Biblioteca Digital do IPB
Publications Repository of the Polytechnic Institute of Bragança
Recent Submissions
Optimization of heat and ultrasound-assisted extraction of Eucalyptus globulus leaves reveals strong antioxidant and antimicrobial properties
Publication . Lima, Laíres; Pereira, Ana I.; Vaz, Clara Bento; Amorim Sá Ferreira, Maria Olga; Dias, Maria Inês; Heleno, Sandrina A.; Calhelha, Ricardo C.; Barros, Lillian; Carocho, Márcio
The extraction of phenolic compounds from eucalyptus leaves was optimized using heat and ultrasound-assisted
techniques, and the bioactive potential of the resulting extract was assessed. The independent variables,
including time (t), solvent concentration (S), and temperature (T) or power (P), were incorporated into a fivelevel
central composite design combined with Response Surface Methodology. Phenolic content was determined
by HPLC-DAD-ESI/MS and used as response criteria. The developed models were successfully fitted to the
experimental data to identify the optimal extraction conditions. Heat-assisted extraction proved to be the most
efficient method for phenolic recovery, yielding 27 ± 2 mg/g extract under optimal conditions (120 min,
76.5 ◦C, and 25 % ethanol, v/v). The extracts exhibited a high concentration of phenolic glycoside derivatives,
including gallotannin, quercetin, and isorhamnetin. These findings suggest that the extracts hold promise as
natural additives in food technology, owing to their moderate antimicrobial activity and strong antioxidant
properties.
Optimization, Learning Algorithms and Applications (OL2A 2024) Part II
Publication . Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Paulo Teixeira, Joao; Lima, José; Pacheco, Maria F.; Lopes, Rui Pedro; Álvarez, Santiago T.; Santiago T.
This volume, CCIS 2280, contains the refereed proceedings of the 4th International
Conference on Optimization, Learning Algorithms and Applications (OL2A 2024), a
hybrid event held on July 24–26.
OL2A provided a space for the research community on optimization and learning
to get together and share the latest developments, trends and techniques as well as
develop new paths and collaborations. OL2A had the participation of more than three
hundred participants in an online and face-to-face environment throughout three days,
discussing topics associated with areas such as optimization and learning and state-of-the-
art applications related to multi-objective optimization, optimization for machine
learning, robotics, health informatics, data analysis, optimization and learning under
uncertainty, and the 4th industrial revolution.
Three special sessions were organized on the topics Learning Algorithms in Engineering
Education, Optimization in the SDG context, and Optimization in Control Systems
Design. The event accepted 41 papers. All papers were carefully reviewed and
selected from 105 submissions. All the reviews were carefully carried out by a scientific
committee of 115 researchers from twenty-six countries.
Optimization, Learning Algorithms and Applications (OL2A 2024) Part I
Publication . Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Paulo Teixeira, Joao; Lima, José; Pacheco, Maria F.; Lopes, Rui Pedro; Álvarez, Santiago T.; Álvarez
This volume, CCIS 2280, contains the refereed proceedings of the 4th International
Conference on Optimization, Learning Algorithms and Applications (OL2A 2024), a
hybrid event held on July 24–26.
OL2A provided a space for the research community on optimization and learning
to get together and share the latest developments, trends and techniques as well as
develop new paths and collaborations. OL2A had the participation of more than three
hundred participants in an online and face-to-face environment throughout three days,
discussing topics associated with areas such as optimization and learning and state-ofthe-
art applications related to multi-objective optimization, optimization for machine
learning, robotics, health informatics, data analysis, optimization and learning under
uncertainty, and the 4th industrial revolution.
Three special sessions were organized on the topics Learning Algorithms in Engineering
Education, Optimization in the SDG context, and Optimization in Control Systems
Design. The event accepted 41 papers. All papers were carefully reviewed and
selected from 105 submissions. All the reviews were carefully carried out by a scientific
committee of 115 researchers from twenty-six countries.
Exploring acorn shells: Phenolic composition and bioactive potential for sustainable valorization
Publication . Mateus, Cristiano; Alonso-Esteban, José Ignacio; Finimundy, Tiane Cristine; Mandim Pires, Ana Filipa; de Oliveira, Izamara; Babo, Pedro; Canadas, Raphael; Ferreira, Isabel C.F.R.; Barros, Lillian
Pedunculate (Quercus robur L.), holm (Quercus rotundifolia Lam.), and cork (Quercus suber L.) oaks are abundant
across the Portuguese landscape. This study aims to evaluate the phenolic composition and bioactivities of acorn
shell samples and determine their potential as a functional compound source. In total, five acorn shell samples
collected in different locations and from different species were analyzed: Q. rotundifolia (Q. rot-1 and Q. rot-2),
Q. suber (Q. sub-1 and Q. sub-2) and Q. robur (Q. rob-1). A total of nine phenolic compounds were tentatively
identified, namely gallic and ellagic acids and derivatives. Digalloyl hexoside was the compound detected in
higher concentrations in all extracts (2.093 – 8.3 mg/g extract). Q. suber samples exhibited the lowest IC50 values
for TBARS assay, lower than the positive control used (Trolox). Overall, the studied samples demonstrated the
capacity to inhibit the proliferation of all tumor cell lines tested. Sample Q. sub-1 demonstrated the most
promising antibacterial capacity. According to the results, the acorn shell extracts exhibited promising potential,
and it may be interesting to conduct a deeper study on the samples of this species.
Machine learning classification of consumption habits of creatine supplements in GYM goers
Publication . Magalhães, Patrícia C.; Encarnação, Samuel; Schneider, Andre; Miguel Forte, Pedro; de Araújo Teixeria, José Eduardo; Monteiro, A. M.; Barbosa, Tiago M.; Pereira, Ana M.; ENCARNACAO, SAMUEL GONCALVES ALMEIDA
The aim is to identify usage patterns and the main factors that influence creatine supplementation, providing a basis for future educational interventions and recommendations for safe and effective use. The study was applied to gym goers in Bragança, where a QR code for a survey was released. 158 people participated, 65 non-consumers of creatine supplementation (37.34% men; 22.78% women) and 95 consumers (15.19% men; 24.68% women). Five machine learning algorithms were implemented to classify creatine consumption in gym goers: Logistic Regression, Gradient Boosting Classifier, Ada Boost Classifier, Xgboost Classifier. K-folds cross-validation was implemented to validate the machine learning performance. There was an increased proportion of females with considered themselves not sufficiently informed about the creatine effects/side effects (22.2%) in comparison to males (8.47%), p=0.03. The AdaBoost classifier exposed the best overall performance (86%) in classifying overuse of creatine in gym goers based on their Smoke habits (r = 0.33), grams of creatine used per day (r = 0.50) and lack information about the side effects of creatine intake (r = -0.33). The K-folds method validates the results with very good performance (86%). In conclusion, the five machine learning methods employed well characterized the overuse of creatine in gym goers based on smoke habits, grams of creatine per day, and lack information about the side effects of creatine intake.