A nutrient density standard for vegetables and fruits: Nutrients per calorie and nutrients per unit cost
ABSTRACT The Dietary Guidelines for Americans 2005 recommended that consumers give priority to nutrient-dense foods, those that contain substantial amounts of key nutrients in relation to the dietary energy they provide. This study developed a scoring system to estimate the nutritional adequacy of vegetables and fruits, on a per weight, per calorie, and per unit cost basis.
We used a French national food composition database for 637 foods, including 129 vegetables and fruits. Mean national retail prices were obtained for each food.
The nutrient adequacy score was defined as the mean of percent daily values for 16 nutrients, based on 100 g of food. The nutrient density score and the nutrient-to-price ratio were the mean of percent daily values for 16 nutrients, expressed per 100 kcal and per 1 euro of food, respectively. Relationships between energy density of vegetables and fruits, nutrient adequacy score, nutrient density score, and nutrient-to-price ratio were tested using linear regression.
Energy density and nutrient density score were negatively correlated, confirming the widely accepted notion that energy-dense foods tend to be nutrient-poor. As expected, fruits and vegetables had the highest nutrient density score because they were nutrient-rich in relation to their low energy content. They also had a relatively high nutrient-to-price ratio, showing that they provided nutrients at a reasonable cost when compared with other foods.
Foods ranked differently when nutritional adequacy was calculated per weight (nutrient adequacy score), per calorie (nutrient density score), or per unit cost (nutrient-to-price ratio). The present results showed that although fruits and vegetables are an expensive source of dietary energy, they provide key nutrients at a reasonable cost.
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ABSTRACT: This paper analyzes the impact of deep-fat frying options on the nutritional profile of fried plantain-based products by using the SAIN, LIM system. 23 nutrients and 4 disqualifying compound including acrylamide were used to calculate SAIN and LIM scores. Experimental data for heat, mass transfers and reactions modeling were from previous studies. The options of the frying were oil type, micronutrient-enrichment or not, thickness of final product, frying temperature and number of baths. Frying turned plantain from neutral food to positive or negative categories as a function of process variables. The most impacting parameters were the oil type (SAIN and LIM scores of crisps varied by 6.5 and 7.5-fold respectively) and product thickness assimilated with the final oil content (LIM scores increased by 5-fold from 6% to 33% final fat content). Acrylamide increased LIM scores by 2 to 11-fold in crisps while the use of oils enriched with beta-carotene impacted the SAIN scores by 10-20%. The best trade off process time/nutritional score was obtained for thick products (fat content <25%) fried in sunflower oil in non-isothermal conditions. The use of geometrical mean to calculate SAIN scores enabled a more sensitive evaluation of the effect of number of bath.Journal of Food Engineering 03/2015; 149. DOI:10.1016/j.jfoodeng.2014.10.017 · 2.58 Impact Factor
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ABSTRACT: Socioeconomic disparities in diet quality are well established. This study tested the hypothesis that such disparities are mediated, in part, by diet cost. The Seattle Obesity Study (S.O.S) was a cross-sectional study based on a representative sample of 1266 adults of King County, WA, conducted in 2008-09. Demographic and socioeconomic variables were obtained through telephone survey. Income and education were used as indicators of socioeconomic position. Dietary intake data were obtained using a food frequency questionnaire (FFQ). Diet cost was calculated based on retail prices for FFQ component foods. Energy density (KJ/g) and mean adequacy ratio (MAR) were used as two indices of overall diet quality. Higher income and education were each associated with lower energy density and higher MAR scores, adjusting for covariates. Higher income and education were also associated with higher energy adjusted diet cost. Higher quality diets were in turn associated with higher diet costs. All these associations were significant (P<0.0001). In formal mediation analyses, diet cost significantly mediated the pathway between income and diet quality measures, adjusting for covariates (P<0.05 each). Further, income-diet cost-diet quality pathway was found to be moderated by education level. The social gradient in diet quality may be explained by diet cost. Strategies to improve diet quality among lower socioeconomic strata may need to take food prices and diet cost along with nutrition education into account.European journal of clinical nutrition 05/2011; 65(9):1059-66. DOI:10.1038/ejcn.2011.72 · 2.95 Impact Factor
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ABSTRACT: Nutrient profiling of foods is defined as the science of classifying foods based on their nutrient content. Food rankings generated by nutrient profile models need to be tested against objective reality as opposed to public opinion. To test the performance of selected nutrient profile models in relation to the foods' energy density (kcal g(-1)) and energy cost (Dollar per 1000 kcal). Analyses were based on 378 component foods of a food frequency instrument. The models tested were the French nutrient adequacy models NAS23 and NAS16 and nutrient density models NDS23 and NDS16; and a family of nutrient-rich models (NR(n), where n=5-7; 10-12, and 15). Also tested were LIM scores and a modified British Food Standards Agency model WXYfm. Profiles were calculated based on 100 g, 100 kcal and on Reference Amounts Customarily Consumed. Food rankings generated by different models were correlated with each other and with the foods' energy density and energy cost. Nutrient profile models based on protein, fiber, vitamins and minerals showed an inverse correlation with energy density that diminished as more micronutrients were introduced into the model. Models based on fat, sugar and sodium were highly correlated with energy density. Foods classified as healthier were generally associated with higher energy costs. Not all models accurately reflected the foods' content of nutrients known to be beneficial to health. High correlations with energy density meant that some models classified foods based on their energy density as opposed to nutrient content.European journal of clinical nutrition 03/2008; 63(5):674-83. DOI:10.1038/ejcn.2008.16 · 2.95 Impact Factor