[Note: Integrated as part of FoodData Central, April 2019.] The database consists of several sets of data: food descriptions, nutrients, weights and measures, footnotes, and sources of data. The Nutrient Data file contains mean nutrient values per 100 g of the edible portion of food, along with fields to further describe the mean value. Information is provided on household measures for food items. Weights are given for edible material without refuse. Footnotes are provided for a few items where information about food description, weights and measures, or nutrient values could not be accommodated in existing fields. Data have been compiled from published and unpublished sources. Published data sources include the scientific literature. Unpublished data include those obtained from the food industry, other government agencies, and research conducted under contracts initiated by USDA’s Agricultural Research Service (ARS). Updated data have been published electronically on the USDA Nutrient Data Laboratory (NDL) web site since 1992. Standard Reference (SR) 28 includes composition data for all the food groups and nutrients published in the 21 volumes of "Agriculture Handbook 8" (US Department of Agriculture 1976-92), and its four supplements (US Department of Agriculture 1990-93), which superseded the 1963 edition (Watt and Merrill, 1963). SR28 supersedes all previous releases, including the printed versions, in the event of any differences. Attribution for photos: Photo 1: k7246-9 Copyright free, public _domain photo by Scott Bauer Photo 2: k8234-2 Copyright free, public _domain photo by Scott Bauer Resources in this dataset:Resource Title: READ ME - Documentation and User Guide - Composition of Foods Raw, Processed, Prepared - USDA National Nutrient Database for Standard Reference, Release 28. File Name: sr28_doc.pdfResource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: ASCII (6.0Mb; ISO/IEC 8859-1). File Name: sr28asc.zipResource Description: Delimited file suitable for importing into many programs. The tables are organized in a relational format, and can be used with a relational database management system (RDBMS), which will allow you to form your own queries and generate custom reports.Resource Title: ACCESS (25.2Mb). File Name: sr28db.zipResource Description: This file contains the SR28 data imported into a Microsoft Access (2007 or later) database. It includes relationships between files and a few sample queries and reports.Resource Title: ASCII (Abbreviated; 1.1Mb; ISO/IEC 8859-1). File Name: sr28abbr.zipResource Description: Delimited file suitable for importing into many programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Title: Excel (Abbreviated; 2.9Mb). File Name: sr28abxl.zipResource Description: For use with Microsoft Excel (2007 or later), but can also be used by many other spreadsheet programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/ Resource Title: ASCII (Update Files; 1.1Mb; ISO/IEC 8859-1). File Name: sr28upd.zipResource Description: Update Files - Contains updates for those users who have loaded Release 27 into their own programs and wish to do their own updates. These files contain the updates between SR27 and SR28. Delimited file suitable for import into many programs.

[Note: Integrated as part of FoodData Central, April 2019.] The USDA National Nutrient Database for Standard Reference (SR) is the major source of food composition data in the United States and provides the foundation for most food composition databases in the public and private sectors. This is the last release of the database in its current format. SR-Legacy will continue its preeminent role as a stand-alone food composition resource and will be available in the new modernized system currently under development. SR-Legacy contains data on 7,793 food items and up to 150 food components that were reported in SR28 (2015), with selected corrections and updates. This release supersedes all previous releases. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_DB.zipResource Description: Locally stored copy - The USDA National Nutrient Database for Standard Reference as a relational database using AcessResource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: Locally stored copy - ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.

The Nutrient Data Laboratory is responsible for developing authoritative nutrient databases that contain a wide range of food composition values of the nation's food supply. This requires updating and revising the USDA Nutrient Database for Standard Reference (SR) and developing various special interest databases. However, with over 7,000 food items in SR and a complete nutrient profile costing approximately $2,000 for one sample, analyzing every food item for every nutrient and meeting all user requirements is impossible. Consequently, priorities must be determined. Procedures using food consumption data and nutrient values for developing the Key Foods list are explained. Key Foods have been identified as those food items that contribute up to 75% of any one nutrient to the dietary intake of the US population. These Key Foods will be used to set priorities for nutrient analyses under the National Food and Nutrient Analysis Program. The tables describe key foods based on Continuing Survey Of Food Intakes By Individuals (CSFII, 1989-) and WWEIA-NHANES (What We Eat In America - National Health and Nutrition Examination Survey 2001-) survey data.

