The French food composition database is run by CIQUAL in the Observatory of Food, unit of ANSES (the French agency for food, environmental and occupational health safety). These files are in French and provide the composition of 3185 foods for 67 components (e.g.: carbohydrates, individual sugars and starch, proteins, fat and fatty acids, vitamins and minerals, energy...),

[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 Data have been compiled from published and unpublished sources.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 Unpublished data include those obtained from the food industry, other government agencies, and research conducted under contracts initiated by USDA’s Agricultural Research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8" (US Department of Agriculture 1976-92), and its four supplements (US Department of Agriculture 1990-93), which superseded the 1963 edition (Watt and Merrill, 1963).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.

Abstract

Household Income and Expenditure Surveys (HIES) are implemented to rebase consumer price indices and estimates of household contribution to national gross domestic product. More recently, HIES data have been used in poverty analyses and to conduct nutrition and food security oriented analyses. The more recent applications of HIES data – poverty, nutrition and food security – require the use of edible-portion conversion factors to convert the reported acquisition of wholefoods into edible portions so estimates can be made of what people apparently ingest. These data then require the use of food composition tables (FCTs) to convert the edible portion into caloric and nutrient consumption values, so total energy and nutrient consumption can be estimated. HIES data in the Pacific region are coded using the United Nations Statistics Division’s Classification of Individual Consumption According to Purpose (COICOP); however, there is no regionally standardised linkage between COICOP and the Pacific Islands Food Composition Tables Second Edition (PIFCT). Furthermore, the PIFCTs do not have edible-portion conversion factors and are insufficient to cover the full list of foods reported in the HIES. To address this, the Pacific Nutrient Database (PNDB) was developed to provide the Pacific region with a standard set of conversion factors and food composition data that are mapped to COICOP (1999). To add more value to the database, each food item is also mapped to COICOP 2018, classified into FAO Commodity Groups and food groups to compute Household Dietary Diversity Scores (HDDS). The PNDB includes 26 components plus edible and inedible portions for a total of 822 foods.

Geographic coverage

Pacific Region.

Analysis unit

COICOP commodity

Universe

Not applicable.

Kind of data

Aggregate data [agg]

Sampling procedure

Not applicable.

Sampling deviation

Not applicable.

Mode of data collection

Other [oth]

Research instrument

Questionnaires used were those from Household Income and Expenditure Surveys (HIES) in the Pacific Region.

Cleaning operations

Data editing was done using the software Excel.

Response rate

Not applicable.

Sampling error estimates

Not applicable.

Data appraisal

Not applicable.

Food composition tables organized by the commodities included in the Global Expanded Nutrient Supply model (GENuS) dataset. Their construction and use are described in the GENuS methods paper (citation below). Citations for original source tables and units for each nutrient are found in accompanying "readme" file.

A dataset of expert-validated Nutrition and Physical Activity Plans (NAP), i.e., plans created and reviewed by experts in nutrition and physcial activity, as well as, medical experts. The dataset contains foods, meals, recipes, and physcial activities.

The following databases have been used as input:

• USDA Food Composition Database, April 2018 (United States)
• Portuguese Food Composition Table (TCAP)
• Brazilian Food Composition Table (TACO)
• Peso, Uma Questão de Peso (Portugal)*
• Base de Datos del Centro de Enseñanza Superior de Nutrición y Dietética (CESNID Foundation, Spain)*
• Luján University Data Base (Argentina)
• Spanish Food Composition Database (BEDCA, UGR)
• French food composition (CIQUAL, ANSES)
• Composition tables of foods and Greek dishes (CTFG, HHF)*
• Food Composition Database for Epidemiological Studies in Italy (FCDBE, IEO)*
• Irish Food Composition Database (IFCD, UCC)
• Polish Foods Composition Database (PFCT, NFNI)*
• Tables of the Nutritional Composition of Food Consumed in Brazil of IBGE (TCNA, IBGE)
• McCance and Widdowson’s The Composition of Foods integrated dataset (Co FID)
• Mexican Food Equivalents System (Mexico)*

* Items originating from these databases where not included in the offered file due to copyright issues; you may contact the authors directly for further inquiries regarding these items.

Funding: The research leading to these results has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 817732 (PROTEIN: PeRsOnalized nutriTion for hEalthy living).

© 2022 by the authors.

