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  • Improving the extraction of Ara h 6 (a peanut allergen) from a chocolate-based matrix for immunosensing detection: Influence of time, temperature and additives
    Publication . Alves, Rita C.; Pimentel, Filipa B; Nouws, Henri P.A.; Silva, Túlio H B; Oliveira, M Beatriz P P; Delerue-Matos, Cristina
    The extraction of Ara h 6 (a peanut allergen) from a complex chocolate-based food matrix was optimized by testing different temperatures, extraction times, and the influence of additives (NaCl and skimmed milk powder) in a total of 36 different conditions. Analyses were carried out using an electrochemical immunosensor. Three conditions were selected since they allowed the extraction of the highest levels of Ara h 6. These extractions were performed using 2g of sample and 20ml of Tris-HNO3 (pH=8) containing: a) 0.1M NaCl and 2g of skimmed milk powder at 21°C for 60min; b) 1M NaCl and 1g of skimmed milk powder at 21°C for 60min; and c) 2g of skimmed milk powder at 60°C for 60min. Recoveries were similar or higher than 94.7%. This work highlights the importance to adjust extraction procedures regarding the target analyte and food matrix components.
    2017-03-01Journal article Open access Show more
  • Dynamic electricity pricing for electric vehicles using stochastic programming
    Publication . Soares, João; Ghazvini, Mohammad Ali Fotouhi; Borges, Nuno; Vale, Zita
    Electric Vehicles (EVs) are an important source of uncertainty, due to their variable demand, departure time and location. In smart grids, the electricity demand can be controlled via Demand Response (DR) programs. Smart charging and vehicle-to-grid seem highly promising methods for EVs control. However, high capital costs remain a barrier to implementation. Meanwhile, incentive and price-based schemes that do not require high level of control can be implemented to influence the EVs' demand. Having effective tools to deal with the increasing level of uncertainty is increasingly important for players, such as energy aggregators. This paper formulates a stochastic model for day-ahead energy resource scheduling, integrated with the dynamic electricity pricing for EVs, to address the challenges brought by the demand and renewable sources uncertainty. The two-stage stochastic programming approach is used to obtain the optimal electricity pricing for EVs. A realistic case study projected for 2030 is presented based on Zaragoza network. The results demonstrate that it is more effective than the deterministic model and that the optimal pricing is preferable. This study indicates that adequate DR schemes like the proposed one are promising to increase the customers' satisfaction in addition to improve the profitability of the energy aggregation business.
    2017-03-01Journal article Restricted access Show more