Objective 1. Determine the impact of industrial packaging methods (canning, freezing and drying) on the nutrients and bioactive compounds in fresh fruits and vegetables. Objective 2. Validate a software program based on mathematical optimization techniques for estimating nutrient contents of commercial multi-ingredient foods. Objective 3. Determine the impact of dietary fiber methodology on fiber composition and intake estimates.
Objective 1. Industrial processing alters nutrients/bioactive compounds in fruits and vegetables. A 2-step, 2-year study for sample collection will be conducted. Consulting USDA plant scientists/other collaborators, multiple same-cultivar ripe samples will be collected simultaneously from one. Samples will be analyzed for vitamins, fiber, minerals, polyphenols, and metabolomics (baseline). Portions will be transported and stored to emulate typical commercial storage conditions; nutrients/polyphenols will be conducted in stored raw samples every 3 days until they decay. Shipping practices for samples will be simulated through collaborations with processing plants near harvest locations, emulating agricultural and industrial practices. Analyses at 0, 14, 35, 70, 120, 180 and 360 days post processing, and analysis in validated commercial laboratories, using AOAC methods, polyphenolic compounds analysis by ARS/academic collaborators will address the impact of processing. Objective 2. Linear programming software for estimating missing nutrient values in commercially processed foods (using label values, ingredient lists) requires improved automation and analytical ingredient data. Food types (e.g., baked products) and nutrients will be identified, program functionality improvements completed, and tests, where ingredient proportions and nutrient values are known, will allow determination of estimation accuracy. The Virginia Tech (VT) Food Analysis Laboratory Control Center will prepare foods and QC materials for analysis; food manufacturers will be consulted on ingredient proportions, and an equivalence study of the estimates will be conducted to determine classes of nutrients and food types where the estimated and analytical values are similar, i.e. within ± 20%. Program validation will ensue to assess which nutrients to include. Objective 3. The McCleary method (MCF) is a more complete determination of dietary fiber (DF) content in foods compared to the enzymatic-gravimetric (EGF) method, enabling better intake estimates. Select high-fiber foods and frequently consumed, fiber-containing foods will be analyzed by EGF (985.29) and MCF (2011.25) methods at a USDA-qualified commercial analytical lab. Foods with isolated or synthetic non-digestible carbohydrates may be analyzed. EGF (AOAC 985.29; total DF) and EGF (AOAC 991.43; soluble and insoluble DF), and MCF (AOAC 2009.01 and 2011.25 (fractions) will be studied and summed. Sumswill determine the food types where the fiber method used does not make a difference for measuring total DF. This allowsbetter understanding of the effect of fiber methodologies for selecting the appropriate analytical method for specific foods.
Objective 1. Determine the impact of industrial packaging methods (canning, freezing and drying) on the nutrients and bioactive compounds in fresh fruits and vegetables. One local corn grower was identified as a main collaborator, and two others as backups. Selected yellow sweet corn samples from different spots of the same field, at different harvest days within the harvest window (4-5 days), at different farms in the same area, and from different states (Maryland and Virginia) were collected and their nutrients (carbohydrates, B vitamins, minerals and carotenoids) were analyzed in contract labs for a pilot study. Yellow corn samples with and without cooking (steaming) were also analyzed in contract labs to evaluate the cooking effects on carbohydrates. The data suggested that the variability of different nutrients in the samples picked at 9 different spots of the same field were different, CV (%) ranging from potassium (3.7%) to calcium (32.5%). Harvest day significantly affects the carbohydrate composition, yellow sweet corn harvested at later day of the harvest window has much higher sucrose and lower glucose and fructose compared to those harvested at early day. Steaming significantly increased sucrose, but decreased glucose, fructose and maltose. Yellow sweet corn samples obtained from four different locations (two different farms in Maryland, and two samples from Virginia) differed from each other greatly in terms of their nutrient contents, likely caused by different growing conditions and/or cultivars. The data revealed that the variabilities of carbohydrates and carotenoids (CV%, 30% to over 100% for most of them) were greater than that of B vitamins and minerals (CV%, 10-20% for most of them, up to 42%). The data obtained from the pilot study will help us better design future experiments and better understand the factors influencing nutrient composition in yellow sweet corn. Objective 2. Validate a software program based on mathematical optimization techniques for estimating nutrient contents of multi-ingredient