IntroductionFood composition databases (FCDBs) are essential resources for characterizing, documenting, and advancing scientific understanding of food quality across the entire spectrum of edible biodiversity. This knowledge supports a wide range of applications with societal impact spanning the global food system. To maximize the utility of food composition data, FCDBs must adhere to criteria such as validated analytical methods, high-resolution metadata, and FAIR Data Principles (Findable, Accessible, Interoperable, and Reusable). However, complexity and variability in food data pose significant challenges to meeting these standards.MethodsIn this study, we conducted an integrative review of 35 data attributes across 101 FCDBs from 110 countries. The data attributes were categorized into three groups: general database information, foods and components, and FAIRness.ResultsOur findings reveal evaluated databases show substantial variability in scope and content, with the number of foods and components ranging from few to thousands. FCDBs with the highest numbers of food samples (≥1,102) and components (≥244) tend to rely on secondary data sourced from scientific articles or other FCDBs. In contrast, databases with fewer food samples and components predominantly feature primary analytical data generated in-house. Notably, only one-third of FCDBs reported data on more than 100 food components. FCDBs were infrequently updated, with web-based interfaces being updated more frequently than static tables. When assessed for FAIR compliance, all FCDBs met the criteria for Findability. However, aggregated scores for Accessibility, Interoperability, and Reusability for the reviewed FCDBs were 30, 69, and 43%, respectively.DiscussionThese scores reflect limitations in inadequate metadata, lack of scientific naming, and unclear data reuse notices. Notably, these results are associated with country economic classification, as databases from high-income countries showed greater inclusion of primary data, web-based interfaces, more regular updates, and strong adherence to FAIR principles. Our integrative review presents the current state of FCDBs highlighting emerging opportunities and recommendations. By fostering a deeper understanding of food composition, diverse stakeholders across food systems will be better equipped to address societal challenges, leveraging data-driven solutions to support human and planetary health.

ABSTRACT Objective: To analyze the variations in the daily intake of dietary fiber and calories according to the different nutrient composition and homemade measure tables. Methods: Five different methods based on different nutrient composition and household measure tables were used to calculate daily calorie and fiber intake, measured using a food frequency questionnaire, of 633 pregnant women receiving care in primary health care units in the Southern region of Brazil; they were selected to participate in a cohort study. The agreement between the five methods was evaluated using the Kappa and weighted Kappa coefficients. The Nutritional Support Table, a Brazilian traditional food composition table and the Brazilian household expenditure survey were used in Method 1. Brazilian Food Composition Table and the Table for the Assessment of Household Measures (Pinheiro) were used in Methods 2 and 3. The average values of all subtypes of food listed in the Brazilian Food Composition Table for each corresponding item in the food frequency questionnaire were calculated in the method 3. The United States Department of Agriculture Food Composition Table and the table complied by Pinheiro were used in Method 4. The Brazilian Food Composition Table and the Brazilian household expenditure survey were used in Method 5. Results: The highest agreement of calorie intake values were found between Methods 2 and 3 (Kappa=0.94; 0.92-0.95), and the lowest agreement was found between Methods 4 and 5 (Kappa=0.46; 0.42-0.50). As for the fiber intake, the highest agreement was found between Methods 2 and 5 (Kappa=0.87; 0.82-0.90), and the lowest agreement was observed between Methods 1 and 4 (Kappa=0.36; 0.3-0.43). Conclusion: Considerable differences were found between the nutritional composition tables. Therefore, the choice of the table can influence the comparability between studies.

[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.

IntroductionThe identification of classes of nutritionally similar food items is important for creating food exchange lists to meet health requirements and for informing nutrition guidelines and campaigns. Cluster analysis methods can assign food items into classes based on the similarity in their nutrient contents. Finite mixture models use probabilistic classification with the advantage of taking into account the uncertainty of class thresholds.MethodsThis paper uses univariate Gaussian mixture models to determine the probabilistic classification of food items in the South African Food Composition Database (SAFCDB) based on nutrient content.ResultsClassifying food items by animal protein, fatty acid, available carbohydrate, total fibre, sodium, iron, vitamin A, thiamin and riboflavin contents produced data-driven classes with differing means and estimates of variability and could be clearly ranked on a low to high nutrient contents scale. Classifying food items by their sodium content resulted in five classes with the class means ranging from 1.57 to 706.27 mg per 100 g. Four classes were identified based on available carbohydrate content with the highest carbohydrate class having a mean content of 59.15 g per 100 g. Food items clustered into two classes when examining their fatty acid content. Foods with a high iron content had a mean of 1.46 mg per 100 g and was one of three classes identified for iron. Classes containing nutrient-rich food items that exhibited extreme nutrient values were also identified for several vitamins and minerals.DiscussionThe overlap between classes was evident and supports the use of probabilistic classification methods. Food items in each of the identified classes were comparable to allowed food lists developed for therapeutic diets. This data-driven ranking of nutritionally similar classes could be considered for diet planning for medical conditions and individuals with dietary restrictions.

