Magnetic fields (MF) are increasingly being applied in food processing to preserve food quality. They can be static (SMF), oscillating (OMF) or pulsed (PMF) depending on the type of equipment. The food characteristics can be influenced by several configurations of the applied magnetic field as its flux density, frequency, polarity and exposure time. Several mechanisms have been proposed to explain the effects of magnetic fields on foods. Some of them propose interactions at the subatomic particle level that show quantum behavior, such as the radical pair and cyclotron resonance mechanisms. Other proposals are at the level of DNA, compounds, subcellular organelles and cells. The interactions between food and magnetic fields are addressed in a general way in this work, highlighting the applications and action models involved and their effects on the physicochemical, enzymatic and microbiological characteristics of food.

This article uses the Adaptive Gaussian Representation (AGR) for human electroencephalogram (EEG) feature extraction aiming the discrimination among mental tasks to be used in a brain computer interface (BCI). It does not focus on the AGR time-frequency representation, but rather on their projection coefficients. Ten volunteers were asked to imagine either right or left hand movement, according to a proper visual stimulus. The features of the resulting EEG signals were characterised by extracting AGR coefficients. Classification was carried out using a Multilayer perceptron (MLP) trained with the classical backpropagation algorithm. Overall results show that AGR coefficients representation is able to reveal a significant EEG discrimination between imagination of right and left hand movement with a mean classification performance of 91%+/-5.8% achieved for female subjects and 87%+/-5.0% achieved for male subjects.