Introduction The growing market for whole grain products worldwide is accompanied with lively discussions about the definition of whole grain. Which grains can be included and which processing treatments are allowed? Furthermore there is a wish for having analytical methods for determination of the level of whole grain in products. This paper is aiming at providing some new views and insights for these debates.
Cereal food processing technologies play an important role in transforming raw cereals and grains into usable ingredients (e.g., flour and rice kernels) and subsequently into food products (e.g., pasta, bread, and breakfast cereal). New developments in processing are driven by many factors, including the need to 1) reduce processing costs through economies of scale, improved productivity, and automation; 2) cater to changes in the marketplace, including increased consumer demand for healthier food products with reduced calories or sodium; 3) conserve energy and resources by way of water conservation and waste management; and 4) eliminate potential microbial contamination in food products. The cereal food industry has made significant strides in the last decade in addressing some of these challenges. In the flour milling industry, developments include reduction of milling roll surface area per pound of wheat processed and better automation controls to improve capacity and efficiency.
The majority of adult diets in the United States do not meet recommendations for consumption of whole grains (3), putting these individuals at increased risk for diet-related chronic diseases (10). Poor diet quality, inadequate consumption of whole grains, and chronic diseases are particularly prevalent in the impoverished rural South (1,7,9,12). Hence, dietary interventions designed to address nutrition inadequacies and thereby improve diet quality could lower chronic disease prevalence in this region of the country. Assessing a population’s diet quality is a necessary step for determining nutrition inadequacies and subsequently designing effective dietary interventions that address these problems. The Healthy Eating Index–2005 (HEI-2005) is a tool that assesses diet quality in terms of adherence to the 2005 Dietary Guidelines for Americans (DGA). Whole grains are among the 2005 and 2010 DGA key recommendations for food groups to encourage (10,11). However, little research has been conducted to determine the potential impact of whole grain for refined grain substitutions on diet quality. Hence, the objective of this study was to use simulation modeling to determine the effects of substituting familiar, more healthful whole grain foods for less healthy, refined grain foods on diet quality and total energy intake in lower Mississippi Delta (LMD) adults. Analyses were performed using data from the Foods of Our Delta Study (FOODS), a cross-sectional telephone survey of residents in a 36-county LMD region that was conducted from January to June 2000 (2). Dietary intake data were collected using the U.S. Department of Agriculture’s (USDA) 24-hour dietary recall multiple pass methodology with the assistance of a foods measurement guide that had been mailed to study participants. Dietary intake data were collected for 1,751 adults (18 years of age and older) using a single 24-hour recall. However, only plausible (500 ≤ intake kcal ≤ 6,000) 24-hour recalls were used for this study (13). The HEI-2005, a scoring method designed to measure adherence to the 2005 DGA (5), was used to measure diet quality. Briefly, the HEI-2005 is composed of 12 components corresponding to total fruit, whole fruit, total vegetables, dark green and orange vegetables and legumes, total grains, whole grains, milk, meat and beans, oils, saturated fat, sodium, and kcal from solid fats, alcoholic beverages, and added sugars (SoFAAS). The total score, calculated as the sum of the component scores, has a maximum value of 100. For each component, higher scores reflect better adherence to DGA recommendations corresponding to that component. All foods selected as substitutions were deemed familiar foods based on their inclusion in the FOODS data set. White breads, rolls, biscuits, and spaghetti were replaced with their whole wheat counterparts, and white rice was replaced with brown rice. To simulate the effects of substituting 25, 50, and 100% of targeted foods with their replacements on HEI-2005 total and component scores, the targeted items’ nutrient profiles were reduced by the respective amounts for the analyses and the replacement items’ nutrient profiles were inserted at the corresponding level. The nutrient profiles for the replacement foods were extracted from the FOODS data set. All statistical analyses were performed using SAS software (version 9.2, SAS Institute) and SUDAAN software (version 10.0.1, Research Triangle Institute). SAS survey and SUDAAN procedures were used to compute frequencies, means, and their associated 95% confidence intervals. These procedures are tailored to account for the complex sampling design used in FOODS. Hence, the results are weighted and should be considered representative of the LMD adult population. The population ratio method was used to compute mean HEI-2005 scores and corresponding 95% confidence intervals using jackknife variances for the overall population. It is the least biased way to estimate a mean HEI-2005 score for a population (4). Sixty-two of the 1,751 LMD respondents were excluded due to implausible dietary records, resulting in a total sample size of 1,689 adults. The sample was composed of 48% males, 43% African Americans, 21% < 30 years of age, and 23% ≥ 60 years of age. Approximately one-fourth (23%) of the sample’s household income was under $15,000 per year and 23% of the sample had less than a high school education. Almost one-fourth (24%) of the respondents were current smokers and 18% were former smokers. Based on self-reported heights and weights, 67% of the sample was either overweight (25.0 ≤ BMI ≤ 29.9) or obese (BMI ≥ 40.0). The mean daily energy intake for the LMD adult respondents was 2,010 kcal. 1 Corresponding author. USDA Agricultural Research Service. E-mail: jessica.thomson@ars.usda.gov. 2 Department of Medicine, University of Illinois at Chicago. Affiliated with USDA Agricultural Research Service at the time this work was completed.
