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将Nova食品分类应用于使用歧视性成分的食品数据库:一个方法建议
Mariana Fagundes Grilo1,2, Beatriz Nunes1,3, Ana Clara Duran1,3,4
1Center for Food Studies and Research, University of Campinas, Campinas, Brazil.
识别超加工食品 (UPF) 对公众健康至关重要. 这项研究提出了基于成分的方法,使用化品添加剂和罕见的物质,以可靠地分类UPF用于研究和政策.
科学领域:
- 营养科学 营养科学
- 食品科学 食品科学 食品科学
- 公共卫生 公共卫生
背景情况:
- 越来越多的证据将超加工食品 (UPF) 与不良健康结果联系起来.
- 利益相关者对Nova食品分类系统的兴趣正在增加.
- 准确的UPF识别对于研究和监管工作至关重要.
研究的目的:
- 开发和验证可复制的方法来识别UPF.
- 测试拟议的UPF识别方法的敏感性和特异性.
- 使用2017年巴西食品标签数据库进行方法验证.
主要方法:
- 创建了五种情景,以基于罕见的物质和食品添加剂来识别UPF.
- 这些场景与Nova分类的产品名称和类别方法进行了比较.
- 用诊断测试,ROC曲线和灵敏度分析来评估性能.
主要成果:
- 在不同情景中,UPF的患病率从47%到72%不等,而使用经典方法则为70%.
- 方案3 (化品添加剂和罕见的物质) 确定了65%的UPF具有良好的灵敏度和特异性.
- 添加维生素和矿物质并没有显著改变UPF的识别.
结论:
- 基于成分的标准,特别是化品添加剂和罕见的物质,提供了一种可靠的UPF识别方法.
- 提出的方法表明可重复性,支持它们在研究中的应用.
- 这些发现有助于制定有关UPF的政策和监管行动.
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