Antioxidant activity shown by olive pomace extracts

In this study, the effects of experimental variables such as type of solvent, sample/solvent ratio, and time of extraction have been evaluated to individuate the best results in phenolic recovery by Olive Pomaces (OP) belonging to Carolea and Ottobratica cultivars. Folin–Ciocaulteu procedure and DPPH and ABTS assays were used, respectively, for total phenol quantification and total antioxidant activity of pomace extracts. The ethanol/water mixture was resulted the most efficient extraction solvent for both olive cultivars. The highest amount of phenolic compounds (171 ± 4 mg of gallic acid 100 g^?1 of dry pomace) was obtained after extraction at 120 min with 2:1 solvent/OP (v/w) of Ottobratica Olive Pomace. The recovery of phenol compounds from olive wastes increases the sustainability of sector, allowing obtaining an extract that could be a suitable alternative in the food industry to the use of synthetic antioxidants in order to improve the quality of foods.     

Some Interpolation Estimators in Environmental Risk Assessment for Spatially Misaligned Health Data

Ecological regression studies are widely used in geographical epidemiology to assess the relationships between health hazard and putative risk factors. Very often, health data are measured at an aggregate level because of confidentiality restrictions, while putative risk factors are measured on a different grid, i.e., independent (exposure) variable and response (counts) variable are spatially misaligned. To perform a regression of risk on exposure, one needs to realign the spatial support of the variables. Bayesian hierarchical models constitute a natural approach to the problem because of their ability to model the exposure field and the relationship between exposure and relative risk at different levels of the hierarchy, taking proper account of the variability induced by the covariate estimation. In the current paper, we propose two fully Bayesian solutions to the problem. The first one is based on the kernel-smoothing technique, while the second one is built on the tessellation of the study region. We illustrate our methods by assessing the relationship between exposure to uranium in drinkable waters and cancer incidence, in South Carolina (USA).