The US Department of Agriculture (USDA) Nutrient Data Laboratory (NDL) develops and maintains the USDA National Nutrient Databank System (NDBS). Data are released from the NDBS for scientific and public use through the USDA National Nutrient Database for Standard Reference (SR) ( http://www.ars.usda.gov/ba/bhnrc/ndl ). In 1997 the NDL initiated the National Food and Nutrient Analysis Program (NFNAP) to update and expand its food-composition data. The program included: 1) nationwide probability-based sampling of foods; 2) central processing and archiving of food samples; 3) analysis of food components at commercial, government, and university laboratories; 4) incorporation of new analytical data into the NDBS; and 5) dissemination of these data to the scientific community. A key feature and strength of the NFNAP was a rigorous quality-control program that enabled independent verification of the accuracy and precision of analytical results. Custom-made food-control composites and/or commercially available certified reference materials were sent to the laboratories, blinded, with the samples. Data for these materials were essential to ongoing monitoring of analytical work, to identify and resolve suspected analytical problems, to ensure the accuracy and precision of results for the NFNAP food samples.

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The National Food and Nutrient Analysis Program (NFNAP) was implemented in 1997 to update and improve the quality of food composition data maintained by the United States Department of Agriculture (USDA). NFNAP was designed to sample and analyze frequently consumed foods in the U.S. food supply using statistically rigorous sampling plans, established sample handling procedures, and qualified analytical laboratories. Methods for careful handling of food samples from acquisition to analysis were developed to ensure the integrity of the samples and subsequent generation of accurate nutrient values. The infrastructure of NFNAP, under which over 1500 foods have been sampled, mandates tested sample handling protocols for a wide variety of foods. The majority of these foods were categorized into several major areas: 1) frozen foods; 2) fresh produce and/or highly perishable foods requiring refrigeration; 3) fast foods and prepared foods; 4) shelf-stable foods; 5) specialized study and non-retail (point of production) foods; and 6) foods from remote areas (e.g. American Indian reservations). This paper describes the sample handling approaches, from the collection and receipt of the food items to the preparation of the analytical samples, with emphasis on the strategies developed for those foods. It provides a foundation for developing sample handling protocols of foods to be analyzed under NFNAP and for other researchers working on similar projects.

Database average values for vitamin C in fresh produce can significantly over- or underestimate the content in a specific food supply. © 2018 Society of Chemical Industry.

A wide range of nutrients and health-promoting non-nutrient components in mushrooms are a subject of international research, but specific reference materials to facilitate comparison of results among laboratories are lacking. Commercially available food matrix reference materials do not contain components unique to mushrooms (e.g., ergosterol, vitamin D 2 , chitin, beta-glucans, agaritine, ergothioneine). A Mixed Mushroom Control Material (CM) (homogeneous mixture of 15 types of mushrooms) was prepared and characterized for selected components, including proximates (moisture, protein, ash), total folate, folate vitamers, ergosterol, ergosterol metabolites, vitamin D 2 (ergocalciferol), amino acids, total dietary fiber, agaritine, elements (sodium, potassium, phosphorous, magnesium, calcium, iron, copper, manganese, zinc), riboflavin, niacin, thiamin, vitamin B6, pantothenic acid). Subsamples of the CM are available to qualified laboratories from the Food Analysis Laboratory Control Center at Virginia Tech (Blacksburg, VA, USA), to be assayed concurrently with mushroom samples for which food composition data will be published along with results for the CM. Implementation of this CM should facilitate comparison of published data on mushroom composition and health benefit among species, and biodiversity within species by serving as common control sample that allows the separation of analytical variability from true differences in sample composition determined at different laboratories.