Several USDA food composition databases, including the Food and Nutrient Database for Dietary Studies (FNDDS), Standard Reference (SR) Legacy, and the USDA Branded Food Products Database, have transitioned to FoodData Central, a new and harmonized USDA food and nutrient data system. FoodData Central also includes expanded nutrient content information as well as links to diverse data sources that offer related agricultural, environmental, food, health, dietary supplement, and other information. The new system is designed to strengthen the capacity for rigorous research and policy applications through its search capabilities, downloadable datasets, and detailed documentation. Application developers can incorporate the information into their applications and web sites through the application programming interface (API) REST access. The constantly changing and expanding food supply is a challenge to those who are interested in using food and nutrient data. Including diverse types of data in one data system gives researchers, policymakers, and other audiences a key resource for addressing vital nutrition and health issues. FoodData Central: Includes five distinct types of data containing information on food and nutrient profiles, each with a unique purpose: Foundation Foods; Experimental Foods; Standard Reference; Food and Nutrient Database for Dietary Studies; USDA Global Branded Food Products Database. Provides a broad snapshot in time of the nutrients and other components found in a wide variety of foods and food products. Presents data that come from a variety of sources and are updated as new information becomes available. Includes values that are derived through a variety of analytic and computational approaches, using state-of-the-art methodologies and transparent presentation. FoodData Central is managed by the Agricultural Research Service and hosted by the National Agricultural Library. Resources in this dataset:Resource Title: Website Pointer for FoodData Central. File Name: Web Page, url: https://fdc.nal.usda.gov/index.html Includes Search, Download data, API Guide, Data Type Documentation, and Help pages.

The dataset, Survey-SR, provides the nutrient data for assessing dietary intakes from the national survey What We Eat In America, National Health and Nutrition Examination Survey (WWEIA, NHANES). Historically, USDA databases have been used for national nutrition monitoring (1). Currently, the Food and Nutrient Database for Dietary Studies (FNDDS) (2), is used by Food Surveys Research Group, ARS, to process dietary intake data from WWEIA, NHANES. Nutrient values for FNDDS are based on Survey-SR. Survey-SR was referred to as the "Primary Data Set" in older publications. Early versions of the dataset were composed mainly of commodity-type items such as wheat flour, sugar, milk, etc. However, with increased consumption of commercial processed and restaurant foods and changes in how national nutrition monitoring data are used (1), many commercial processed and restaurant items have been added to Survey-SR.

The current version, Survey-SR 2013-2014, is mainly based on the USDA National Nutrient Database for Standard Reference (SR) 28 (2) and contains sixty-six nutrientseach for 3,404 foods. These nutrient data will be used for assessing intake data from WWEIA, NHANES 2013-2014. Nutrient profiles were added for 265 new foods and updated for about 500 foods from the version used for the previous survey (WWEIA, NHANES 2011-12). New foods added include mainly commercially processed foods such as several gluten-free products, milk substitutes, sauces and condiments such as sriracha, pesto and wasabi, Greek yogurt, breakfast cereals, low-sodium meat products, whole grain pastas and baked products, and several beverages including bottled tea and coffee, coconut water, malt beverages, hard cider, fruit-flavored drinks, fortified fruit juices and fruit and/or vegetable smoothies. Several school lunch pizzas and chicken products, fast-food sandwiches, and new beef cuts were also added, as they are now reported more frequently by survey respondents. Nutrient profiles were updated for several commonly consumed foods such as cheddar, mozzarella and American cheese, ground beef, butter, and catsup. The changes in nutrient values may be due to reformulations in products, changes in the market shares of brands, or more accurate data. Examples of more accurate data include analytical data, market share data, and data from a nationally representative sample. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES 2013-14 (Survey SR 2013-14). File Name: SurveySR_2013_14 (1).zipResource Description: Access database downloaded on November 16, 2017. US Department of Agriculture, Agricultural Research Service, Nutrient Data Laboratory. USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES (Survey-SR), October 2015. Resource Title: Data Dictionary. File Name: SurveySR_DD.pdf

