Visualising and analysing the research trends of dietary fiber: a bibliometric study
Bibliographic record
Abstract
Abstract The increasing recognition of dietary fiber’s health benefits has driven extensive research in this area. However, with the growing volume of studies, tracking emerging trends and identifying key research directions can be challenging. This study employs CiteSpace and bibliometric analysis to examine 21,434 articles from the Web of Science database, providing a comprehensive overview of dietary fiber research from 2010 to 2024. Major outcomes reveal that research in dietary fiber has shown a steady upward trend, particularly in the last five years, with China and the United States contributing the most publications. Canada, however, exhibits the highest centrality in global cooperation. The analysis identifies Nutrients , Foods , and Food Chemistry as the top journals publishing dietary fiber research. Prolific authors, such as Gidley and Zhang, along with leading institutions like the Consejo Superior de Investigaciones Cientificas and the United States Department of Agriculture, are highlighted. Keyword co-occurrence analysis reveals research hotspots, including the functional characteristics of dietary fiber, its relationship with intestinal health, and its application in functional foods. Emerging trends focus on the development of new dietary fibers, interaction mechanisms with intestinal flora, and the role of dietary fiber in chronic disease prevention. These insights offer valuable guidance for future research directions and practical applications in nutrition and health-related industries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.128 | 0.160 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".