ALBERT

Description: This study investigated the knowledge, attitudes, consumption habits, and degree of acceptability of edible insects (EI) or derived products among Portuguese consumers. This work consisted of a questionnaire survey, undertaken on a sample of 213 participants. For the treatment of data, basic descriptive statistics were used, complemented with chi-square tests to assess some associations between categorical variables. Moreover, a tree classification analysis was carried out using a classification and regression tree (CRT) algorithm with cross-validation. The results indicated that people tend to have correct perceptions about the sustainability issues associated with the use of insects as alternative sources of protein; however, the level of knowledge and overall perception about their nutritive value is low. Regarding the consumption of EI, it was found that only a small part of the participants had already eaten them, doing it mostly abroad, by self-initiative, in a restaurant or at a party or event. Additionally, it was found that the reluctance to consume insects is higher if they are whole, but when they are transformed into ingredients used in food formulations, the level of acceptance increases. Furthermore, men have shown to have a better perception about EI, be more informed about sustainability, and have a higher level of acceptability when compared to women. As a final conclusion, it was observed that the Portuguese still show some resistance to adhere to the use of insects as replacements for meat products, but the market of insect based products can be a good alternative to overpass the neophobia associated with this type of food.

Description: This research study focuses on the evaluation of the total phenolic compounds (TPC) and antioxidant activity (AOA) of strawberries according to different experimental extraction conditions by applying the Artificial Neural Networks (ANNs) technique. The experimental data were applied to train ANNs using feed- and cascade-forward backpropagation models with Levenberg-Marquardt (LM) and Bayesian Regulation (BR) algorithms. Three independent variables (solvent concentration, volume/mass ratio and extraction time) were used as ANN inputs, whereas the three variables of total phenolic compounds, DPPH and ABTS antioxidant activities were considered as ANN outputs. The results demonstrate that the best cascade- and feed-forward backpropagation topologies of ANNs for the prediction of total phenolic compounds and DPPH and ABTS antioxidant activity factors were the 3-9-1, 3-4-4-1 and 3-13-10-1 structures, with the training algorithms of trainlm, trainbr, trainlm and threshold functions of tansig-purelin, tansig-tansig-tansig and purelin-tansig-tansig, respectively. The best R2 values for the predication of total phenolic compounds and DPPH and ABTS antioxidant activity factors were 0.9806 (MSE = 0.0047), 0.9651 (MSE = 0.0035) and 0.9756 (MSE = 0.00286), respectively. According to the comparison of ANNs, the results showed that the cascade-forward backpropagation network showed better performance than the feed-forward backpropagation network for predicting the TPC, and the FFBP network, in predicting the DPPH and ABTS antioxidant activity factors, had more precision than the cascade-forward backpropagation network. The ANN technique is a potential method for estimating targeted total phenolic compounds and the antioxidant activity of strawberries.