In food and toxicology science, a huge amount of research and other data has been collected. To enable its full utilization, advanced statistical and computer methods are required. All data is related to food items, but additionally include different kinds of information. Nowadays the consumption of avocado has increased. To understand the full impact of this increased consumption on public health and the environment, different data related to avocado need to be considered. In this paper, we present an approach for representing foods in the form of vectors of continuous numbers (food embeddings) as an alternative solution to manual indexing. The utility of representing food data as a vector of continuous numbers was evaluated and demonstrated in four tasks: i) automated determination of different food groups, ii) automated detection of the food class for each food concept (raw, derivative or composite), iii) identification of most similar food concepts for a given food concept, and iv) qualitative evaluation by a food expert. The experimental results showed that these kind of vector representations outperform the traditional representational methods used for food data analysis, and thus they present a step forward to more advanced food data analysis used for discovering new knowledge.