Poor diet quality contributes to stunting in children under five years’, micronutrient deficiencies, overweight/obesity and diet-related non-communicable diseases. Dietary diversity is low in Burkina Faso, where less than 25% of the population meets the minimum dietary diversity recommended. Increasing consumption of fruits, vegetables, nuts, seeds and whole grains increases the likelihood of consuming adequate amounts of the full range of nutrients essential to human health and protects against many forms of non-communicable disease. We compiled food composition data on 97 foods from 37 tree species from Burkina Faso, largely indigenous, with few exceptions, and undomesticated. Data were gathered through an extensive review of the literature and were compiled following international standards. Metodology:Literature review

Here we report a list of diverse and culturally relevant foods that justify compositional cataloging as part of the Periodic Table of Food Initiative (PTFI). The PTFI is a participatory project aimed at addressing food composition information gaps worldwide through a standardized, accessible, and enabling platform based on state-of-the-art nutritional analytical technology. Through data analytics and engagement with experts across the globe, here we identify 1650 important foods for biochemical compositional analysis.

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.

a Food Standards Australia and New Zealand,b United States Department of Agriculture,c Food Standards Agency,d Separate databases for flavonoids, carotenoids, proanthocyanidins and isoflavones,e Eurofir EBASIS contains bioactive data for UK and Europe,f National Health Survey,ghttps://www.xyris.com.au/foodworks/fw_pro.html,hhttp://www.nutribase.com/highend.html,ihttp://www.foodresearch.ca/wp-content/uploads/2013/06/candat-features-1.pdf,j Tinuviel Software,i Downlees Systems,k Forestfield Software,l Kelicomp,mhttp://www.tinuvielsoftware.com/faqs.htm,nhttp://www.dietsoftware.com/canada.html,o Text file: a file that only contains text,p A file containing tables of information stored in columns and separated by tabs (can be exported into almost any spreadsheet program),q Microsoft Excel spreadsheet,r Microsoft Access Database file: is a database file with automated functions and queries,s American Standard Code for Information Interchange (a standard file type that can be used by many programs),t Database File Format (this file type can be opened with Microsoft Excel and Access),u information to create Excel or PDF available,v Composition of Foods, Australia,w International Network of Food Data System,x Users guide states food name is most descriptive & recognisable of food referencedyhttp://www.foodstandards.gov.au/science/monitoringnutrients/nutrientables/nuttab/Pages/NUTTAB-2010-electronic-database-files.aspx,zhttp://www.foodstandards.gov.au/science/monitoringnutrients/ausnut/ausnutdatafiles/Pages/default.aspx,aahttp://ndb.nal.usda.gov/ndb/search/list,bbhttp://tna.europarchive.org/20110116113217/http://www.food.gov.uk/science/dietarysurveys/dietsurveys/,cchttp://webprod3.hc-sc.gc.ca/cnf-fce/index-eng.jspDesktop analysis and examination of six key food composition databases format.

Background: The past few years have witnessed an increase in the availability of food products containing one or more low- and no-calorie sweeteners (LNCS) in the Spanish market, mostly due to the new massive reformulation plan. However, these are not included in food composition tables or databases, and, therefore, assessment of their intake among the population is complex. This study aims to update a database including commercialized foods and beverages.Method: A systematic search of ingredients information from the different food and beverage categories was undertaken during 2019 by recording the availability and type of LNCS declared in the information of the product from labels and online shopping platforms of retailers from Spain to update a previous food composition database compiled in 2017.Results: A total of 1,238 products were identified. The major groups were sugar and sweets (24%), non-alcoholic beverages (21%), cereals and grains (19%), and milk and dairy products (14%) accounting for >70% of total products. The mainly declared LNCS were sorbitol (19.5%), sucralose (19.5%), and acesulfame K (19.2%).Conclusion: There is a wide variety of products that include LNCS as a main ingredient with higher availability than when compared with the results of database of 2017, consequently, it might be expected that LNCS are commonly consumed at present in the Spanish diet.