The United States Department of Agriculture (USDA) Nutrient Data Laboratory (NDL), in collaboration with the National Cattlemen's Beef Association, National Pork Board, American Lamb Board, and meat scientists at selected universities, has conducted several research studies designed to update and expand nutrient data on retail meat cuts in the USDA National Nutrient Database for Standard Reference (SR). These studies have provided current and accurate estimates of data to update SR, and the study results have been incorporated into data sets that can be used for nutrient labeling. NDL has developed these data sets, presented in an easy-to-use table format.
Each data set provides retailers with easier access to the most accurate nutrient data for the purpose of on-pack nutrition labeling and for nutrition claims. These data sets focus on the cuts identified by USDA Food Safety and Inspection Service (FSIS) proposed labeling regulations for fresh, single-ingredient meats. The FSIS, an agency of the USDA, is the public health agency responsible for ensuring that the nation's commercial supply of meat, poultry and egg products is safe, wholesome, and correctly labeled and packaged. Resources in this dataset:Resource Title: The USDA Nutrient Data Set for Retail Beef Cuts, Release 3.0. File Name: Retail_Beef_Cuts03.pdfResource Description: Each data set provides retailers with easier access to the most accurate nutrient data for the purpose of on-pack nutrition labeling and for nutrition claims. These data sets focus on the cuts identified by USDA Food Safety and Inspection Service (FSIS) proposed labeling regulations for fresh, single-ingredient meats. The online version of this document can be found at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/Retail_Beef_Cuts03.pdfResource Title: The USDA Nutrient Data Set for Retail Beef Cuts, Release 3.0 (MS Excel download). File Name: Beef_Labelling_Table03.xlsxResource Description: The tables in "The USDA Nutrient Data Set for Retail Beef Cuts" imported into a Microsoft Excel spreadsheet. The online version of this spreadsheet can be found at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/Beef_Labelling_Table03.xlsxResource Title: USDA Nutrient Data Set for Retail Pork Cuts, Release 2. File Name: Pork09.pdfResource Description: Each data set provides retailers with easier access to the most accurate nutrient data for the purpose of on-pack nutrition labeling and for nutrition claims. These data sets focus on the cuts identified by USDA Food Safety and Inspection Service (FSIS) proposed labeling regulations for fresh, single-ingredient meats. Find the online version of this document at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/Pork09.pdfResource Title: USDA Nutrient Data Set for Retail Pork Cuts, Release 2 (MS Excel download). File Name: Pork09_Tbl.xlsResource Description: The tables in "The Revised USDA Nutrient Data Set for Fresh Pork" imported into a Microsoft Excel spreadsheet. Find the online version of this spreadsheet at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/Pork09_Tbl.xlsResource Title: Raw Ground Pork (MS Excel download). File Name: EstNutrRawGrndPork4_28.xlsResource Description: These tables provide nutrient profiles for raw ground pork from 4-28% fat, in increments of 1% fat, as determined by regression equations. Find the online version of this spreadsheet at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/EstNutrRawGrndPork4_28.xlsResource Title: USDA Nutrient Data Set for Retail Veal Cuts. File Name: Retail_Veal_Cuts.pdfResource Description: Each data set provides retailers with easier access to the most accurate nutrient data for the purpose of on-pack nutrition labeling and for nutrition claims. These data sets focus on the cuts identified by USDA Food Safety and Inspection Service (FSIS) proposed labeling regulations for fresh, single-ingredient meats. Find the online version of this document at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/Retail_Veal_Cuts.pdfResource Title: Veal Labeling Table (MS Excel download). File Name: Veal_Labeling_Table.xlsxResource Description: The tables in "The USDA Nutrient Data Set for Retail Veal Cuts" imported into a Microsoft Excel spreadsheet. Find the online version of this spreadsheet at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/Veal_Labeling_Table.xlsxResource Title: USDA Nutrient Data Set for Retail Lamb Cuts. File Name: Lamb_Labeling_Doc.pdfResource Description: Each data set provides retailers with easier access to the most accurate nutrient data for the purpose of on-pack nutrition labeling and for nutrition claims. These data sets focus on the cuts identified by USDA Food Safety and Inspection Service (FSIS) proposed labeling regulations for fresh, single-ingredient meats. Find the online version of this document at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/Lamb_Labeling_Doc.pdfResource Title: Lamb Labeling Table (MS Excel download). File Name: Lamb_Labeling_Table.xlsxResource Description: The tables in "The USDA Nutrient Data Set for Retail Lamb Cuts" imported into a Microsoft Excel spreadsheet. Find the online version of this spreadsheet at https://www.ars.usda.gov/ARSUserFiles/80400525/Data/Meat/Lamb_Labeling_Table.xlsx

[Note: Integrated as part of FoodData Central, April 2019.] USDA's Food and Nutrient Database for Dietary Studies (FNDDS) is a database that is used to convert food and beverages consumed in What We Eat In America (WWEIA), National Health and Nutrition Examination Survey (NHANES) into gram amounts and to determine their nutrient values. Because FNDDS is used to generate the nutrient intake data files for WWEIA, NHANES, it is not required to estimate nutrient intakes from the survey. FNDDS is made available for researchers using WWEIA, NHANES to review the nutrient profiles for specific foods and beverages as well as their associated portions and recipes. Such detailed information makes it possible for researchers to conduct enhanced analysis of dietary intakes. FNDDS can also be used in other dietary studies to code foods/beverages and amounts eaten and to calculate the amounts of nutrients/food components in those items. FNDDS is released every two-years in conjunction with the WWEIA, NHANES dietary data release. The FNDDS is available for free download from the FSRG website. Resources in this dataset:Resource Title: Website Pointer to Food and Nutrient Database for Dietary Studies. File Name: Web Page, url: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/fndds/ USDA's Food and Nutrient Database for Dietary Studies (FNDDS) is a database that is used to convert food and beverages consumed in What We Eat In America (WWEIA), National Health and Nutrition Examination Survey (NHANES) into gram amounts and to determine their nutrient values.

