Artículos Científicos UNJ

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https://repositorio.unj.edu.pe/handle/UNJ/505

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Improving Behavior Monitoring of Free-Moving Dairy Cows Using Noninvasive Wireless EEG Approach and Digital Signal Processing Techniques
(Universidad Nacional de Jaén, 2024-02-26) Arteaga Miñano, Hubert Luzdemio; -
Electroencephalography (EEG) is the most common method to access brain information. Techniques to monitor and extract brain signal characteristics in farm animals are not as developed as those for humans and laboratory animals. The objective of this study was to develop a noninvasive method for monitoring brain signals in cattle, allowing the animals to move freely, and to characterize these signals. Brain signals from six Holstein heifers that could move freely in a paddock compartment were acquired. The control group consisted of the same number of bovines, contained in a climatic chamber (restrained group). In the second step, the signals were characterized by Power Spectral Density, Short-Time Fourier Transform, and Lempel–Ziv complexity. The preliminary results revealed an optimal electrode position, referred to as POS2, which is located at the center of the frontal region of the animal’s head. This positioning allowed for attaching the electrodes to the front of the bovine’s head, resulting in the acquisition of longer artifact-free signal sections. The signals showed typical EEG frequency bands, like the bands found in humans. The Lempel–Ziv complexity values indicated that the bovine brain signals contained random and chaotic components. As expected, the signals acquired from the retained bovine group displayed sections with a larger number of artifacts due to the hot 32 degree C temperature in the climatic chamber. We present a method that helps to monitor and extract brain signal features in unrestrained bovines. The method could be applied to investigate changes in brain electrical activity during animal farming, to monitor brain pathologies, and to other situations related to animal behavior.
Pijuayo (Bactris gasipaes) Pulp and Peel Flours as Partial Substitutes for Animal Fat in Burgers: Physicochemical Properties
(Universidad Nacional de Jaén, 2024-02-26) Arteaga Miñano, Hubert Luzdemio; Rios Mera, Juan Dario
This study aimed to evaluate the incorporation of peach palm (PP) pulp and peel flours as substitutes for animal fat (25 and 50% substitution) in beef-based burgers. Incorporation of PP flours reduced hardness, springiness, cohesiveness, chewiness, fat, cooking losses, and diameter reduction. Burgers made with PP peel flour stood out for having low values of lipid oxidation in the two levels of fat substitution (0.14–0.23 malondialdehyde/kg) (p < 0.05). PP fruit has the potential to be utilized as a new ingredient in burgers, but future studies are needed regarding detailed sensory trials and consumer acceptance.
The convolutional neural network as a tool to classify electroencephalography data resulting from the consumption of juice sweetened with caloric or non-caloric sweeteners
(2022-07-19) Arteaga Miñano, Hubert Luzdemio
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.

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Examinando Artículos Científicos UNJ por Autor "Arteaga Miñano, Hubert Luzdemio"

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