Quali-quantitative study of the phenolic and polyphenolic compounds and their antioxidant capacity in vegetal matrix processed with different technologies

Food technologies today mean reducing agricultural food waste, improvement of food security, enhancement of food sensory properties, enlargement of food market and food economies. Food technologists must be high-skilled technicians with good scientific knowledge of food hygiene, food chemistry, industrial technologies and food engineering, sensory evaluation experience and analytical chemistry. Their role is to apply the modern vision of science in the field of human nutrition, rising up knowledge in food science. The present PhD project starts with the aim of studying and improving frozen fruits quality. Freezing process in very powerful in preserve initial raw material characteristics, but pre-treatment before the freezing process are necessary to improve quality, in particular to improve texture and enzymatic activity of frozen foods. Osmotic Dehydration (OD) and Vacuum Impregnation (VI), are useful techniques to modify fruits and vegetables composition and prepare them to freezing process. These techniques permit to introduce cryo-protective agent into the food matrices, without significant changes of the original structure, but cause a slight leaching of important intrinsic compounds. Phenolic and polyphenolic compounds for example in apples and nectarines treated with hypertonic solutions are slightly decreased, but the effect of concentration due to water removal driven out from the osmotic gradient, cause a final content of phenolic compounds similar to that of the raw material. In many experiment, a very important change in fruit composition regard the aroma profile. This occur in strawberries osmo-dehydrated under vacuum condition or under atmospheric pressure condition. The increment of some volatiles, probably due to fermentative metabolism induced by the osmotic stress of hypertonic treatment, induce a sensory profile modification of frozen fruits, that in some way result in a better acceptability of consumer, that prefer treated frozen fruits to untreated frozen fruits. Among different processes used, a very interesting result was obtained with the application of a osmotic pre-treatment driven out at refrigerated temperature for long time. The final quality of frozen strawberries was very high and a peculiar increment of phenolic profile was detected. This interesting phenomenon was probably due to induction of phenolic biological synthesis (for example as reaction to osmotic stress), or to hydrolysis of polymeric phenolic compounds. Aside this investigation in the cryo-stabilization and dehydrofreezing of fruits, deeper investigation in VI techniques were carried out, as studies of changes in vacuum impregnated prickly pear texture, and in use of VI and ultrasound (US) in aroma enrichment of fruit pieces. Moreover, to develop sensory evaluation tools and analytical chemistry determination (of volatiles and phenolic compounds), some researches were bring off and published in these fields. Specifically dealing with off-flavour development during storage of boiled potato, and capillary zonal electrophoresis (CZE) and high performance liquid chromatography (HPLC) determination of phenolic compounds.

Data-driven and data-oriented methods for materials science and technologies

The discovery of new materials and their functions has always been a fundamental component of technological progress. Nowadays, the quest for new materials is stronger than ever: sustainability, medicine, robotics and electronics are all key assets which depend on the ability to create specifically tailored materials. However, designing materials with desired properties is a difficult task, and the complexity of the discipline makes it difficult to identify general criteria. While scientists developed a set of best practices (often based on experience and expertise), this is still a trial-and-error process. This becomes even more complex when dealing with advanced functional materials. Their properties depend on structural and morphological features, which in turn depend on fabrication procedures and environment, and subtle alterations leads to dramatically different results. Because of this, materials modeling and design is one of the most prolific research fields. Many techniques and instruments are continuously developed to enable new possibilities, both in the experimental and computational realms. Scientists strive to enforce cutting-edge technologies in order to make progress. However, the field is strongly affected by unorganized file management, proliferation of custom data formats and storage procedures, both in experimental and computational research. Results are difficult to find, interpret and re-use, and a huge amount of time is spent interpreting and re-organizing data. This also strongly limit the application of data-driven and machine learning techniques. This work introduces possible solutions to the problems described above. Specifically, it talks about developing features for specific classes of advanced materials and use them to train machine learning models and accelerate computational predictions for molecular compounds; developing method for organizing non homogeneous materials data; automate the process of using devices simulations to train machine learning models; dealing with scattered experimental data and use them to discover new patterns.