StandFood: Standardization of Foods Using a Semi-Automatic System for Classifying and Describing Foods According to FoodEx2.
StandFood: Standardization of Foods Using a Semi-Automatic System for Classifying and Describing Foods According to FoodEx2.
复制标题
DOI：
10.3390/nu9060542
复制
发表时间：
2017-05-26
期刊：
影响因子：
5.9
通讯作者：
Koroušić Seljak B
中科院分区：
文献类型：
--
作者：
Eftimov T;Korošec P;Koroušić Seljak B
欧洲食品安全局已经开发了一个标准化的食品分类和描述系统，称为FoodEx2。它使用facet从不同的角度描述食物的属性和方面，从而更容易比较来自不同来源的食物消费数据，并进行更详细的数据分析。然而，需要链接的食品成分数据和食品消费数据在FoodEx2中都是缺乏的，因为分类和描述的过程必须手动执行，这是一个费力的过程，需要对系统有很好的了解，也需要对食品（成分、加工、营销等）有很好的了解。本文介绍了一种基于fooddex2的半自动食品分类描述系统，该系统由三部分组成。第一种方法涉及机器学习方法，并将食品分为四类，其中两种用于单一食品：生的(r)和衍生物(d)，两种用于复合食品：简单的(s)和聚合的(c)。第二种方法使用自然语言处理方法和概率论来描述食物。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手工编码的食品（来自斯洛文尼亚）对系统进行了测试。新的半自动系统在分类部分的准确率为89%，描述部分的准确率为79%，整个系统的总体准确率为79%。
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.