Browsing by Author "Silva, Nuno"
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- Antimicrobial activity of essential oils from mediterranean aromatic plants against several foodborne and spoilage bacteriaPublication . Silva, Nuno; Alves, Sofia; Gonçalves, Alexandre; Amaral, Joana S.; Poeta, PatríciaThe antimicrobial activity of essential oils extracted from a variety of aromatic plants, often used in the Portuguese gastronomy was studied in vitro by the agar diffusion method. The essential oils of thyme, oregano, rosemary, verbena, basil, peppermint, pennyroyal and mint were tested against Gram-positive (Listeria monocytogenes, Clostridium perfringens, Bacillus cereus, Staphylococcus aureus, Enterococcus faecium, Enterococcus faecalis, and Staphylococcus epidermidis) and Gram-negative strains (Salmonella enterica, Escherichia coli, and Pseudomonas aeruginosa). For most essential oils examined, S. aureus, was the most susceptible bacteria, while P. aeruginosa showed, in general, least susceptibility. Among the eight essential oils evaluated, thyme, oregano and pennyroyal oils showed the greatest antimicrobial activity, followed by rosemary, peppermint and verbena, while basil and mint showed the weakest antimicrobial activity. Most of the essential oils considered in this study exhibited a significant inhibitory effect. Thyme oil showed a promising inhibitory activity even at low concentration, thus revealing its potential as a natural preservative in food products against several causal agents of foodborne diseases and food spoilage. In general, the results demonstrate that, besides flavoring the food, the use of aromatic herbs in gastronomy can also contribute to a bacteriostatic effect against pathogens.
- PROMORE: a procedural modeler of virtual rural environments with artificial dataset generation capabilities for remote sensing contextsPublication . Adão, Telmo; Cerqueira, João; Adão, Miguel; Silva, Nuno; Pascoal, David; Magalhães, Luís G.; Barros, Tiago; Premebida, Cristiano; Nunes, Urbano J.; Peres, Emanuel; Morais, RaulRemote sensing (RS) is a rapidly evolving field that facilitates the study of phenomena on the Earth’s surface. Through various platforms, including satellites, manned aircraft, and remotely piloted aerial vehicles (RPAV), RS has been strategically applied to critical sectors like agriculture and forestry, which are essential for humanity’s sustenance. Key applications include crops classification, yield estimation and livestock monitoring and quantification. In the era of artificial intelligence (AI), the development of deep learning (DL) models for such applications often requires extensive field data collection and labor-intensive image labeling, which are both time-consuming and resource-intensive. To address these challenges, this paper presents Procedural Modeling of Rural Environments (PROMORE), a parameterizable, ontology driven system designed to generate 3D virtual environments encompassing forestry, farmland – mainly focused on vineyards – and village settings. This system also implements functionalities to automate the extraction of training data for deep learning applications in remote sensing, with the declared aim of providing complementary capabilities to data augmentation techniques, encompassing both traditional methods (e.g., flips, rotations, zooming) and advanced approaches such as generative adversarial networks (GANs). By simulating RPAV flights and managing virtual object visibility, PROMORE enables the automatic labeling, delineation, and highlighting of elements of interest (e.g., vine plants, trees, buildings), facilitating the generation of datasets tailored for tasks such as semantic segmentation, and object detection.