The database consists of several sets of data: food descriptions, nutrients, weights and measures, footnotes, and sources of data. The Nutrient Data file contains mean nutrient values per 100 g of the edible portion of food, along with fields to further describe the mean value. Information is provided on household measures for food items. Weights are given for edible material without refuse. Footnotes are provided for a few items where information about food description, weights and measures, or nutrient values could not be accommodated in existing fields.

Data have been compiled from published and unpublished sources. Published data sources include the scientific literature. Unpublished data include those obtained from the food industry, other government agencies, and research conducted under contracts initiated by USDA’s Agricultural Research Service (ARS).

Updated data have been published electronically on the USDA Nutrient Data Laboratory (NDL) web site since 1992. Standard Reference (SR) 27 includes composition data for all the food groups and nutrients published in the 21 volumes of “Agriculture Handbook 8” (US Department of Agriculture 1976-92), and its four supplements (US Department of Agriculture 1990-93), which superseded the 1963 edition (Watt and Merrill, 1963). SR27 supersedes all previous releases, including the printed versions, in the event of any differences.

Attribution for photos:Photo 1: k7246-9 Copyright free, public domain photo by Scott BauerPhoto 2: k8234-2 Copyright free, public domain photo by Scott Bauer

This database contains values for six choline metabolites: Betaine, Glycerophosphocholine, Phosphocholine, Phosphatidylcholine, Sphingomyelin, and Total choline This database was created through a collaborative effort between the USDA and the Department of Nutrition, University of North Carolina. Resources in this dataset:Resource Title: READ ME - Documentation: USDA Database for the Choline Content of Common Foods . File Name: Choln02.pdfResource Description: Contains information about documentation, methods and procedures, data evaluation, format, and dissemination information. Also contains references and general information about choline compounds. Resource Title: Choline Content Release 2. File Name: Choln02.zipResource Description: .zip file with Food and Nutrient database tables for Choline from phosphocholine, Choline from phosphatidylcholine, Choline from glycerophoshocholine, Betaine, and Choline from sphingomyelin.

USDA Branded Food Dataset

The USDA Branded Food Dataset is a cleaned and structured dataset created from USDA FoodData Central (FDC) data. This dataset consolidates extensive information on branded food items available in the U.S., providing a valuable resource for nutrition analysis, machine learning, and consumer research.

  Overview

The USDA Branded Food Dataset compiles data on over 456,000 branded food items, including:

Nutrient Profiles: Detailed nutrient content for… See the full description on the dataset page: https://huggingface.co/datasets/jacktol/usda-branded-food-data.

Through its food distribution programs, USDA purchases a variety of food including fruits, vegetables, meat, grains, and dairy products to be distributed directly to needy households or for use in congregate feeding programs that help Americans obtain access to nutritious food and support American agriculture. This report contains nutrient and food group analyses of the USDA Food distributed through NSLP, CACFP, CSFP, FDPIR, and TEFAP in fiscal year 2009.

Analysis of ‘USDA Table of Nutrient Retention Factors, Release 6 (2007)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b6817e18-07be-426c-9fce-3ef5be3289a8 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Release 6, of this data set, replaces the previous release, Release 5, issued in 2003. The data set contains the factors for calculating retention of 26 vitamins, minerals and alcohol during food preparation. Factors for the other 25 food components in Release 4 are unchanged. Factors for total choline have been added to this release.

--- Original source retains full ownership of the source dataset ---

Context

The PLANTS Database provides standardized information about the vascular plants, mosses, liverworts, hornworts, and lichens of the U.S. and its territories. It includes names, plant symbols, checklists, distributional data, species abstracts, characteristics, images, plant links, references, and crop information, and automated tools.

This particular dataset is the Crop Nutrient Database.

Content

These are the fields included in the dataset. I'll be honest, I have no idea what some of them mean:

