2 resultados para content discovery and ranking

em Instituto Politécnico do Porto, Portugal


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Presently power system operation produces huge volumes of data that is still treated in a very limited way. Knowledge discovery and machine learning can make use of these data resulting in relevant knowledge with very positive impact. In the context of competitive electricity markets these data is of even higher value making clear the trend to make data mining techniques application in power systems more relevant. This paper presents two cases based on real data, showing the importance of the use of data mining for supporting demand response and for supporting player strategic behavior.

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The antioxidant activity and phenolic composition of brewer's spent grain (BSG) extracts obtained by microwave-assisted extraction from twomalt types (light and darkmalts) were investigated. The total phenolic content (TPC) and antioxidant activity among the light BSG extracts (pilsen, melano, melano 80 and carared)were significantly different (p b 0.05) compared to dark extracts (chocolate and black types), with the pilsen BSG showing higher TPC (20 ± 1 mgGAE/g dry BSG). In addition, the antioxidant activity assessed by 2,2-diphenyl- 1-picrylhydrazyl, 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) and deoxyribose assays decreased as a result of increasing kilning temperatures in the following order: pilsen N melano N melano 80 N carared N chocolate N black. HPLC-DAD/ESI-MS/MS analysis indicated the presence of phenolic acids, such as ferulic, p-coumaric and syringic acids, as well as several isomeric ferulate dehydrodimers and one dehydrotrimer. Chocolate and black extracts, obtained frommalts submitted to the highest kilning temperatures, showed the lowest levels of ferulic and p-coumaric acids. These results suggested that BSG extracts from pilsen malt might be used as an inexpensive and good natural source of antioxidants with potential interest for the food, pharmaceutical and/or cosmetic industries after purification.