Fagundes Grilo Mariana, Nunes Beatriz, Duran Ana Clara, Zancheta Ricardo Camila, Baraldi Larissa Galastri, Martinez Steele Euridice, Borges Camila Aparecida
Center for Food Studies and Research, University of Campinas, Campinas, Brazil.
Department of Exercise and Nutrition Sciences, The George Washington University, Washington, DC, United States.
Front Public Health. 2025 Jul 1;13:1575136. doi: 10.3389/fpubh.2025.1575136. eCollection 2025.
Growing interest in the Nova food classification system surged among various stakeholders, driven primarily by compelling evidence linking the consumption of ultra-processed foods (UPFs) to negative health outcomes. This growing interest underscores the potential value of identifying clear markers to classify UPFs, particularly to support research and regulatory efforts.
To propose replicable methods to identify UPFs, by testing the sensitivity and specificity of these methods using a large sample of packaged foods from the 2017 Brazilian Food Labels Database.
We created five scenarios to identify UPFs using substances of rare culinary use and food additives typically found in UPFs and compared them with the Nova food classification process based on the product name and food categories, considered the classic method to identify UPFs. We estimated the proportion of foods and beverages identified as UPFs using the different scenarios based on the presence of discriminative ingredients. We used a diagnostic test and a receiver operating characteristic (ROC) to understand which of the five scenarios performed better compared to the classic method to identify UPFs. Finally, we conducted a sensitivity analysis to test the role of vitamins and minerals in identifying UPFs.
We found variations in UPFs prevalence from 47 to 72% across the five scenarios, compared to 70% using the classic method to identify UPFs in Brazilian packaged foods. The scenario using food additives of a sole cosmetic function and substances of rare culinary use (scenario 3) identified a 65% UPF, while maintaining reasonable sensitivity and specificity, and the best-performing ROC curve. There was no significant difference in identifying UPFs when comparing the addition of vitamins and minerals to the food additives with sole cosmetic function.
This study shows that using ingredient-based criteria, specifically cosmetic additives and substances of rare culinary use, can reliably identify UPFs, offering reproducibility, and supporting its use in research and policy applications.
超加工食品（UPF）的消费与负面健康结果之间存在令人信服的证据，这主要推动了各利益相关方对诺瓦食品分类系统的兴趣日益浓厚。这种兴趣的增加凸显了识别明确的超加工食品分类标志的潜在价值，特别是为了支持研究和监管工作。
通过使用2017年巴西食品标签数据库中的大量包装食品样本测试这些方法的敏感性和特异性，提出可复制的超加工食品识别方法。
我们创建了五种情景，利用超加工食品中常见的罕见烹饪用途物质和食品添加剂来识别超加工食品，并将其与基于产品名称和食品类别（这被认为是识别超加工食品的经典方法）的诺瓦食品分类过程进行比较。我们根据鉴别成分的存在情况，估计了使用不同情景识别为超加工食品的食品和饮料的比例。我们使用诊断测试和受试者工作特征（ROC）曲线来了解与识别超加工食品的经典方法相比，这五种情景中哪一种表现更好。最后，我们进行了敏感性分析，以测试维生素和矿物质在识别超加工食品中的作用。
我们发现，在这五种情景中，超加工食品的患病率在47%至72%之间变化，而使用经典方法识别巴西包装食品中的超加工食品的患病率为70%。使用仅具有美容功能的食品添加剂和罕见烹饪用途物质的情景（情景3）识别出65%的超加工食品，同时保持了合理的敏感性和特异性，以及表现最佳的ROC曲线。将维生素和矿物质添加到仅具有美容功能的食品添加剂中时，在识别超加工食品方面没有显著差异。
本研究表明，使用基于成分的标准，特别是美容添加剂和罕见烹饪用途物质，可以可靠地识别超加工食品，具有可重复性，并支持其在研究和政策应用中的使用。
