Multi-Glycomic Characterization of Fiber from AOAC Methods Defines the Carbohydrate Structures.

Dietary fiber has long been known to be an essential component of a healthy diet, and recent investigations into the gut microbiome-health paradigm have identified fiber as a prime determinant in this interaction. Further, fiber is now known to impact the gut microbiome in a structure-specific manner, conferring differential bioactivities to these specific structures. However, current analytical methods for food carbohydrate analysis do not capture this important structural information. To address this need, we utilized rapid-throughput LC-MS methods to develop a novel analytical pipeline to determine the structural composition of soluble and insoluble fiber fractions from two AOAC methods (991.43 and 2017.16) at the total monosaccharide, glycosidic linkage, and free saccharide level. Two foods were chosen for this proof-of-concept study: oats and potato starch. For oats, both AOAC methods gave similar results. Insoluble fiber was found to be comprised of linkages corresponding to ß-glucan, arabinoxylan, xyloglucan, and mannan, while soluble fiber was found to be mostly ß-glucan, with small amounts of arabinogalactan. For raw potato starch, each AOAC method gave markedly different results in the soluble fiber fractions. These observed differences are attributable to the resistant starch content of potato starch and the different starch digestion conditions used in each method. Together, these tools are a means to obtain the complex structures present within dietary fiber while retaining "classical" determinations such as soluble and insoluble fiber. These efforts will provide an analytical framework to connect gravimetric fiber determinations with their constituent structures to better inform gut microbiome and clinical nutrition studies.

USDA's FoodData Central: what is it and why is it needed today?

FoodData Central (FDC) is the center of the USDA-based food-composition information web. It is an integrated data system that presently provides-in 1 place-5 distinct types of data containing information on food and nutrient profiles. Each data type has a unique purpose. Two of the data types-Foundation Foods (FF) and Experimental Foods (EF)-represent "a bridge to the future" in food and nutrient composition. They provide data and metadata that have never previously been available from a database. The other 3 data types are well established and familiar to many users: Standard Reference (SR) Legacy, Food and Nutrient Database for Dietary Studies (FNDDS), and Global Branded Foods Products Database (GBFPD). After >100 y of maintaining food-composition data within the USDA, it was clear that change was needed to respond to the rapid increase in the number and variety of foods in the food supply, evolution of analytical approaches, and new agricultural practices and products. FDC is USDA's answer to the challenge of providing reliable, web-based, transparent, and easily accessible information about the nutrients and other components of foods to meet the increasingly diverse needs of many audiences, including public health professionals, agricultural and environmental researchers, policy makers, nutrition professionals, health care providers, product developers, and the public at large.

Dietary fiber, starch, and sugars in bananas at different stages of ripeness in the retail market.

The goal of this work was to evaluate changes in dietary fiber measured by the traditional enzymatic-gravimetric method (AOAC 991.43) and the more recently accepted modified enzymatic-gravimetric method (AOAC 2011.25), mono- and disaccharides, and starch as a function of assessed ripeness in a controlled study of a single lot of bananas and in bananas at the same assessed stages of ripeness from bananas purchased in retail stores, from different suppliers. Sugars, starch, and dietary fiber were analyzed in bananas from a single lot, at different stages of ripeness, and in retail samples at the same assessed stages of ripeness. Mean fiber measured by the traditional enzymatic-gravimetric method (EG) was ~2 g/100g and not affected by ripeness. Mean fiber assessed with the recently modified method (mEG) was ~18 g/100g in unripe fruit and decreased to 4-5 g/100g in ripe and ~2 g/100g in overripe bananas. Slightly ripe and ripe bananas differed by ~1.1 g/100g in the controlled single-lot study but not among retail samples. There was a large increase in fructose, glucose and total sugar going from unripe to ripe with no differences between ripe and overripe. Aside from stage of ripeness, the carbohydrate composition in retail bananas is likely affected by differences in cultivar and post-harvest handling. Results from this study demonstrate the importance of measuring dietary fiber using the mEG approach, developing more comprehensive and sensitive carbohydrate analytical protocols and food composition data, and recognizing the impact of different stages of maturity and ripeness on carbohydrate intake estimated from food composition data.

The COMPARE Database: A Public Resource for Allergen Identification, Adapted for Continuous Improvement.

Motivation: The availability of databases identifying allergenic proteins via a transparent and consensus-based scientific approach is of prime importance to support the safety review of genetically-modified foods and feeds, and public safety in general. Over recent years, screening for potential new allergens sequences has become more complex due to the exponential increase of genomic sequence information. To address these challenges, an international collaborative scientific group coordinated by the Health and Environmental Sciences Institute (HESI), was tasked to develop a contemporary, adaptable, high-throughput process to build the COMprehensive Protein Allergen REsource (COMPARE) database, a publicly accessible allergen sequence data resource along with bioinformatics analytical tools following guidelines of FAO/WHO and CODEX Alimentarius Commission. Results: The COMPARE process is novel in that it involves the identification of candidate sequences via automated keyword-based sorting algorithm and manual curation of the annotated sequence entries retrieved from public protein sequence databases on a yearly basis; its process is meant for continuous improvement, with updates being transparently documented with each version; as a complementary approach, a yearly key-word based search of literature databases is added to identify new allergen sequences that were not (yet) submitted to protein databases; in addition, comments from the independent peer-review panel are posted on the website to increase transparency of decision making; finally, sequence comparison capabilities associated with the COMPARE database was developed to evaluate the potential allergenicity of proteins, based on internationally recognized guidelines, FAO/WHO and CODEX Alimentarius Commission.