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Food composition data (FCD) are detailed sets of information on the nutritionally important components of foods and provide values for energy and nutrients including protein, carbohydrates, fat, vitamins and minerals and for other important food components such as fibre. The data are presented in food composition databases (FCDBs).
The analysis highlights Food composition dataset, Chemical analysis and Data evaluation and quality as prominent areas in the source structure around Food composition data.
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
The extracted context around Food composition data shows recurring relationship patterns in the source. For example, Food composition data → Agriculture, Agriculture Handbook No, Analysis, Archived, Available, Church, Data/Classics/ah74, DC, Energy, EuroFIR, European, FAO, Foods, Greenfield, Harlow, Longman Scientific, Management, Merrill AL, No, Pearson’s Composition Another extracted example is Food composition data → Before, FCD, FCDBs, Food. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
food composition data foods fcd values fcdbs tables chemical components analysis example including uk quality analytical information nutrition samples available
TTTA extracted 49 structured relationships around Food composition data. Examples in this analysis include fibre → instance of → vitamins and minerals and for other important food components and the Association of Official Analytical Chemists → instance of → i.e. those recommended by organisation. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| fibre | instance of | vitamins and minerals and for other important food components | 0.80 | text |
| the Association of Official Analytical Chemists | instance of | i.e. those recommended by organisation | 0.80 | text |
| comparing the values to similar foods from other sources | instance of | should be considered | 0.80 | text |
| checking whether the sums of the values equate to | instance of | should be considered | 0.80 | text |
| fibre.Before computer technology | instance of | vitamins and minerals and for other important food components | 0.80 | text |
| these resources existed in printed tables with the oldest tables dating back to the early 19th century.In the UK | instance of | vitamins and minerals and for other important food components | 0.80 | text |
| FCD are listed in tables known as The Chemical Composition of Foods | instance of | vitamins and minerals and for other important food components | 0.80 | text |
| McCance | instance of | vitamins and minerals and for other important food components | 0.80 | text |
| Widdowson | instance of | vitamins and minerals and for other important food components | 0.80 | text |
| Food composition data | related to Collection process | FCDBs | 0.60 | section |
| Food composition data | related to Collection process | Food | 0.60 | section |
| Food composition data | related to Collection process | Chemical | 0.60 | section |
The concept neighborhoods around Food composition data bring nearby vocabulary together. In this analysis, examples include Food, Fcdbs and Data. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Food composition data, one of the stronger structural bridges in this analysis connects Food composition data with Food composition dataset. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Food composition data to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Food composition dataset, Chemical analysis & Data evaluation and quality, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Food composition data · EN edition · Analysis: TopicsToTalkAbout