Food technology innovations rooted in local wisdom: a bibliometric analysis of global research trends and future directions
Bibliographic record
Abstract
Local wisdom-informed food technology innovation is increasingly attracting global scholarly attention as a viable strategy for addressing food security, sustainability, and public health challenges. This investigation examines prevailing research trajectories pertinent to applying traditional knowledge in food innovation through a bibliometric analysis framework. Following PRISMA 2020, we identified, screened, and included records under predefined criteria to ensure rigor and transparency. A total of 158 scholarly documents (coverage: 2014–2024; search conducted through 31 December 2024) were scrutinized utilizing the Scopus database. The articles selected pertained to local wisdom-informed food technology innovation and were published in English in peer-reviewed academic journals. The bibliometric analysis employed VOSviewer and Bibliometrix (R-package) to delineate publication trends, keyword prevalence, and international collaborative efforts within this domain. The analysis revealed a notable escalation in publication volume, with the United States, Italy, and India leading the output (19 documents each), followed by Indonesia (15), Canada (14), and China (13). Predominant themes encompassed “traditional knowledge,” “ethnobotany,” “food security,” and “nutrition.” Beyond mapping, we articulate a practical translation perspective for local-wisdom technologies, emphasizing documentation, standardization, validation, and scale-up, to bridge discovery and application. However, a discernible research gap persists concerning the integration and validation of advanced technologies (e.g. artificial intelligence, the Internet of Things, nanotechnology) and the commercialization and equitable governance (e.g. benefit-sharing, traceability) of local food innovations. Findings should be interpreted in light of Scopus-only and English-language inclusion, which may under-represent non-English local scholarship.
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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 | 0.041 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.190 | 0.299 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".