Uso de diferentes agentes ligantes no desenvolvimento de barra de cereal salgada adicionada de chia (Salvia hispânica L.)

The objective of this study was to evaluate different binders when preparing salt cereal bars and to characterize them as physical, physico-chemical and sensory parameters. Four formulations of different cereal bars using binders have been developed. The evaluated binders were collagen, guar gum, xanthan gum and psyllium. The developed cereal bars were evaluated according to their physical characteristics (color and texture), physicochemical (pH, moisture, ash, protein, lipids, Aw, crude fiber) beyond their calorie, fatty acid composition and concentration of the main minerals. Among the four binding agents evaluated, psyllium stood out due to its physicochemical characteristics. A cereal bar high in protein and fiber; low in carbohydrates and water activity. The binding agent guar gum and xanthan showed characteristics similar to psyllium, especially regarding to fiber content. Collagen as binder gave the final product a high level in protein and lipid. The color and texture analyzes showed that the salt cereal bars had the color and crispness characteristics for this type of product. Regarding to the composition in the fatty acid, the developed bars offer a good supply of essential fatty acids to the human body. The same was observed regarding to mineral contents. Sensory, salt cereal bars made with chia showed good acceptability, highlighting the elaborate bar with psyllium binder. Different binders demonstrated technological efficiency in the preparation of salt cereal bars. The binder psyllium agent over others showed better physical-chemical and sensory characteristics. However, in general the product has healthy and nutritional characteristics it may be indicated for a protein diet with high fiber content and free sugars.

A contribution to semantic description of images and videos: an application of soft biometrics

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.