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食品加工分类系统的稳健性。

Robustness of Food Processing Classification Systems.

机构信息

Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA.

Tufts University School of Medicine, Tufts University, Boston, MA 02111, USA.

出版信息

Nutrients. 2019 Jun 14;11(6):1344. doi: 10.3390/nu11061344.

Abstract

Discrepancies exist among food processing classification systems and in the relationship between processed food intake and dietary quality of children. This study compared inter-rater reliability, food processing category, and the relationship between processing category and nutrient concentration among three systems (Nova, International Food Information Council (IFIC), and University of North Carolina at Chapel Hill (UNC)). Processing categories for the top 100 most commonly consumed foods children consume (NHANES 2013-2014) were independently coded and compared using Spearman's rank correlation coefficient. Relative ability of nutrient concentration to predict processing category was investigated using linear discriminant analysis and multinomial logistic regression and compared between systems using Cohen's kappa coefficient. UNC had the highest inter-rater reliability ( = 0.97), followed by IFIC ( = 0.78) and Nova ( = 0.76). UNC and Nova had the highest agreement (80%). Lower potassium was predictive of IFIC's classification of foods as moderately compared to minimally processed ( = 0.01); lower vitamin D was predictive of UNC's classification of foods as highly compared to minimally processed ( = 0.04). Sodium and added sugars were predictive of all systems' classification of highly compared to minimally processed foods ( < 0.05). Current classification systems may not sufficiently identify foods with high nutrient quality commonly consumed by children in the U.S.

摘要

食品加工分类系统之间存在差异,加工食品的摄入量与儿童饮食质量之间也存在差异。本研究比较了三种系统(Nova、国际食品信息理事会(IFIC)和北卡罗来纳大学教堂山分校(UNC))之间的评分者间可靠性、加工类别以及加工类别与营养素浓度之间的关系。使用 Spearman 秩相关系数对儿童消费的前 100 种最常见食品(NHANES 2013-2014)的加工类别进行了独立编码和比较。使用线性判别分析和多项逻辑回归研究了营养素浓度对加工类别的相对预测能力,并使用 Cohen's kappa 系数比较了系统之间的差异。UNC 的评分者间可靠性最高( = 0.97),其次是 IFIC( = 0.78)和 Nova( = 0.76)。UNC 和 Nova 的一致性最高(80%)。与 IFIC 的中加工分类相比,较低的钾更能预测食品被归类为低加工( = 0.01);与 IFIC 的低加工分类相比,较低的维生素 D 更能预测 UNC 的高加工分类( = 0.04)。钠和添加糖可预测所有系统对高加工与低加工食品的分类( < 0.05)。目前的分类系统可能无法充分识别出美国儿童普遍食用的高营养食品。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c4bb/6627649/e0dd3def5ac7/nutrients-11-01344-g001.jpg

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