Centre of Biotechnology and Fine Chemistry

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Omega-3 fatty acids from fish by-products: Innovative extraction and application in food and feed

Publication . Rodrigues, Matilde; Rosa, Ana; Almeida, André; Martins, Rui; Ribeiro, Tânia; Pintado, Manuela; Gonçalves, Raquel F.S.; Pinheiro, Ana C.; Fonseca, António J.M.; Maia, Margarida R.G.; Cabrita, Ana R.J.; Barros, Lillian; Caleja, Cristina

Omega -3 fatty acids (O3FA) are essential nutrients that play a crucial role in maintaining human and animal health. They are known for their numerous health claims, including cardiovascular benefits, contributing to both the prevention and treatment of immunological, neurological, reproductive, and cardiovascular complications, and supporting overall well-being. Fish, especially oily fish, comprise rich source of O3FA. In the fish industry, significant amounts of by-products and waste are generated during processing which are often discarded or used for lower -value applications. However, there is recognition of the potential value of extracting O3FA from these by-products. Various extraction techniques can be used, but the goal is to efficiently extract and concentrate the O3FA while minimizing the loss of nutritional value. To prevent oxidation and maintain the stability of O3FA, natural antioxidants can be added. Antioxidants like polyphenolic compounds and plant extracts help to protect the O3FA from degradation caused by exposure to oxygen, light, and heat. By stabilizing the O3FA, the shelf life and nutritional value of the extracted product can be extended. In summary, this work presents a forwardlooking strategy for transforming fish by-products into high -quality oils, which hold great potential for application in food and feed.

2024Journal article

Open access

Smart-data-driven system for alzheimer disease detection through electroencephalographic signals

Publication . Araújo, Teresa; Teixeira, João Paulo; Rodrigues, Pedro Miguel

Alzheimer’s Disease (AD) stands out as one of the main causes of dementia worldwide and it represents around 65% of all dementia cases, affecting mainly elderly people. AD is composed of three evolutionary stages: Mild Cognitive Impairment (MCI), Mild and Moderate AD (ADM) and Advanced AD (ADA). It is crucial to create a tool for assisting AD diagnosis in its early stages with the aim of halting the disease progression. Methods: The main purpose of this study is to develop a system with the ability of differentiate each disease stage by means of Electroencephalographic Signals (EEG). Thereby, an EEG nonlinear multi-band analysis by Wavelet Packet was performed enabling to extract several features from each study group. Classic Machine Learning (ML) and Deep Learning (DL) methods have been used for data classification per EEG channel. Results: The maximum accuracies obtained were 78.9% (Healthy controls (C) vs. MCI), 81.0% (C vs. ADM), 84.2% (C vs. ADA), 88.9% (MCI vs. ADM), 93.8% (MCI vs. ADA), 77.8% (ADM vs. ADA) and 56.8% (All vs. All). Conclusions: The proposed method outperforms previous studies with the same database by 2% in binary comparison MCI vs. ADM and central and parietal brain regions revealed abnormal activity as AD progresses.

2022Journal article

Open access