Eftimov Tome, Korošec Peter, Koroušić Seljak Barbara
Computer Systems Department, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia.
Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia.
Nutrients. 2017 May 26;9(6):542. doi: 10.3390/nu9060542.
The European Food Safety Authority has developed a standardized food classification and description system called FoodEx2. It uses facets to describe food properties and aspects from various perspectives, making it easier to compare food consumption data from different sources and perform more detailed data analyses. However, both food composition data and food consumption data, which need to be linked, are lacking in FoodEx2 because the process of classification and description has to be manually performed-a process that is laborious and requires good knowledge of the system and also good knowledge of food (composition, processing, marketing, etc.). In this paper, we introduce a semi-automatic system for classifying and describing foods according to FoodEx2, which consists of three parts. The first involves a machine learning approach and classifies foods into four FoodEx2 categories, with two for single foods: raw (r) and derivatives (d), and two for composite foods: simple (s) and aggregated (c). The second uses a natural language processing approach and probability theory to describe foods. The third combines the result from the first and the second part by defining post-processing rules in order to improve the result for the classification part. We tested the system using a set of food items (from Slovenia) manually-coded according to FoodEx2. The new semi-automatic system obtained an accuracy of 89% for the classification part and 79% for the description part, or an overall result of 79% for the whole system.
欧洲食品安全局开发了一种名为FoodEx2的标准化食品分类和描述系统。它使用多个方面从不同角度描述食品特性和特征，使得比较来自不同来源的食品消费数据以及进行更详细的数据分析变得更加容易。然而，FoodEx2缺乏需要关联的食品成分数据和食品消费数据，因为分类和描述过程必须手动执行——这一过程既费力又需要对该系统有充分了解，还需要对食品（成分、加工、营销等）有充分了解。在本文中，我们介绍了一种根据FoodEx2对食品进行分类和描述的半自动系统，该系统由三个部分组成。第一部分涉及一种机器学习方法，将食品分为四个FoodEx2类别，其中两个用于单一食品：生食（r）和衍生物（d），另外两个用于复合食品：简单（s）和聚合（c）。第二部分使用自然语言处理方法和概率论来描述食品。第三部分通过定义后处理规则将第一部分和第二部分的结果结合起来，以改进分类部分的结果。我们使用一组根据FoodEx2手动编码的食品（来自斯洛文尼亚）对该系统进行了测试。新的半自动系统在分类部分的准确率为89%，在描述部分的准确率为79%，整个系统的总体结果为79%。
