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

2.50
Hdl Handle:
http://hdl.handle.net/10029/621801
Title:
Identification of Requirements for Computer-Supported Matching of Food Consumption Data with Food Composition Data.
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
Abstract:
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.
Citation:
Identification of Requirements for Computer-Supported Matching of Food Consumption Data with Food Composition Data. 2018, 10 (4) Nutrients
Journal:
Nutrients 2018; 10(4):E433
Issue Date:
30-Mar-2018
URI:
http://hdl.handle.net/10029/621801
DOI:
10.3390/nu10040433
PubMed ID:
29601516
Type:
Article
Language:
en
ISSN:
2072-6643
Appears in Collections:
Miscellaneous

Full metadata record

DC FieldValue Language
dc.contributor.authorKoroušić Seljak, Barbaraen
dc.contributor.authorKorošec, Peteren
dc.contributor.authorEftimov, Tomeen
dc.contributor.authorOcke, Margaen
dc.contributor.authorvan der Laan, Janen
dc.contributor.authorRoe, Marken
dc.contributor.authorBerry, Rachelen
dc.contributor.authorCrispim, Sandra Patriciaen
dc.contributor.authorTurrini, Aidaen
dc.contributor.authorKrems, Carolinen
dc.contributor.authorSlimani, Nadiaen
dc.contributor.authorFinglas, Paulen
dc.date.accessioned2018-04-19T09:44:57Z-
dc.date.available2018-04-19T09:44:57Z-
dc.date.issued2018-03-30-
dc.identifier.citationIdentification of Requirements for Computer-Supported Matching of Food Consumption Data with Food Composition Data. 2018, 10 (4) Nutrientsen
dc.identifier.issn2072-6643-
dc.identifier.pmid29601516-
dc.identifier.doi10.3390/nu10040433-
dc.identifier.urihttp://hdl.handle.net/10029/621801-
dc.description.abstractThis 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.en
dc.language.isoenen
dc.rightsArchived with thanks to Nutrientsen
dc.titleIdentification of Requirements for Computer-Supported Matching of Food Consumption Data with Food Composition Data.en
dc.typeArticleen
dc.identifier.journalNutrients 2018; 10(4):E433en

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