Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2
Search results for: Ouassima Riffi
2 Phytochemical Evaluation and In-Vitro Antibacterial Activity of Ethanolic Extracts of Moroccan Lavandula x Intermedia Leaves and Flowers
Authors: Jamila Fliou, Federica Spinola, Ouassima Riffi, Asmaa Zriouel, Ali Amechrouq, Luca Nalbone, Alessandro Giuffrida, Filippo Giarratana
Abstract:
This study performed a preliminary evaluation of the phytochemical composition and in vitro antibacterial activity of ethanolic extracts of Lavandula x intermedia leaves and flowers collected in the Fez-Meknes region of Morocco. Phytochemical analyses comprised qualitative colourimetric determinations of alkaloids, anthraquinones, and terpenes and quantitative analysis of total polyphenols, flavonoids, and condensed tannins by UV spectrophotometer. Antibacterial activity was evaluated by determining minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values against different ATCC bacterial strains. The phytochemical analysis showed a high amount of total polyphenols, flavonoids, and tannins in the leaf extract and a higher amount of terpenes based on colourimetric reaction than the flower extract. A positive colourimetric reaction for alkaloids and anthraquinones was detected for both extracts. The antibacterial activity of leaves and flower extract was not different against Gram-positive and Gram-negative strains (p<0.05). The results of the present study suggest the possible use of ethanolic extracts of L. x intermedia collected in the Fez-Meknes region of Morocco as a natural agent against bacterial pathogens.Keywords: antimicrobial activity, Lavandula spp., lavender, lavandin, UV spectrophotometric analysis
Procedia PDF Downloads 691 Optimization of Cloud Classification Using Particle Swarm Algorithm
Authors: Riffi Mohammed Amine
Abstract:
A cloud is made up of small particles of liquid water or ice suspended in the atmosphere, which generally do not reach the ground. Various methods are used to classify clouds. This article focuses specifically on a technique known as particle swarm optimization (PSO), an AI approach inspired by the collective behaviors of animals living in groups, such as schools of fish and flocks of birds, and a method used to solve complex classification and optimization problems with approximate solutions. The proposed technique was evaluated using a series of second-generation METOSAT images taken by the MSG satellite. The acquired results indicate that the proposed method gave acceptable results.Keywords: remote sensing, particle swarm optimization, clouds, meteorological image
Procedia PDF Downloads 19