Digital tools and technologies used in food fortification: A scoping review
Bibliographic record
Abstract
Food fortification (i.e., industrial fortification and biofortification) increases the micronutrient content of foods to improve population nutrition. Implementing effective fortification programs requires the generation and use of data to inform decision making. The use of digital tools and technologies (DTTs) for such purposes in broader nutrition programs is growing; however, there is limited consolidation of those used in fortification. This scoping review aimed to identify and describe DTTs used in fortification programs. We searched peer-reviewed and gray literature and conducted 17 stakeholder surveys. We then mapped DTTs identified against the fortification and nutrition data value chains. Of 11,741 articles identified, 158 met the inclusion criteria. From the included articles and stakeholder surveys, 125 DTTs were identified across three categories: software and tooling (n = 58), data and information lifecycle (n = 50), and hardware and infrastructure (n = 17). Gaps were identified in processing, post-harvest storage, aggregation, and transport nodes of the fortification value chain, and data prioritization, translation/dissemination, and decision-making nodes of the nutrition data value chain. DTTs have the potential to address challenges faced by fortification stakeholders to generate and use data to improve program decision making and nutritional impact. Further work is needed to standardize terminology, identify relevant DTTs from other sectors, and explore stakeholder needs.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".