In this paper we show that it is possible to model sensory impressions of consumers about beef meat. This is not a straightforward task; the reason is that when we are aiming to induce a function that maps object descriptions into ratings, we must consider that consumers' ratings are just a way to express their preferences about the products presented in the same testing session. Therefore, we had to use a special purpose SVM polynomial kernel. The training data set used collects the ratings of panels of experts and consumers; the meat was provided by 103 bovines of 7 Spanish breeds with different carcass weights and aging periods. Additionally, to gain insight into consumer preferences, we used feature subset selection tools.

This study evaluates the discriminating capacity (uniqueness) of the EEG data from the WAY EEG GAL public dataset to authenticate individuals against one another as well as its permanence. In addition to the EEG data, Luciw et al. provide EMG (Electromyography), and kinematics data for engineers and researchers to utilize WAY EEG GAL for further studies. However, evaluating the EMG and kinematics data is outside the scope of this study. The goal of the state-of-the-art is to determine whether EEG data can be utilized to control prosthetic devices. On the other hand, this study aims to evaluate the separability of individuals through EEG data to perform user authentication. A feature importance algorithm is utilized to select the best features for each user to authenticate them against all others. The authentication platform implemented for this study is based on Machine Learning models/classifiers. As an initial test, two pilot studies are performed using Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) to observe the learning trends of the models by multi-labeling the EEG dataset. Utilizing kNN first as the classifier for user authentication, accuracy around 75% is observed. Thereafter to improve the performance both linear and non-linear SVMs are used to perform classification. The overall average accuracies of 85.18% and 86.92% are achieved using linear and non-linear SVMs respectively. In addition to accuracy, F1 scores are also calculated. The overall average F1 score of 87.51% and 88.94% are achieved for linear and non-linear SVMs respectively. Beyond the overall performance, high performing individuals with 95.3% accuracy (95.3% F1 score) using linear SVM and 97.4% accuracy (97.3% F1 score) using non-linear SVM are also observed.