• Crop
• ScientificName
• Symbol
• NuContAvailable
• PlantPartHarvested
• CropCategory
• YieldUnit
• AvYieldUnitWeight(lb)
• AvMoisture%
• AvN%(dry)
• AvP%(dry)
• AvK%(dry)
• YieldUnitWeight(lb)_set
• YieldUnitWeight(lb)_Bau
• YieldUnitWeight(lb)_Joh
• YieldUnitWeight(lb)_Roberts
• YieldUnitWeight(lb)_WEEP
• YieldUnitWeight(lb)_Men
• YieldUnitWeight(lb)_Guy
• YieldUnitWeight(lb)_Mc
• YieldUnitWeight(lb)_Mah
• YieldUnitWeight(lb)_Sha
• YieldUnitWeight(lb)_Sch
• YieldUnitWeight(lb)_Atu
• YieldUnitWeight(lb)_Zim
• YieldUnitWeight(lb)_Scu
• YieldUnitWeight(lb)_John
• YieldUnitWeight(lb)_Arc
• DryMatter%_M-FF
• DryMatter%_NAS
• DryMatter%_F&L
• DryMatter%_F&N
• DryMatter%_Alb
• DryMatter%_Est1
• DryMatter%_Est2
• DryMatter%_Est3
• DryMatter%_Est4
• DryMatter%_Est5
• DryMatter%_Est6
• DryMatter%_M&R
• DryMatter%_M&L
• DryMatter%_Sun
• DryMatter%_Gro
• DryMatter%_AgH8-9
• DryMatter%_AgH8-12
• DryMatter%_B788
• AvDryMatter%
• N%(dry)_NAS
• N%(dry)_F&L
• N%(dry)_F&N
• N%(dry)_Swa
• N%(dry)_Chapko
• N%(dry)_Hill
• N%(dry)_Bru
• N%(dry)_AgH8-9
• N%(dry)_AgH8-12
• N%(dry)_B788
• N%(dry)_M&L
• N%(dry)_M-FF
• N%(dry)_M&R
• N%(dry)_Foster
• N%(dry)_Rob1
• N%(dry)_Rob2
• N%(dry)_Coa
• N%(dry)_And
• N%(dry)_Gol1
• N%(dry)_Gol2
• N%(dry)_Wol
• N%(dry)_Pete
• N%(dry)_Col
• N%(dry)_Alb
• N%(dry)_Arc
• N%(dry)_Bis
• N%(dry)_Gar
• N%(dry)_Heg
• N%(dry)_Flo
• N%(dry)_Feil
• N%(dry)_Bre
• N%(dry)_Burns
• N%(dry)_Coc
• P%(dry)_M-FF
• P%(dry)_NAS
• P%(dry)_F&L
• P%(dry)_F&N
• P%(dry)_AgH8-9
• P%(dry)_AgH8-12
• P%(dry)_B788
• P%(dry)_M&L
• P%(dry)_L&V
• P%(dry)_Foster
• P%(dry)_Rob1
• P%(dry)_Rob2
• P%(dry)_Coa
• P%(dry)_And
• P%(dry)_Gol1
• P%(dry)_Gol2
• P%(dry)_Sims
• P%(dry)_Wol
• P%(dry)_Pete
• P%(dry)_Col
• P%(dry)_Alb
• P%(dry)_Arc
• P%(dry)_Swa
• P%(dry)_Rei
• K%(dry)_M-FF
• K%(dry)_NAS
• K%(dry)_F&L
• K%(dry)_F&N
• K%(dry)_AgH8-9
• K%(dry)_AgH8-12
• K%(dry)_B788
• K%(dry)_Foster
• K%(dry)_Rob1
• K%(dry)_Rob2
• K%(dry)_Coa
• K%(dry)_And
• K%(dry)_Gol1
• K%(dry)_Gol2
• K%(dry)_Sims
• K%(dry)_Wol
• K%(dry)_Pete
• K%(dry)_Col
• K%(dry)_Alb
• K%(dry)_Arc
• K%(dry)_Swa
• K%(dry)_Rei
• Moisture%_M&R
• Moisture%_M&L
• Moisture%_Sun
• Moisture%_Gro
• gWater/100g_AgH8-9
• gWater/100g_AgH8-12
• gWater/100g_B788
• Protein%(dry)_NAS
• Protein%(dry)_F&L
• Protein%(dry)_F&N
• Protein%(dry)_Swa
• Protein%(dry)_Chapko
• Protein%(dry)_Hill
• Protein%(dry)_Bru
• Protein%(dry)_Bis
• Protein%(dry)_Gar
• Protein%(dry)_Heg
• Protein%(dry)_Flo
• Protein%(dry)_Feil
• Protein%(dry)_Bre
• Protein%(dry)_Burns
• gProtein/100g(wet)_AgH8-9
• gProtein/100g(wet)_AgH8-12
• gProtein/100g(wet)_B788
• Protein%(wet)_M&L
• N%(wet)_M-FF
• P%(wet)_M-FF
• gP/100g(wet)_AgH8-9
• gP/100g(wet)_AgH8-12
• gP/100g(wet)_B788
• P%(wet)_M&L
• K%(wet)_M-FF
• gK/100g(wet)_AgH8-9
• gK/100g(wet)_AgH8-12
• gK/100g(wet)_B788

The database contains values for 283 food items for the following proanthocyanidins groups: Dimers Trimers 4-6 mers (tetramers, pentamers and hexamers) 7-10 mers (heptamers, octamers, nonamers and decamers Polymers (DP>10) Resources in this dataset:Resource Title: READ ME - USDA Database for the Proanthocyanidin Content of Selected Foods. File Name: PA02.pdfResource Description: Information regarding the documentation, data sources, data management, data quality evaluation, aggregation and format, sources of data, and references cited.Resource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: Data Dictionary. File Name: PA02_DD.pdfResource Title: PA02.accdb. File Name: PA02.zipResource Description: This file contains the Proanthocyanidin Database imported into a MS Access database version 2007 or later. The file structure is the same as that of the USDA National Nutrient Database for Standard Reference.

