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)
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.
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.
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