Background: currently, functional foods are the type of foods of most interest to the modern consumer, due to the health benefits they provide. Objectives: Optimize the spray drying process to obtain cape gooseberry powder added with active compounds. Methods: A process of spray drying was carried out to obtain a powder from cape gooseberry suspensions added with vitamin C, iron, folic acid, isolated soy protein and dietary fiber. The drying process was optimized according to the characteristics of food formulations and operating conditions, obtaining a product with low hygroscopicity, high solubility and high levels of physiologically active compounds. Response surface methodology was used, considering a central composite design with four factors: maltodextrin (0-40%p/p), inlet air temperature (170-210°C), atomizer disc speed (16000-24000 rpm) and outlet air temperature (75-95°C). Results: The results showed a higher retention of vitamin C (69.7±0.7%), folic acid (90.9±1.8%) and iron (90.8±1.0%) with the food formulation containing a 24.4% of maltodextrin and the drying process defined by an atomizer disc speed of 19848 rpm and inlet and outlet air temperature of 194.2°C and 87.7°C, respectively. Conclusions: The spray drying process is an effective technology that provides added value to the fruit of cape gooseberry, allowing the incorporation and conservation of active compounds such as iron, folic acid and ascorbic acid.
Due to its popularity, social networks (SNs) have been subject to different analyses. A research field in this area is the identification of several types of users and groups. To make the identification process easier, a SN is usually represented through a graph.
A research field in the area of social networks (SNs) is the identification of some types of users and groups. To facilitate this process, a SN is usually represented by a graph. The centrality measures, which identify the most important vertices in a graph according to some criterion, are usual tools to analyze a graph. One of these measures is the PageRank (a measure originally designed to classify web pages). Informally, in the context of a SN, the PageRank of a user i represents the probability that another user of the SN is seeing the page of i after a considerable time of navigation in the SN. In this paper, we define a new type of user in a SN: the best current friend. The idea is to identify, among the friends of a user i, who is the friend k that would generate the highest decrease in the PageRank of i if k stops being his/her friend. This may be useful to identify the users/customers whose friendship/relationship should be a priority to keep. We provide formal definitions, algorithms and some experiments for this subject. Our experiments showed that the best current friend of a user is not necessarily the one who has the highest PageRank in the SN nor the one who has more friends.Keywords: Social networks, friends, centrality measures, pagerank, graphs. RESUMENUn campo de investigación en el área de las redes sociales (RSs) es la identificación de ciertos tipos de usuarios y de grupos. Para facilitar este proceso, una RS se suele representar mediante un grafo. Las medidas de centralidad, las cuales identifican los nodos más importantes en un grafo según algún criterio, suelen ser usadas para analizar un grafo. Una de estas medidas es el PageRank (una medida inicialmente concebida para clasificar las páginas web). Informalmente, en el contexto de las RSs, el PageRank de un usuario i representa la probabilidad de que otro usuario de la RS esté viendo la página de i luego de un tiempo considerable de navegación por la RS. En este artículo, se define un tipo de usuario en una RS: el mejor amigo actual. La idea es identificar, entre los amigos de i, quién es el amigo k que generaría el mayor decremento en el PageRank de i, si k dejara de ser amigo de i. Esto puede ser útil para identificar los usuarios/clientes cuya amistad/relación es prioritario conservar. En este artículo se presentan las definiciones formales, algoritmos y experimentos al respecto. Los experimentos demostraron que el mejor amigo actual de un usuario no es necesariamente aquel que tiene el mayor PageRank en la RS ni aquel que tiene más amigos.
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