3 resultados para Alkaline extraction and molbydate blue spectrophotometry
em Repositório Institucional da Universidade Tecnológica Federal do Paraná (RIUT)
Resumo:
Esta dissertação é composta por 5 artigos.
Resumo:
The fish meat has a particular chemical composition which gives its high nutritional value. However, this food is identified for being highly perishable and this aspect is often named as a barrier to fish consumption. The southwestern Paraná region, parallel to the country's reality, it is characterized by low fish consumption; and one of the strategies aimed at increasing the consumption of this important protein source is encouraging the production of other species besides tilapia. Within this context, it is necessary to know about the meat characteristics. In this sense, the objective of this study was to evaluate the technological potential of pacu, grass carp and catfish species. To do so, at first, it was discussed the chemical and biometric assessment under two distinct descriptive statistical methods, of the three species; and it was also evaluated the discriminating capacity of the study. In a second moment, an evaluation of effects done by two different processes of washing (acid and alkaline) regarding the removal of nitrogen compounds, pigments and the emulsifying ability of the proteins contained in the protein base obtained. Finally, in the third phase, it was aimed to realize the methodology optimization in GC-MS for the analysis geosmin and MIB (2-metilisoborneol) compounds that are responsible for taste/smell of soil and mold in freshwater fish. The results showed a high protein and low lipid content for the three species. The comparison between means and medians revealed symmetry only for protein values and biometric measurements. Lipids, when evaluated only by the means, overestimate the levels for all species. Correlations between body measurements and fillet yield had low correlation, regardless of the species analyzed, and the best prediction equation relates the total weight and fillet weight. The biometric variables were the best discriminating among the species. The evaluation of the washings, it was found that the acidic and basic processes were equally (p ≥ 0.05) efficient (p ≤ 0.05) for the removal of nitrogen compounds on the fish pulps. Regarding the extraction of pigments, a removal efficiency was recorded only for the pacu species, the data were assessed by the parameters L *, a *, b *. When evaluated by the total color difference (ΔE) before and after washing for both processes (acid/alkaline) the ΔE proved feasible perceived by naked eye for all species. The catfish was characterized as the fish that presents the clearest meat with the basic washing considered the most effective in removing pigments for this species. Protein bases obtained by alkaline washes have higher emulsifying capacity (p ≤ 0.05) when compared to unwashed and washed in acid process pulps. The methodology applied for the quantification of MIB and geosmin, allowed to establish that the method of extraction and purification of analytes had low recovery and future studies should be developed for identification and quantification of MIB and geosmin on fish samples.
Resumo:
Humans have a high ability to extract visual data information acquired by sight. Trought a learning process, which starts at birth and continues throughout life, image interpretation becomes almost instinctively. At a glance, one can easily describe a scene with reasonable precision, naming its main components. Usually, this is done by extracting low-level features such as edges, shapes and textures, and associanting them to high level meanings. In this way, a semantic description of the scene is done. An example of this, is the human capacity to recognize and describe other people physical and behavioral characteristics, or biometrics. Soft-biometrics also represents inherent characteristics of human body and behaviour, but do not allow unique person identification. Computer vision area aims to develop methods capable of performing visual interpretation with performance similar to humans. This thesis aims to propose computer vison methods which allows high level information extraction from images in the form of soft biometrics. This problem is approached in two ways, unsupervised and supervised learning methods. The first seeks to group images via an automatic feature extraction learning , using both convolution techniques, evolutionary computing and clustering. In this approach employed images contains faces and people. Second approach employs convolutional neural networks, which have the ability to operate on raw images, learning both feature extraction and classification processes. Here, images are classified according to gender and clothes, divided into upper and lower parts of human body. First approach, when tested with different image datasets obtained an accuracy of approximately 80% for faces and non-faces and 70% for people and non-person. The second tested using images and videos, obtained an accuracy of about 70% for gender, 80% to the upper clothes and 90% to lower clothes. The results of these case studies, show that proposed methods are promising, allowing the realization of automatic high level information image annotation. This opens possibilities for development of applications in diverse areas such as content-based image and video search and automatica video survaillance, reducing human effort in the task of manual annotation and monitoring.