foods. Ingredients were parsed from ingredient statements of top selling commercial baked products (~5,500), using a custom program. Ingredient statements were very inconsistent and several major challenges were identified. The identified parsed ingredients were reviewed to identify equivalent ingredients, such as synonyms, spelling and usage variants, common names, possible errors etc. The parsed list of ingredients was used to develop a thesaurus of ~ 4,000 ingredients, using standardized formats and procedures. Manuscripts on the development and application of an Ingredient dataset for baked products sold in the U.S., including the challenges, are under development. The parsed ingredients were also used for a project, “Application of machine learning for predicting label nutrients using USDA Global Branded Food Products Database”; a manuscript is being is being drafted. Improvement of the software program for estimating nutrient contents of multi-ingredient foods is under consideration, but not a high priority. Objective 3. Determine the impact of dietary fiber methodology on fiber composition and intake estimates. High carbohydrate foods were identified to support research on the impact of dietary fiber methods and distribution of carbohydrate fractions. Garlic, onions, apples, flours (whole and all purpose wheat) rice, corn, and slightly, ripe, and overripe bananas were analyzed for sugars, total dietary fiber, McCleary dietary fiber, starch, and organic acids. Bananas were sampled and analyzed to investigate how ripeness stage affects changes in carbohydrate composition. Objective 3 was expanded to include measurement of dietary fiber (enzymatic gravimetric AOAC 991.43), McCleary fiber (AOAC 2009.01, 2011.25), starch (digestible, residual, and retrograde, after cooking/cooling/reheating), and oligosaccharides in highly consumed, high carbohydrate commodity foods. All data were or will be included in FDC Foundation Foods. Alpha- and beta-glyosidic linkages are also being studied by collaborators at the University of California, Davis. Sampling frames for potatoes (multiple cultivars (fresh and cooked), amylose and amylopectin), Rice (dry and cooked of different types), additional pulses, legumes, and corn (fresh and cooked) are being planned. The effect of cooking on flours used in multi-ingredient foods is also being considered. A new committee of BHNRC scientists will be formed to address carbohydrate data needs with the intent of addressing human and animal research. It will include ARS lead scientists on this project and other BHNRC chemists/scientists in this area. Review of external carbohydrate data/datasets will be reviewed for linking and/or inclusion in FDC. Human Breast Milk Composition. The Methods and Application of Food Composition Laboratory (MAFCL) is one of the leads of the Human Milk Composition Initiative (HMCI), a joint undertaking by federal agencies in the US and Canada to articulate human milk (HM)-related data needs relevant to federal programs, policies, and regulations. A manuscript series exemplifying potential public health opportunities in HM research is in process with perspectives from 50 federal staff from both countries with respect to: 1) HM composition data and metadata for collection, 2) public health relevance of such data to the US and Canadian populations, and 3) potential uses of these data to support federal programs and policies addressing public health. Dietary bioactive compounds and Special Interest Database. An HPLC method using relative response factors to quantify proanthocyanidins in cranberries was developed, validated and published in Phytochemical Analysis. It can be used to quantify individual oligomers from DP 2–9, total polymers and total proanthocyanidins in cranberries, cranberry food products and dietary supplements. Structures of cranberry proanthocyanidins oligomers were further characterized by using LC-MSn technologies. Data for anthocyanins in processed red raspberries were published in Journal of Berry Research and will be used to update the Special Interest Database on Flavonoids. Dietary supplements: Children’s Multivitamin/mineral (MVM) Supplements: Label Claims and Measured Content Compared to RDA and Tolerable Upper Intake Levels (UL) for ages 1 to <4 years. Dietary supplements (DSs), designed to fill gaps in food intakes, are not to be sole sources of these nutrients. We previously showed that labeled and analytically confirmed contents of children’s MVMs for ages 4+ exceeded the RDAs for 4-8 years for 13 nutrients in most MVMs. Most DS (n=45/60) in this national study had recommended serving sizes (SS) for ages 1-3 and 4+. SS were the same (n=14) or different (n=31) which may lead to significant excess of DVs for the younger children – 577/1038 (55.6%) serving sizes exceeding 100% DV, suggesting MVMs for two age groups in one bottle should be reconsidered. All were labeled above the RDAs for total vitamin A, natural sources of vitamin E, vitamins B-6 and B-12, riboflavin and thiamin; >85% of MVMs for copper, iron, vitamin D, folic acid, niacin; and >50% of MVMs for zinc, chromium, retinol, selenium, and synthetic sources of vitamin E. Products