Mapping Probiotic Encapsulation Research: A Bibliometric and Thematic Analysis of Functional Food Applications
Bibliographic record
Abstract
Probiotic encapsulation has gained significant attention in functional food science as a means to enhance the stability and viability of probiotics during processing, storage, and gastrointestinal transit.This study provides a bibliometric analysis of global research trends in probiotic encapsulation, identifying key contributors, influential publications, and thematic developments.Using the Scopus database, 515 relevant articles were analyzed through Bibliometrix and VOSviewer to map the evolution of this field.The findings indicate a notable increase in research output, particularly after 2015, with a focus on advanced techniques such as microencapsulation and co-encapsulation.Key applications include improving gut health, managing chronic diseases, and integrating probiotics into functional food matrices.Despite these advancements, significant gaps remain, particularly in clinical validation, large-scale production, and the development of sustainable delivery systems.The analysis highlights the interdisciplinary nature of probiotic encapsulation research, involving collaborations across food science, biotechnology, and health sciences.Future research should emphasize innovative technologies, personalized nutrition approaches, and sustainable encapsulation materials to enhance probiotic efficacy and meet growing consumer demands.This study provides a comprehensive roadmap for advancing probiotic encapsulation, ensuring its integration into functional food products and therapeutic applications.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.238 | 0.287 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".