Release 6, of this data set, replaces the previous release, Release 5, issued in 2003. The data set contains the factors for calculating retention of 26 vitamins, minerals and alcohol during food preparation. Factors for the other 25 food components in Release 4 are unchanged. Factors for total choline have been added to this release. Resources in this dataset:Resource Title: USDA Table of Nutrient Retention Factors, Release 6 (2007). File Name: retn06.pdfResource Description: Release 6, of this data set, replaces the previous release, Release 5, issued in 2003. The data set contains the factors for calculating retention of 26 vitamins, minerals and alcohol during food preparation. Factors for the other 25 food components in Release 4 are unchanged. Factors for total choline have been added to this release. Resource Title: USDA Table of Nutrient Retention Factors, Release 6 (2007). File Name: NutrientRetention.csvResource Description: Release 6, of this data set, replaces the previous release, Release 5, issued in 2003. The data set contains the factors for calculating retention of 26 vitamins, minerals and alcohol during food preparation. Factors for the other 25 food components in Release 4 are unchanged. Factors for total choline have been added to this release. This data was downloaded from the published web site text version and converted to CSV format on November 16, 2017.Resource Title: USDA Table of Nutrient Retention Factors, Release 6 (2007) Data Dictionary. File Name: NutrientRetentionDD.csv

Context

I needed to record our grocery intake using a standard nomenclature that supports access and tabulation of nutrients consumed. That is, a nomenclature that spans retail grocery using UPC to the identifiers used to access nutrition labels. I wanted to know if our household's intake of nutrients complied with the bounds set forth in the 2015-2020 USDA Dietary Guidelines. And if not, what foods will most efficiently remedy dietary deficiencies/excesses. Professionally, I have been devising countermeasures to fight chronic disease and see food more so than medication as the key.

Content

Downloaded from the USDA FoodData Central database: https://fdc.nal.usda.gov/download-datasets.html, this constellation of tables is centered on the food table. That table identifies foods broadly classed as Standard like what you might find in the periphery of the store, or Branded which typically occupies the center aisles. Each Standard Reference food identifier points to exactly one food label, where each label (in the set of all labels) is a set of entity attribute value (EAV) triples comprised of FoodId NutrientId and Quantity per hectogram (i.e., 100g). Standard Reference and Branded Reference foods each occupy their own tables. Since categories for Standard Reference foods are coded, a table providing their descriptions are also provided. Note that categories for Branded Reference foods are in verbose text.

Layouts

For field definitions and table layouts see: Download_&_API_Field_Descriptions_April_2021.pdf in this distribution.

Derived tables

Syntax for tables derived from USDA FoodData Central: - Filenames are prefixed by user initials 'JCS_' - The remaining portion of the filename is a hyphenated list of its domain names. - FoodId in derived files were zero-left padded for proper collation and joining.

Domains for derived tables from which individual column values are drawn: - SRCat: Standard Reference food category, zero-left padded to two places. - Applicable: A Boolean {YES,NO} indicating whether a standard reference food category applies to the current study. - CatDesc: The description of that food category. - FoodId: A unique and unchanging food identifier, zero-left padded to seven places. - Description: Text describing each Standard Reference food item. - FoodName: Text describing each Branded Reference food item. - BRCat: Branded Reference category expressed as verbose text. - UPC: Uniform Product Code that identifies a branded food item regardless of revision date. - BrandOwner: Corporate entity that owns that brand of foods. - SvgSizeHgs: Serving size of a branded food item in hectograms. - DateAvail: Revision date for a UPC's metadata or nutrition label. - VerCnt: Number of revisions that a food identified by its UPC code underwent. - NutrientId: Uniquely identifies a nutrient as a 4-digit number with cross-references stored in nutrient.csv - Per100g: Quantity of a nutrient per hectogram as a float but stored as plain text.

Acknowledgements

To my Chair, Prof. Taghi M. Khoshgoftaar, PhD.

Inspiration

Given a list of foods and when and how much of each were consumed: - aggregate quantities of each nutrient consumed, - norming the quantities of nutrients consumed to average daily calorie burn, and - comparing each nutrient consumed to the target range for that nutrient.

Nutrient-wise deficiencies and excesses relative to target ranges are fed to a recommender that identifies: - what foods most efficiently by weight remedies these deficiencies and excesses - while being foods most likely to be consumed.

