Browsing by Issue Date, starting with "2019-10-17"
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- PortFIR – An integrated data provider as support for the food policy-makersPublication . Fernandes, Paulo; Lopes, Andreia; Brazão, Roberto; Oliveira, Luísa; Dias, Maria da GraçaIntroduction: Portuguese Food Composition Table (TCA) is managed by the National Institute of Health Doutor Ricardo Jorge (INSA) and is publicly available on the Portuguese Food Information Resource (PortFIR) platform. PortFIR, besides food composition data, was designed to include data on food contamination and food consumption, in order to provide national data easily available in one platform. Objective:To compile and make widely available food-related data (food composition, food safety and food consumption), while providing science-based information, to support food policy-makers.
- Alimentação SeguraPublication . Coelho, Anabela; Furtado, RosáliaIniciativa de sensibilização para o público do ensino escolar básico sobre os perigos alimentares e regras básicas para uma alimentação segura - noções básicas.
- Vitamin D in food samples based on a TDS approachPublication . Ravasco, Francisco; Dias, Maria da GraçaVitamin D occurs in two distinct forms: vitamin D2 (ergocalciferol) and vitamin D3 (cholecalciferol). It is necessary in human diet to calcium absorption and regulation, as well as bone development and in the immune system. Humans naturally produce vitamin D through exposure to sunlight. Vitamin D deficiency became a major health concern in many societies, as ever-growing numbers of people began spending most of their daily routines indoors and removed from sunlight. According EFSA, the daily intake values should be between 10 μg/day for infants and 15 μg/day for adults, for both genders. Objectives: To evaluate the vitamin D content of representative samples of the Portuguese consumption, based on a Total Diet Study (TDS).
- Carotenoids content in Ibero-American and European Foods – contribution to creation of DatabasesPublication . Dias, Maria da GraçaContribution to the creation of databases on the content of carotenoids in foods.
- IDRisk (Improving Data quality for RISK assessment). Module: food description mapping systemPublication . Tomé, SidneyIntroduction: For the past years we have been working with EFSA on report of food data for the domains of chemical contaminants and food additives; Over the years, INSA has been the central point for gathering data from multiple national entities for processing and redirecting these information to EFSA; A National Data Management Systems was built from the start in order to facilitate such tasK; A consortium between 3 partners (INSA, ASAE & HAPIH) was created and they share the common interest of further developing their own official control National Data Management Systems (NDMS); INSA has developed PT.ON.DATA NDMS based on SSD and SSD2 data models in cooperation with ASAE and other national competent authorities. HAPIH has developed a NDMS with a similar approach; Both ASAE and HAPIH are interested in implementing real-time sample data collection based on preparatory digital forms already existing in PT.ON.DATA. The three partners are committed to investigate and implement an automatic approach to FoodEx2 classification of food samples using the knowledge and databases existing in HAPIH and INSA. Consortium aim: The consortium envisages improving the quality of raw occurrence data for risk assessment by reducing error, incrementing completeness and timeliness both in data fields and food classification, and simultaneously reducing human time and work and therefore releasing time of scientists for data analysis and for performing risk assessment; Together, the improvements will be reflected on the strengthening of food safety risk assessment capacity of the countries involved and contributing to a better assess on risks associated with the food chain by EFSA. Project IDRisk (Improving Data quality for RISK assessment) - This project emerged from two distinct problems regarding data report: Technicians/Data experts, whether they are working on-field sampling data or cleaning and preparing data for report, an absurd amount of manual work is done: too much time is spent; The more manual work we have, the more prone to errors the data will become. Main objectives: Improve/Restructure the dynamic sampling forms module; Develop the application that will run on the mobile devices; Plan and implement an automatic NDMS FoodEx2 classification system for sampling descriptions.
- IDRisk - Improving d ata quality for risk assessmentPublication . Nabais, Pedro; Carmona, Paulo; Monteiro, Sarogini; Vasconcelos, Filipa Melo de; Liberato, Patrícia; Dias, Maria da Graça; Oliveira, Luísa; Brazão, Roberto; Ravasco, Francisco; Sokolić, Darja; Bašić, Sandra; Tomé, Sidney“PT•ON•DATA“, has features to load sampling as well as laboratory related data files containing sampling information and results of the analyses, respectively. It also allows the mapping and validation of the analytical data in SSD2 format; conduct research and statistics; extract results and report data to different authorities. In addition has the dynamic creation of electronic sampling forms, which permits through a mobile device, to fill in the information gathered in the field.