally suited to separate particles over a broad size range (1 nm-1 μm) according to their hydrodynamic radius (rh). The fractionation is carried out in an empty channel by a flow stream of a mobile phase of any composition. For these reasons, fractionation is developed without surface interaction of the analyte with packing or gel media, and there is no stationary phase able to induce mechanical or shear stress on nanosized analytes, which are for these reasons kept in their native state. Characterization of nano(bio)analytes is made possible after fractionation by interfacing the FlFFF system with detection techniques for morphological, optical or mass characterization. For instance, FlFFF coupling with multi-angle light scattering (MALS) detection allows for absolute molecular weight and size determination, and mass spectrometry has made FlFFF enter the field of proteomics. Potentialities of FlFFF couplings with multi-detection systems are discussed in the first section of this dissertation. The second and the third sections are dedicated to new methods that have been developed for the analysis and characterization of different samples of interest in the fields of diagnostics, pharmaceutics, and nanomedicine. The second section focuses on biological samples such as protein complexes and protein aggregates. In particular it focuses on FlFFF methods developed to give new insights into: a) chemical composition and morphological features of blood serum lipoprotein classes, b) time-dependent aggregation pattern of the amyloid protein Aβ1-42, and c) aggregation state of antibody therapeutics in their formulation buffers. The third section is dedicated to the analysis and characterization of structured nanoparticles designed for nanomedicine applications. The discussed results indicate that FlFFF with on-line MALS and fluorescence detection (FD) may become the unparallel methodology for the analysis and characterization of new, structured, fluorescent nanomaterials.