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The food sector is driven by a large number of actors, including primary producers, manufacturers, logistics providers, retailers, and consumers. At each phase of the food value chain, a significant amount of data is generated that provides important information to the agents involved in processing and flow of food products from farm to fork. Proper handling of food data has a crucial role in providing safe, quality and affordable products to the increasing world population. The independent production of food data, without following any specific guidelines and procedures, often results in inconsistent and incomparable datasets that cannot be directly utilised by multiple users. Data harmonisation means reconciling various types, levels and sources of data in formats that are compatible and comparable, and thus useful for better decision making. In the food sector, one way of performing data harmonisation is to represent food data according to reliable classification and description systems. Another approach towards harmonisation is to match various food concepts to the existing and widely used ontologies. Furthermore, harmonisation is facilitated by following specific guidelines and procedures during data collection processes. This study explores some of the most important tools, frameworks and methodologies for data harmonisation in the food sector.
Zeb et al. (Wed,) studied this question.