INRA, Unité Met@risk, 16 rue Claude Bernard, 75231 Paris Cedex 05, France.
Food Chem Toxicol. 2013 Feb;52:180-7. doi: 10.1016/j.fct.2012.11.005. Epub 2012 Nov 23.
Dietary risk assessment is a major public health concern, positioned in the context of establishing overall food safety policy. It requires some understanding of population food choices although geographical location and social-cultural environment are variable. Several years ago, a cluster analysis based on FAO consumption data, ranging from 1990 to 1994, was at the origin of the 13, so called, GEMS/Food cluster diets. This analysis required the initial identification of 19 food markers based on geographical and cultural differences. This paper proposes a new modelling of FAO food consumption database in order to define new cluster diets based on updated consumption data from 2002 to 2007 and better adapted statistical methods. Two statistical methods were combined to extract, consumption systems that generate a substructure from the initial food consumption database and then by deriving a clustering of countries according to their consumption system profiles. The clustering resulted in 17 cluster diets composed of 2 up to 30 countries. The few discrepancies between these new clusters and former ones may be due to more recent data, and to the fact that the new approach is based on another mathematical modelling which does not require any initial identification of food markers.
饮食风险评估是公共卫生关注的主要问题,是制定总体食品安全政策的背景。尽管地理位置和社会文化环境存在差异,但需要对人群的食物选择有一些了解。几年前,基于粮农组织从 1990 年至 1994 年的消费数据的聚类分析,最初确定了 19 种食物标记,这些标记基于地理和文化差异。本文提出了一种新的粮农组织食物消费数据库建模方法,以便根据 2002 年至 2007 年更新的消费数据和更好的统计方法,定义新的聚类饮食。两种统计方法相结合,从初始食物消费数据库中提取消费系统,然后根据消费系统概况对国家进行聚类。聚类结果产生了 17 种聚类饮食,其中包含 2 个至 30 个国家。这些新聚类与前聚类之间的差异可能是由于更近期的数据以及新方法基于不需要任何初始食物标记识别的另一种数学建模。