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The physical properties density and viscosity of 21 binary mixtures composed of 1-hexanol, 1-heptanol, 1octanol, 1-nonanol, 1-decanol, 1-undecanol, and 1-dodecanol were determined at temperatures from 298.15 to 338.15 K, stepped by 10 K. Well-known methods, based on the physical properties of the pure compounds and their compositions in the mixtures, were used to estimate density (simple form of Kay's rule) and viscosity (logarithmic form of Kay's rule). For density values, the absolute average deviations (AADs) were no higher than 0.06%, when mass fraction was used as the unit of concentration. The viscosity calculations resulted in overall AADs from 1.77% (using molar fraction as the unit of concentration) to 1.85% (using mass fraction as the unit of concentration). Additionally, the predictive capability of the UNIFAC-VISCO and GC-UNIMOD models was tested for viscosity. Satisfactory results were found for all the sets of parameters evaluated in the prediction, with AAD values less than 2%, indicating the appropriate application of these models.

Sweetener type can influence sensory properties and consumer’s acceptance and preference for low-calorie products. An ideal sweetener does not exist, and each sweetener must be used in situations to which it is best suited. Aspartame and sucralose can be good substitutes for sucrose in passion fruit juice. Despite the interest in artificial sweeteners, little is known about how artificial sweeteners are processed in the human brain. Here, we applied the convolutional neural network (CNN) to evaluate brain signals of 11 healthy subjects when they tasted passion fruit juice equivalently sweetened with sucrose (9.4 g/100 g), sucralose (0.01593 g/100 g), or aspartame (0.05477 g/100 g). Electroencephalograms were recorded for two sites in the gustatory cortex (i.e., C3 and C4). Data with artifacts were disregarded, and the artifact-free data were used to feed a Deep Neural Network with tree branches that applied a Convolutions and pooling for different feature filtering and selection. The CNN received raw signal as input for multiclass classification and with supervised training was able to extract underling features and patterns from the signal with better performance than handcrafted filters like FFT. Our results indicated that CNN is an useful tool for electroencephalography (EEG) analyses and classification of perceptually similar tastes.

The electrical signal emitted by the eyes movement produces a very strong artifact on EEG signal due to its close proximity to the sensors and abundance of occurrence. In the context of detecting eye blink artifacts in EEG waveforms for further removal and signal purification, multiple strategies where proposed in the literature. Most commonly applied methods require the use of a large number of electrodes, complex equipment for sampling and processing data. The goal of this work is to create a reliable and user independent algorithm for detecting and removing eye blink in EEG signals using CNN (convolutional neural network). For training and validation, three sets of public EEG data were used. All three sets contain samples obtained while the recruited subjects performed assigned tasks that included blink voluntarily in specific moments, watch a video and read an article. The model used in this study was able to have an embracing understanding of all the features that distinguish a trivial EEG signal from a signal contaminated with eye blink artifacts without being overfitted by specific features that only occurred in the situations when the signals were registered.

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