Rankings presented by the recommender during one period are evaluated by foods consumed in the following period. What ranking is the most 'compact'? That is, what ranking has the most of its foods consumed among its top k foods?

description:

This database was developed with support from the Office of Dietary Supplements, National Institutes of Health for flavonoid intake studies. The database is a useful tool for flavonoid intake and health outcome studies for any population globally. It contains data for 29 individual flavonoid compounds in six subclasses of flavonoids for every food in a subset of 2,926 food items which provide the basis for the Food and Nutrient Database for Dietary Studies (FNDDS 4.1). Proanthocyanidins data are not included at the present time. For flavonoid intake data for the U.S. population based on NHANES 2007-08, please refer to the Food Surveys Research Group website.

This database was developed with support from the Office of Dietary Supplements, National Institutes of Health for flavonoid intake studies. The database is a useful tool for flavonoid intake and health outcome studies for any population globally. It contains data for 29 individual flavonoid compounds in six subclasses of flavonoids for every food in a subset of 2,926 food items which provide the basis for the Food and Nutrient Database for Dietary Studies (FNDDS 4.1). Proanthocyanidins data are not included at the present time. For flavonoid intake data for the U.S. population based on NHANES 2007-08, please refer to the Food Surveys Research Group website.

NUOnet Vision: Efficient use of nutrients to optimize production and product quality of food for animals and humans, fuel and fiber in a sustainable manner that contributes to ecosystem services. This record contains the DET and Data Dictionary for NUOnet - the data files may be found at https://usdaars.maps.arcgis.com/apps/MapSeries/index.html?appid=e90392a99d5c427487c6c37cf6d47844 Best nutrient management practices are critical for maintaining profitable economic returns, sustaining higher yields, lowering environmental impacts, optimizing nutritional quality, and providing ecosystem services. Best management practices that improve nutrient use efficiencies can reduce nutrient losses from agricultural systems. However, we need to improve our understanding of biological, physical and chemical influences on nutrient processes. For instance, crop use efficiency of nitrogen (N), the primary macronutrient regulating yield and protein content, can be reduced by processes such as denitrification (N2O and N2 emission), leaching (NH4-N, NO3-N, and organic-N), ammonia (NH3-N,) volatilization, surface runoff and erosion, disease, and non-crop competition. Similarly, we need to obtain more information about biological and physical cycles of nutrients, especially phosphorus (P), including factors that influence nutrient availability from fertilizers, crop residues, cover crops, manures, and other byproducts. We need a better understanding of relationships between soil biological communities and ecosystems, including plant roots and root exudates, and availability and uptake of macro- and micro-nutrients. In addition, we need information regarding how these practices impact yields, organoleptic qualities, and the macro- and micro-nutritional composition of plants. This information will improve our ability to develop best nutrient management practices. Optimal soil nutrient levels are critical for maximizing economic returns, increasing sustainable yields, lowering environmental impacts, sustaining ecosystem services and optimizing nutritional and organoleptic qualities of human and animal foods. Efficient management practices are crucial for increasing economic returns for land managers in a sustainable manner while producing high quality of food for animals and humans with reduced off-site transfer of nutrients from agricultural areas in watersheds. Optimizing N and P inputs requires more information about nutrient inputs from fertilizers, manures, composts, agricultural byproducts, cover crops, and other nutrient sources in addition to nutrient cycling within soils. This requires data from long-term nutrient management studies across a wide range of soils, crops, and environmental conditions. Land management needs are to connect nutrient management practices for crops with nutrient use efficiency; crop quality; crop chemical composition and nutritional value, quality and acceptability for animal and human health. Development of databases that enable the scientific exploration of connections among data generated from diverse research efforts such as nutrient management, fate and ecosystem service outcomes, nutritional composition of crops, and animal and human health, is needed. Nitrogen is a key nutrient that enhances agricultural yield and protein content, but multiple N loss pathways, as previously mentioned, reduce crop N use efficiency (NUE). Implementing proper management practices is needed to reduce N losses from agricultural systems. ARS has multidisciplinary scientific teams with expertise in soils, ecological engineering, hydrology, livestock management and nutrition, horticulture, crop breeding, human and animal nutrition, post-harvest management and processing, and other areas, and intentional collaboration among these teams offers opportunities to rapidly improve NUE and crop quality and reduce off-site N losses. Similarly, increased P use efficiencies are needed to enhance and ensure sustainable agricultural production and to reduce environmental degradation of water sources. Manure is a valuable source of P and it can be used as a soil amendment to reduce crop production costs. However, there is a need to improve our understanding of the biological and physical cycles of soil P, as well as to obtain more information about P supplies from fertilizer, crop residues, cover crops, manure, and byproducts, and livestock nutrition impacts on manure properties. There is also a need for a better understanding of soil biological communities and ecosystems, including plant roots and root exudates and how their interactions with crops and community ecology affect yield and the uptake of macro- and micro-nutrients and the ultimate nutritional composition and organoleptic qualities of the crop. Studies documenting the responses of crop-associated biological communities to management practices and genetic technologies implemented across multiple environments (e.g., soil types and chemistries, hydrologic regimes, climates) will improve our understanding of gaps in macro- and micro-nutrient management strategies. A goal of the USDA-ARS is to increase agricultural production and quality while reducing environmental impacts. The Nutrient Uptake and Outcomes (NUOnet) database will be able to help establish baselines on nutrient use efficiencies; processes contributing to nutrient losses; and processes contributing to optimal crop yield, nutritional and organoleptic quality. This national database could be used to calculate many different environmental indicators from a comprehensive understanding of nutrient stocks and flows. Increasing our understanding of stocks and flows could help in the identification of knowledge gaps as well as areas where increased efficiencies can be achieved at a national level. NUOnet could also be used to develop tools to derive cost-benefit curves associated with nutrient management improvement scenarios and assess local, regional and national impacts of off-site nutrient loss. Understanding how agricultural production impacts human health is a challenge, and the database could be used to link crop management strategies to crop chemical composition to human consumption patterns and ultimately to human health outcomes. A national database will also be very important for development and evaluation of new technologies such as real-time sensing or other proximal and remote sensing technologies that enable assessment of nutrient use efficiencies, particularly at the grower level. The database could also be used to develop analyses that will contribute to the recommendation of policies for resource allocations that will most effectively fulfill the goals of the Grand Challenge. Such a national database with contributions from peers across different national programs could also enhance collaborations between ARS, universities, and extension specialists, as well as with producers, industry, and other partners. See the NUOnet Home Page for more information about this database and strategic goals. Resources in this dataset:Resource Title: GRACEnet-NUOnet Data Dictionary. File Name: GRACEnet-NUOnet_DD.csvResource Title: NUOnet Data Entry Template. File Name: DET_NATRES_NUO.zipResource Description: A multi-tab worksheet for data entry. Users can customize fields to be mandatory, set minimum and maximum values, and run a validation on fields as specified by the user. https://gpsr.ars.usda.gov/html/NUOnet_DET/DET_NATRES_NUO.xlsm

