Implementação de metodologias para a contagem da flora específica do iogurte e de bifidobactérias na empresa SGS Portugal, S.A.

É do conhecimento geral que a população portuguesa está cada vez mais preocupada com a sua saúde e qualidade de vida, bem como em mudar alguns dos seus hábitos alimentares. Consequentemente, a procura por alimentos que proporcionam, para além de boas propriedades organolépticas e nutritivas, benefícios à saúde do consumidor, aumentou significativamente nas últimas décadas, onde os produtos lácteos fermentados surgem em grande destaque. Vários são os microrganismos empregues no desenvolvimento de produtos lácteos fermentados. Na produção de iogurtes, são utilizadas culturas lácteas tradicionais (Lactobacillus delbrueckii subsp. bulgaricus e Streptococcus thermophilus), também denominadas por flora específica do iogurte, e/ou bactérias probióticas, como as bifidobactérias, devido às suas boas características tecnológicas, terapêuticas e sensoriais. O trabalho descrito nesta dissertação de mestrado foca-se na validação e implementação de metodologias que permitem a contagem da flora específica do iogurte e de bifidobactérias, em iogurtes, na empresa SGS Portugal, S.A. A enumeração da flora específica do iogurte teve como base a norma ISO 7889:2003, por meio da técnica de sementeira por incorporação no meio de cultura apropriado e posterior contagem em placa dos microrganismos presentes. Foram analisadas 20 amostras de iogurtes, obtendo-se valores de contagens da flora específica do iogurte na ordem de 108 UFC/g de produto, indo ao encontro dos valores estabelecidos pela legislação portuguesa em vigor. A enumeração de bifidobactérias teve como base a norma ISO 29981:2010, por meio da técnica de sementeira por incorporação no meio de cultura apropriado e posterior contagem em placa dos microrganismos presentes. Das 20 amostras de iogurtes probióticos analisadas, apenas 15 obtiveram resultados positivos, apresentando valores de contagens de bifidobactérias superiores a 106 UFC/g de produto. Estas amostras, para além de apresentarem valores de acordo com os valores estabelecidos pela legislação portuguesa em vigor, são terapeuticamente eficazes, durante todo o prazo de validade do produto, uma vez que apresentam valores de contagem de bactérias probióticas superiores a 106 UFC/g, sendo este o padrão mínimo necessário para que um iogurte seja considerado como probiótico. Com base nos resultados obtidos e no cumprimento dos parâmetros preconizados nos procedimentos de validação de métodos microbiológicos, foi possível a validação destas metodologias e as suas implementações na empresa SGS Portugal, S.A

Characterising the agriculture 4.0 landscape - Emerging trends, challenges and opportunities

ReviewInvestment in technological research is imperative to stimulate the development of sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and sensor networks, robotics, artificial intelligence, big data, cloud computing, etc. foster the transition towards the Agriculture 4.0 era. This fourth revolution is currently seen as a possible solution for improving agricultural growth, ensuring the future needs of the global population in a fair, resilient and sustainable way. In this context, this article aims at characterising the current Agriculture 4.0 landscape. Emerging trends were compiled using a semi-automated process by analysing relevant scientific publications published in the past ten years. Subsequently, a literature review focusing these trends was conducted, with a particular emphasis on their applications in real environments. From the results of the study, some challenges are discussed, as well as opportunities for future research. Finally, a high-level cloud-based IoT architecture is presented, serving as foundation for designing future smart agricultural systems. It is expected that this work will positively impact the research around Agriculture 4.0 systems, providing a clear characterisation of the concept along with guidelines to assist the actors in a successful transition towards the digitalisation of the sectorinfo:eu-repo/semantics/publishedVersio

Generative adversarial networks for data augmentation in structural adhesive inspection

UIDB/- 00066/2020 POCI-01-0247-FEDER-034072The technological advances brought forth by the Industry 4.0 paradigm have renewed the disruptive potential of artificial intelligence in the manufacturing sector, building the data-driven era on top of concepts such as Cyber-Physical Systems and the Internet of Things. However, data availability remains a major challenge for the success of these solutions, particularly concerning those based on deep learning approaches. Specifically in the quality inspection of structural adhesive applications, found commonly in the automotive domain, defect data with sufficient variety, volume and quality is generally costly, time-consuming and inefficient to obtain, jeopardizing the viability of such approaches due to data scarcity. To mitigate this, we propose a novel approach to generate synthetic training data for this application, leveraging recent breakthroughs in training generative adversarial networks with limited data to improve the performance of automated inspection methods based on deep learning, especially for imbalanced datasets. Preliminary results in a real automotive pilot cell show promise in this direction, with the approach being able to generate realistic adhesive bead images and consequently object detection models showing improved mean average precision at different thresholds when trained on the augmented dataset. For reproducibility purposes, the model weights, configurations and data encompassed in this study are made publicly available.publishersversionpublishe

Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding

The energy consumption of production processes is increasingly becoming a concern for the industry, driven by the high cost of electricity, the growing concern for the environment and the greenhouse emissions. It is necessary to develop and improve energy efficiency systems, to reduce the ecological footprint and production costs. Thus, in this work, a system is developed capable of extracting and evaluating useful data regarding production metrics and outputs. With the extracted data, machine learning-based models were created to predict the expected energy consumption of an automotive spot welding, proving a clear insight into how the input values can contribute to the energy consumption of each product or machine, but also correlate the real values to the ideal ones and use this information to determine if some process is not working as intended. The method is demonstrated in real-world scenarios with robotic cells that meet Volkswagen and Ford standards. The results are promising, as models can accurately predict the expected consumption from the cells and allow managers to infer problems or optimize schedule decisions based on the energy consumption. Additionally, by the nature of the conceived architecture, there is room to expand and build additional systems upon the currently existing software