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

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.

Keywords: food composition; food consumption; food matching; optimization algorithm.

Figure 1

Example of FoodEx2 description and classification.* H—‘hierarchy term’; ** C—‘core term’; *** E—‘extended term’; ⁺ g—‘group’; ⁺⁺ r—‘raw’; ⁺⁺⁺ d—‘derivative’.

Figure 2

Example of LanguaL description and classification.

Figure 3

GloboDiet to LanguaL food matches.

Figure 4

FoodEx2 to LanguaL food matches.

Figure 5

Estimated average number of suggested matches per query by quality level (level 0 is best quality) as indicated by the matching tool. Matches of level higher than 2 are not presented in the graph.

Figure 6

Percentage of poor, sufficient and good quality matches by country as selected and judged by the human experts. * Good—matches in all parameters; ** sufficient—matches in the name and group, but not in all descriptors; *** poor matches—all other matches.

Figure 7

Distribution of quality levels such as indicated by the matching tool (level 0 = best quality) for foods with good, sufficient and poor match as judged by the human expert for three countries. For UK only level 0 matches were proposed so these are not included in the figure.

Figure 8

Percentage of foods from the food consumption survey that were assigned the same match from the food composition database in the testing exercises and in the previous manual match.

Figure 9

Percentage accuracy compared to the expected match for an experienced and an inexperienced human expert (results for UK).