Location: Methods and Application of Food Composition Laboratory
2019 Annual Report
Objectives
Objective 1. Utilize comprehensive, non-targeted methods for classifying foods and for identifying candidate compounds that can then be quantified by specific targeted methods to determine the variance of nutritionally important food components in the western diet. (NP 107, Problem Statement 2A).
Objective 2. Apply comprehensive non-targeted methods to identify, and apply specific targeted methods to quantify, nutritionally important compounds in food crops that may be impacted by genetics, environment, management, and processing (GxExMxP). (NP 107, Problem Statement 1A and NP 216, Component 5)
Objective 3. In collaboration with other laboratories, utilize metabolite fingerprinting, metabolomics, and lipidomics to: A) characterize the impact of genetics, environment (including geographical location) and management on the nutritional characteristics of dry beans and soybeans; and B) evaluate the impact of bovine diet and environment on the nutritional composition (with emphasis on lipids) of milk and dairy products. (NP 107, Problem Statement 1A and 2A and NP 216, Component 5)
Objective 4. Demonstrate that comprehensive non-targeted analysis of individual samples from selected national studies prior to compositing is a critical compliment to targeted data in the new USDA Food Composition Database. (NP 107, Problem Statement 2A)
Approach
New analytical technology will be adapted to the analysis of foods to help nutritionist, health practitioners, and the public to understand the link between agricultural systems, nutrition, and health. The food supply is changing rapidly with new genotypes from the farm and new processed foods in the marketplace. Every food consists of thousands of compounds, each with the potential to impact human health. Each must be identified, quantified, and added to a database. For a database to keep pace with the new foods, high throughput must be combined with even more detailed, comprehensive analyses. Rapid screening methods, based on metabolite fingerprinting, will allow classification of foods and determination of the relative variance associated with food production factors. Selected samples from each class will be subjected to metabolomic and lipidomic analysis. These methods will produce libraries of compounds that will allow metabolite fingerprinting methods to be used for rapid identification and quantification and will fill out nutrient databases. The combination of fingerprinting, metabolomic, and lipidomic methods will be used to analyze commodities and processed foods and to evaluate the nutritional qualities of crops and food products as a function of genetics, environment, management, and processing. This data is critical to establishing relationships between agricultural systems, nutrition, and Health. Ultimately, this data will be combined into a single database available to researchers and the public.
Progress Report
New project has been in place for 3 months at the time of writing. See final report for 8040-52000-063-00D for 2019 accomplishments.
Accomplishments
Review Publications
Wu, X., Sun, J., Haytowitz, D.B., Harnly, J.M., Chen, P., Pehrsson, P.R. 2017. Challenges of developing a valid Dietary Glucosinolate database. Journal of Food Composition and Analysis. https://doi.org/10.1016/j.jfca.2017.07.014.
Dwyer, J., Bailen, R.A., Saldanha, L., Gahche, J., Costello, R., Betz, J., Davis, C., Bailey, R., Potischman, N., Ershow, A., Sorkin, B., Kuzak, A., Rios-Avila, L., Chang, F., Goshorn, J., Andrews, K., Pehrsson, P.R., Gusev, P., Harnly, J.M., Hardy, C.J., Emenaker, N., Herrick, K. 2018. The Dietary Supplement Label Database (DSLD): A new resource for researchers and consumers. Journal of Nutrition. https://doi.org/10.1093/jn/nxy082.
Lu, Y., Dong, W., Yang, T., Luo, Y., Wang, Q., Chen, P. 2017. Effect of preharvest CaCl2 spray and postharvest UV-B radiation on storage quality of broccoli microgreens, a richer source of glucosinolates. Journal of Food Composition and Analysis. 67(1):55-62. https://doi.org/10.1016/j.jfca.2017.12.035.
Harnly, J.M., Bergana, M., Adams, K., Moore, J., Xie, Z. Moore, J. 2018. Variance of commercial powdered milks analyzed by 1H-NMR and impact on detection of adulterants. Journal of Agricultural and Food Chemistry. https://doi.org/10.1021/acs.jafc.8b00432.
Choe, U., Li, Y., Gao, B., Yu, L., Wang, T.T., Sun, J., Chen, P., Liu, J., Yu, L. 2018. Chemical compositions of cold-pressed broccoli, carrot and cucumber seed flours, and their in vitro gut microbiota modulatory, anti-inflammatory and free radical scavenging properties. Journal of Agricultural and Food Chemistry. 66(35):9309-0317. https://doi.org/10.1021/acs.jafc.8b03343.
Krueger, D., Arora, S., Bzhelyansky, A., Chen, P., Chlenov, A., Collison, M., Durst, R., Es-Safi, N., Giancaspro, G., Hall, G., Hammerstone, J., Ingle, P., Jennens-Clough, M., Ji, D., Ji, J., Kennedy, D., Lawson, T., Lee, J., Lock, S., Mudge, E., Neal-Kababick, J., Phillips, M., Phillips, T., Rejaei, A., Rettinger, M., Rimmer, C., R.-Haralambides, A., Royce, S., Schaneberg, B., Solyom, A., Stern, N., Sullivan, D., Szpylka, J., Wise, M.L., Yan, X., Yoo, S., Yu, K., Zhang, Y., Coates, S. 2015. AOAC SMPR 2014.007: Authentication of selected Vaccinium species (Anthocyanins) in dietary ingredients and dietary supplements. Journal of AOAC International. 98(4):1052-1054. https://doi.org/10.5740/jaoac.int.SMPR2014.007.
Sun, J., Zhang, M., Luo, Y., Kubdelia, N., Harnly, J.M., Chen, P. 2018. Determination of variance of secondary metabolites in lettuces grown under different light sources by flow injection mass spectrometric (FIMS) fingerprinting and ANOVA-PCA. Journal of Analysis and Testing. 2(4):312-321. https://doi.org/10.1007/s41664-018-0072-6.
