Computer Systems Department, Jožef Stefan Institute, 1000, Ljubljana, Slovenia.
Computer Systems Department, Jožef Stefan Institute, 1000, Ljubljana, Slovenia; Jožef Stefan International Postgraduate School, 1000, Ljubljana, Slovenia.
Food Chem Toxicol. 2020 Apr;138:111169. doi: 10.1016/j.fct.2020.111169. Epub 2020 Feb 20.
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.
在食品和毒理学科学领域，已经收集了大量的研究和其他数据。为了充分利用这些数据，需要先进的统计和计算机方法。所有的数据都与食品项目有关，但除此之外还包括各种不同的信息。如今，鳄梨的消费有所增加。为了全面了解这种消费增长对公众健康和环境的影响，需要考虑与鳄梨相关的不同数据。在本文中，我们提出了一种以连续数字向量（食品嵌入）的形式表示食品的方法，作为手动索引的替代解决方案。通过四个任务评估了将食品数据表示为连续数字向量的效用，并进行了演示：i）自动确定不同的食品组，ii）自动检测每个食品概念的食品类别（原始、衍生或复合），iii）为给定的食品概念识别最相似的食品概念，以及 iv）食品专家进行定性评估。实验结果表明，这些类型的向量表示优于用于食品数据分析的传统表示方法，因此它们是朝着用于发现新知识的更先进的食品数据分析迈进的一步。