USDA’s Food Patterns Equivalents Database (FPED) converts the foods and beverages in the Food and Nutrient Database for Dietary Studies to the 37 USDA Food Patterns components. The FPED was formerly known as the MyPyramid Equivalents Database. The FPED serves as a unique research tool to evaluate food and beverage intakes of Americans with respect to the 2015-2020 Dietary Guidelines for Americans recommendations. The Food Patterns are measured as cup equivalents of Fruit, Vegetables, and Dairy; ounce equivalents of Grains and Protein Foods; teaspoon equivalents of Added Sugars; gram equivalents of Solid Fats and Oils; and the number of Alcoholic Drinks. In addition to the SAS datasets, the FPED release includes: (1) the Food Patterns Equivalents Ingredient Database (FPID) that includes the 37 USDA Food Patterns components per 100 grams of each unique ingredient used in the FNDDS; and (2) listings of gram weights for one cup equivalents of fruits, vegetables, dairy, and legumes used in the FPED. Resources in this dataset:Resource Title: Food Patterns Equivalents Database. File Name: Web Page, url: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/fped-overview/ Food Patterns Equivalents Database (FPED) converts the foods and beverages in the Food and Nutrient Database for Dietary Studies to the 37 USDA Food Patterns components. The FPED serves as a unique research tool to evaluate food and beverage intakes of Americans with respect to the 2015-2020 Dietary Guidelines for Americans recommendations.

The database contains values for 506 food items for five subclasses of flavonoids: FLAVONOLS:Quercetin, Kaempferol, Myricetin, Isorhamnetin FLAVONES: Luteolin, Apigenin FLAVANONES: Hesperetin, Naringenin, Eriodictyol FLAVAN-3-OLS: (+)-Catechin, (+)-Gallocatechin, (-)-Epicatechin, (-)-Epigallocatechin, (-)-Epicatechin 3-gallate, (-)-Epigallocatechin 3-gallate, Theaflavin, Theaflavin 3-gallate, Theaflavin 3'-gallate, Theaflavin 3,3' digallate, Thearubigins ANTHOCYANIDINS: Cyanidin, Delphinidin, Malvidin, Pelargonidin, Peonidin, Petunidin Resources in this dataset:Resource Title: USDA Database for the Flavonoid Content of Selected Foods, Release 3.3 (March 2018). File Name: Flav_R03-3.accdbResource Description: This file contains the Flavonoid Database imported into a MS Access database. In addition to the summary values, a separate file containing the individual values used to calculate the summary values is included, along with all information on conversion factors used to calculate the individual aglycone values, analytical method, country of origin, etc. It also includes relationships between files. You need MS Access 2007 or later to use this file. The file structure is the same as that of the USDA National Nutrient Database for Standard Reference. Resource Title: USDA Database for the Flavonoid Content of Selected Foods Release 3.3 Documentation . File Name: Flav3.3.pdf