This dataset is about: (Table 3) Relationship between mass percents of food composition and length of beryx-alfonsino in samples caught on the Plato Ridge and the Probatov Bank. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.763150 for more information.

Esta tabla contiene la composición de ciertos alimentos consumidos tradicionalmente en el Ecuador Amazónico, es el compendio de Tabla de Composición externas y literatura científica.

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.

ABSATRCT In Brazil, there are no data on the iodine content of foods, making it difficult for the population to assess their consumption of iodine. Such information is necessary for public policies aimed at establishing nutritional goals. The objective this article is to construct a table of the iodine content of foods. For the construction of the table, databases from 14 countries were used. The foods used were those listed in the 2008-2009 Household Budget Survey, except those containing added salt, and the doubts about whether or not the food was submitted to any kind of preparation. The compilation of international databases of iodine content resulted in 266 foods, which were grouped into 15 groups. Iodine was also quantified by food group and iodized salt. Data were presented as median, minimum, and maximum. A broad variation in the iodine content of foods was found between countries and inter- and intra-food groups. Those with the highest content were fish and seafood, and dairy products. Regarding salt iodization, these countries followed the recommendation of the World Health Organization, except for Spain, Norway and Turkey. The Food Iodine Content Table can be a useful tool for assessing iodine intake, being important in research on nutritional status, food guidance, and public health programs.

The CDNO provides structured terminologies to describe nutritional attributes of material entities that contribute to human diet. These terms are intended primarily to be associated with datasets that quantify concentration of chemical nutritional components derived from samples taken from any stage in the production of food raw materials (including from crops, livestock, fisheries) and through processing and supply chains. Additional knowledge associated with these dietary sources may be represented by terms that describe functional, physical and other attributes. Whilst recognising that dietary nutrients within food substrates may be present as complex and dynamic physical and chemical structures or mixtures, CDNO focuses on components typically quantified in an analytical chemistry laboratory. The primary CDNO class ‘dietary nutritional component’ contains hierarchical sets of terms organised to reflect commonly used classifications of chemical food composition. This class does not represent an exhaustive classification of chemical components, but focuses on structuring terms according to widely accepted categories. This class is independent of, but may be used in conjunction with, classes that describe ‘analytical methods’ for quantification, ‘physical properties’ or ‘dietary function’. Quantification data may be used and reported in research literature, to inform food composition tables and labelling, or for supply chain quality assurance and control. More specifically, terms within the ‘nutritional component concentration’ class may be used to represent quantification of components described in the ‘dietary nutritional component’ class. Concentration data are intended to be described in conjunction with post-composed metadata concepts, such as represented by the Food Ontology (FoodOn) ‘Food product by organism’, which derives from some food or anatomical entity and a NCBI organismal classification ontology (NCBITaxon) entity. The common vocabulary and relationships defined within CDNO should facilitate description, communication and exchange of material entity-derived nutritional composition datasets typically generated by analytical laboratories. The organisation of the vocabulary is structured to reflect common categories variously used by those involved in crop, livestock or other organismal production, associated R&D and breeding, as well as the food processing and supply sector, and nutritionists, inlcuding compilers and users of food composition databases. The CDNO therefore supports characterisation of genetic diversity and management of biodiversity collections, as well as sharing of knowledge relating to dietary composition between a wider set of researchers, breeders, farmers, processors and other stakeholders. Further development of the functional class should also assist in understanding how interactions between organismal genetic and environmental variation contribute to human diet and health in the farm to fork continuum.

The summary data table of estimates of usual intakes for energy, nutrients & other dietary components from food uses data collected from Canadians in the 2004 and 2015 Canadian Community Health Survey (CCHS) - Nutrition. Data are provided for the household population by 16 DRI age–sex groups at the national, regional and provincial levels. Please note that the following estimates have been updated December 2024: Estimates of usual intakes of folate (mcg/d (Dietary Folate Equivalents (DFE)) for 2015 at the national level were revised based on updated calculation of DFE content of recipes in the nutrient database. The following were not revised: national estimates (2004), and provincial- or regional- estimates (2004, 2015). As such, it is not possible to compare national-level estimates between 2004 and 2015 or compare provincial and national-level estimates for 2015. Visit the Nutrition Surveillance Data Tool to explore the 2015 CCHS-Nutrition usual intake data with interactive visualizations and a customizable data table.