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    Identification of Requirements for Computer-Supported Matching of Food Consumption Data with Food Composition Data.

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    Authors
    Koroušić Seljak, Barbara
    Korošec, Peter
    Eftimov, Tome
    Ocke, Marga
    van der Laan, Jan
    Roe, Mark
    Berry, Rachel
    Crispim, Sandra Patricia
    Turrini, Aida
    Krems, Carolin
    Slimani, Nadia
    Finglas, Paul
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    Type
    Article
    Language
    en
    
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    Title
    Identification of Requirements for Computer-Supported Matching of Food Consumption Data with Food Composition Data.
    Published in
    Nutrients 2018; 10(4):E433
    Publiekssamenvatting
    This paper identifies the requirements for computer-supported food matching, in order to address not only national and European but also international current related needs and represents an integrated research contribution of the FP7 EuroDISH project. The available classification and coding systems and the specific problems of food matching are summarized and a new concept for food matching based on optimization methods and machine-based learning is proposed. To illustrate and test this concept, a study has been conducted in four European countries (i.e., Germany, The Netherlands, Italy and the UK) using different classification and coding systems. This real case study enabled us to evaluate the new food matching concept and provide further recommendations for future work. In the first stage of the study, we prepared subsets of food consumption data described and classified using different systems, that had already been manually matched with national food composition data. Once the food matching algorithm was trained using this data, testing was performed on another subset of food consumption data. Experts from different countries validated food matching between consumption and composition data by selecting best matches from the options given by the matching algorithm without seeing the result of the previously made manual match. The evaluation of study results stressed the importance of the role and quality of the food composition database as compared to the selected classification and/or coding systems and the need to continue compiling national food composition data as eating habits and national dishes still vary between countries. Although some countries managed to collect extensive sets of food consumption data, these cannot be easily matched with food composition data if either food consumption or food composition data are not properly classified and described using any classification and coding systems. The study also showed that the level of human expertise played an important role, at least in the training stage. Both sets of data require continuous development to improve their quality in dietary assessment.
    DOI
    10.3390/nu10040433
    PMID
    29601516
    URI
    http://hdl.handle.net/10029/621801
    ae974a485f413a2113503eed53cd6c53
    10.3390/nu10040433
    Scopus Count
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