Original Research Article
Estimating added sugars in US consumer packaged goods: An application to beverages in 2007–08

https://doi.org/10.1016/j.jfca.2015.04.004Get rights and content

Highlights

  • We developed a batch approach to estimate added sugars in consumer packaged goods.

  • We estimated added sugars content of 7021 beverages with any sugar.

  • Results show it is possible to derive valid estimates of added sugars in CPGs.

  • We can monitor the presence and amount of added sugars in the food supply/purchases.

Abstract

This study developed a method to estimate added sugar content in consumer packaged goods (CPG) that can keep pace with the dynamic food system. A team including registered dietitians, a food scientist and programmers developed a batch-mode ingredient matching and linear programming (LP) approach to estimate the amount of each ingredient needed in a given product to produce a nutrient profile similar to that reported on its nutrition facts label (NFL). Added sugar content was estimated for 7021 products available in 2007–08 that contain sugar from ten beverage categories. Of these, flavored waters had the lowest added sugar amounts (4.3 g/100 g), while sweetened dairy and dairy alternative beverages had the smallest percentage of added sugars (65.6% of total sugars; 33.8% of calories). Estimation validity was determined by comparing LP estimated values to NFL values, as well as in a small validation study. LP estimates appeared reasonable compared to NFL values for calories, carbohydrates and total sugars, and performed well in the validation test; however, further work is needed to obtain more definitive conclusions on the accuracy of added sugar estimates in CPGs. As nutrition labeling regulations evolve, this approach can be adapted to test for potential product-specific, category-level, and population-level implications.

Introduction

Added sugars, that is sugars included in foods during processing or preparation, comprise the majority of sugars in the typical American diet (Johnson et al., 2009a, Reedy et al., 2010, U.S. Department of Agriculture, 2010, U.S. Department of Agriculture and U.S. Department of Health and Human Services, 2010). While chemically indistinguishable from naturally occurring sugars (e.g. fructose in fruit or lactose in milk and milk products), added sugars have become an ingredient of public health concern. Foods containing high amounts of added sugars are often sources of energy with very few nutrients (e.g. sugar-sweetened beverages, grain-based desserts, dairy desserts, and candy) (Fitch and Keim, 2012, Ng et al., 2012). Overconsumption of these foods may lead to excess energy intake and poor diet quality (U.S. Department of Agriculture, 2010). Moreover, when considered with solid fats and excess energy intake, added sugars can potentially lead to adverse health effects, including obesity, type-2 diabetes or pre-diabetes, inflammation, and cardiovascular disease (Johnson et al., 2009b, Malik et al., 2010, Morenga et al., 2014, Te Morenga et al., 2013, Welsh et al., 2011).

Appropriately, the Dietary Guidelines for Americans (DGA) include recommendations to reduce the intake of calories from added sugars (U.S. Department of Agriculture, 2010, U.S. Department of Agriculture and U.S. Department of Health and Human Services, 2010). However, it is difficult for consumers to adhere to these recommendations, as the amount of added sugars cannot be identified from the nutrition facts label (NFL) on consumer packaged goods (CPGs). While there is growing concern over the use of added sugars in the US food system, monitoring their presence in products and their consumption remains challenging for the following reasons: (a) no laboratory method can analyze for added sugars (not chemically distinguishable from naturally occurring sugars); (b) current nutrition labeling regulations do not require that added sugars be reported separately from total sugars; (c) added sugar amounts must be either estimated or supplied by food companies; (d) estimations must be able to keep up with new and reformulated products in order to capture changes in the food system.

In February 2014, the Food and Drug Administration (FDA) released a proposed update to the nutrition labeling regulations that includes a mandatory disclosure of added sugar content to be listed on the NFL (Food and Drug Administration, 2014b). In addition, it sought to define added sugars as: “sugars that are either added during the processing of foods, or are packaged as such, and include sugars (free, mono- and disaccharides), syrups, naturally occurring sugars that are isolated from a whole food and concentrated so that sugar is the primary component (e.g. fruit juice concentrates), and other caloric sweeteners… Sugar alcohols are not considered to be added sugars…” (Food and Drug Administration, 2014b).

The proposed revision to the nutrition labels is not finalized, so it is unclear what changes will be made and when any revisions to the nutrition labels would come into effect.

We designed a batch-mode approach to estimate added sugar content in commercially formulated CPG products using linear programming (LP), with an application to CPG beverages available in 2007–08. This followed work undertaken by the University of Minnesota Nutrition Coordinating Center and the United States Department of Agriculture (USDA), both of which have used LP approaches to estimate missing nutrient values (Schakel et al., 1997, Westrich et al., 1994, Westrich et al., 1998) for food composition tables. We built off these past LP methods, and estimated added sugars content in products using a systematic batch-mode manner that allows for larger-scale applications. Because there is no cost- or time-efficient gold-standard in which to compare our results across thousands of products, we assessed the validity of our estimated nutrients in two ways: (a) by comparing LP estimated nutrient values to known values from the NFL and (b) by conducting a small validation study based on 15 known formulations.

Section snippets

Overview

The process for estimating added sugar values requires three pieces of information: the nutrition facts label (NFL), the ingredient list, and nutrient composition for each ingredient. We utilized a linear programming (LP) approach to estimate the amount of each ingredient needed in a given product to produce a nutrient profile as close as possible to that reported on its NFL. To help develop accurate estimations, constraints were applied to ingredients using information gathered from FDA

Results

There were 7021 products from ten beverage categories in the product database from 2007 to 2008 that were candidates for this analysis (total sugar values >0 g per 100 g): caloric sodas and energy drinks (n = 1711), sports drinks (n = 290), flavored waters-carbonated and still (n = 331), fruit and vegetable juice drinks (n = 1649), fruit flavored drinks (n = 183), sweetened dairy and dairy alternatives (n = 860), RTD coffees and teas (n = 742), concentrated fruit drinks (n = 130), powdered beverage mixes (n = 942)

Discussion

This batch-mode approach to estimate added sugars content in consumer packaged goods (CPG) products delivered results that appear reasonable and robust. When applied to 7021 qualifying CPG beverages products in the 2007–08 product database, 6729 (95.8%) contained added sugars. Added sugar accounted for 65.6% to 100% of total sugars across the ten beverage categories studied. Additionally, the LP estimates for total sugar, carbohydrates and calories compared well to the NFL values across these

Conclusions

In conclusion, our findings suggest that using linear programming to estimate added sugar contents of products in a systematic batch-mode manner is possible and has a number of advantages. This approach allows for flexibility in setting and testing the parameters applied across multiple products at once. For example, should regulations regarding the definition of added sugars change, it would be possible to adapt the identification of added sugar ingredients and allow a re-estimation of added

Conflict of interest

Westrich has in the past and may in the future provide consulting services related to the use and development of linear programming approaches for estimating nutrient values in food composition databases to the US Department of Agriculture and other organizations. Otherwise none of the remaining authors have any conflicts of interest to declare.

Acknowledgments

We thank the National Institutes of Health R01DK098072 and CPC (R24 HD050924) for financial support. We also wish to thank Barry M. Popkin for his help in conceptualizing this project and comments on the paper, and Meghan Slining, Jessica Davis, Bridget Hollingsworth, Julie Wandell and Jim Terry for assistance in this effort. We dedicate this work to the memory of Dan Blanchette. Other than noted above, none of the authors have conflict of interests of any type with respect to this manuscript.

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