Drewnowski A, Maillot M, Darmon N
Center for Public Health Nutrition and the Nutritional Sciences Program, Department of Epidemiology, School of Public Health and Community Medicine, University of Washington, 305 Raitt Hall, 353410, Washington, MI 98195-3410, USA.
Eur J Clin Nutr. 2009 Jul;63(7):898-904. doi: 10.1038/ejcn.2008.53. Epub 2008 Nov 5.
BACKGROUND/OBJECTIVES: Nutrient profiling of foods is defined as the science of ranking or classifying foods based on their nutrient content. Nutrient profiles can be calculated based on 100 g or 100 kcal of food or on standard serving sizes. The objective of this study was to compare the performance of nutrient profiles based on 100 g, 100 kcal and government-mandated serving sizes, and to identify the optimal base of calculation.
SUBJECTS/METHODS: Nutrient profiles tested were composed of positive subscores based on nutrients to encourage and negative subscores based on nutrients to limit. Alternative profiles, computed using different bases of calculation, were used to rank order 378 commonly consumed foods from a food frequency instrument. Profile performance was tested with respect to the foods' energy density.
Serving sizes, defined by the US Food and Drug Administration as reference amounts customarily consumed (RACC), were inversely linked to energy density of foods. Positive subscores based on 100 kcal were equivalent to those calculated using RACC values. Negative subscores performed better when based on 100 g as opposed to 100 kcal.
Models based on serving sizes and on 100 kcal were preferable for positive subscores and models based on 100 g of food were preferable for negative subscores. RACC-based profiles may represent an attractive option for the US consumer.
背景/目的:食物的营养成分分析被定义为基于食物营养成分对食物进行排名或分类的科学。营养成分分析可以基于每100克或每100千卡食物,或基于标准食用份量来计算。本研究的目的是比较基于100克、100千卡和政府规定的食用份量的营养成分分析的表现,并确定最佳计算基础。
对象/方法:所测试的营养成分分析由基于鼓励摄入营养素的正分项得分和基于限制摄入营养素的负分项得分组成。使用不同计算基础计算得出的替代营养成分分析,用于对食物频率问卷中的378种常见食用食物进行排名。根据食物的能量密度对营养成分分析的表现进行测试。
美国食品药品监督管理局定义为通常食用参考量(RACC)的食用份量与食物的能量密度呈负相关。基于100千卡的正分项得分与使用RACC值计算得出的得分相当。基于100克而非100千卡的负分项得分表现更好。
基于食用份量和100千卡的模型对于正分项得分更优,基于100克食物的模型对于负分项得分更优。基于RACC的营养成分分析可能对美国消费者具有吸引力。