labeled at or above the ULs included 75-80% of products with copper or niacin, ~25% with zinc or retinol, and 5 MVMs containing folic acid. In products labeled at or above RDAs, the analytical mean of overages was 25.7% above labels for folic acid, 38.3% for vitamin D, 24.1% for vitamin E, 19.9% for B-12 and 25.5% for retinol. Maximum overages were 90%, 110%, 70.9%, 59.9% and 63.5% above labels, respectively. Dietary supplements: Calcium dietary supplements national study. National estimates for calcium and vitamin D content in calcium DSs will be determined by sampling and analysis of representative products. Lab testing of vitamins and minerals in adult DSs (n=99 in 2-3 lots) and children’s DSs (n=20, 2 lots) is complete. Calcium/other minerals were measured by ICP spectrometry; vitamin D content was measured using HPLC. Quality control (QC) materials were sent to labs. Disintegration and dissolution testing of various dosage forms (tablets, capsules, chewable tablets, gummies, softgels) is being planned. Dietary supplements: Turmeric dietary supplements: Turmeric in food and DS is used for potential health effects, e.g., anti-inflammation, anti-tumor activity; we studied turmeric to evaluate the curcuminoid content in DSs containing turmeric material (Curcuma longa root powder and/or extract), comparing mandatory and voluntary label information. The Food and Drug Administration (FDA) requires the weight of turmeric material on labels, but not the concentration of individual constituents. DSs (n=54x2 lots) were analyzed for curcumin, desmethoxycurcumin, and bisdemethoxycurcumin with SRMs, duplicates and in-house controls to ensure analytical quality. Total curcuminoids (3 measured) were compared to labels, if available. Labeled turmeric ranged from 50–1300 mg/serving (most common-500 mg); 41 products (76%) had labeled amounts for total curcuminoids, 15 products (37%) had only a minimum claim (“standardized to at least” an amount, or >1 turmeric source with a claim for only one) and differences from label ranged from -34.4 - 73.1% (mean=11.4, sd=12.1); 26 products (63%) with an exact amount claim, had differences from label ranging from -4 - 25% (mean=7.3, sd=6.1). Most turmeric DSs met or exceeded volunteered curcuminoid content; 1/3 of products provided only a minimum level on the label.
1. USDA, FDA and Office of Dietary Supplements (ODS)-National Institute of Health (NIH) Database for the iodine content of common foods. Foods high in iodine, a nutrient found insufficient in about 20% of the diets for U.S. women of reproductive age and with potential serious growth and development consequences in fetuses, were analyzed and merged with FDA analytical data to develop a multi-agency database on iodine in U.S. foods. More than 400 foods considered significant contributors to U.S. iodine intake are included in a Joint USDA, FDA, ODS-NIH Iodine Table on the content of U.S. foods that was released on the Methods and Applications of Food Composition Laboratory (MAFCL) website. These data will support health policy and nutrition guidance for a vulnerable population.
2. Assessing changes in the sodium content of commercially processed foods in the U.S. High sodium intake is linked to increased chronic disease risk. Most Americans consume higher sodium than recommended, and the majority of it comes from commercially packaged and restaurant foods with added sodium. In 2009, the Centers for Disease Control and USDA initiated the Sentinel Foods Surveillance Program, tracking 125 Sentinel Foods using multiple means to assess changes in their sodium content. The results show some progress had been made in sodium reduction in the marketplace; however, sodium content for many highly consumed foods continued to be high and variable. Continued efforts are needed by the food manufacturers to lower the sodium content of packaged and restaurant foods, and for public health officials to monitor the progress.
Ahuja, J.K., Li, Y., Haytowitz, D., Bahadur, R., Pehrsson, P.R., Cogswell, M. 2019. Assessing Changes in Sodium Content of Selected Popular Commercially Processed and Restaurant Foods: Results from the USDA:CDC Sentinel Foods Surveillance Program. Nutrients. 11(8), 1754. https://doi.org/10.3390/nu11081754.
Casavale, K.O., Ahuja, J.K., Wu, X., Li, Y., Quam, J., Olson, R., Pehrsson, P.R., Allen, L.H., Balentine, D., Hanspal, M., Hayward, D., Hines, E.P., McClung, J., Perrine, C., Belfort, M.B., Dallas, D., German, D., Kim, J., McGuire, M., McGuire, M. Morrow, A., Nommsen-Rivers, Rasmussen, K.M., Zempleni, J., Lynch, C. 2019. NIH workshop on human milk composition: summary and visions. American Journal of Clinical Nutrition. 110(3):769-779. https://doi.org/10.1093/ajcn/nqz123.
Wu, X., Sun, J., Ahuja, J.K., Haytowitz, D.B., Chen, P., Burton-Freeman, B., Pehrsson, P.R. 2019. Anthocyanin profiles and contents in processed red raspberries in the US market. Journal of Berry Research. 12:477 – 48. https://doi.org/10.1186/s13104-019-4506-7.
Burton-Freeman, B.M., Zhang, X., Ahuja, J.K. 2019. Characterization of the nutrient profile of processed raspberries. Nutrition and Healthy Aging. 5: 225–236. https://doi.org/10.3233/NHA-190072.