Regulaciones y reformulación de alimentos: impactos en procesos, costos y cadena de suministro. Revisión sistemática
Bibliographic record
Abstract
Introduction: The increase in the consumption of sugary beverages and other processed drinks has had a direct impact on public health, promoting the adoption of regulations such as front-of-pack nutrition labeling (FOPNL). These policies aim to guide consumer decisions and encourage product reformulation to reduce sugars, sodium, and saturated fats. Objective: To systematically analyze the existing evidence on the impact of FOPNL regulations and product reformulation on production processes, costs, and supply chains. Materials and Methods: A systematic review was conducted in indexed databases (Scopus, SciELO, etc.) covering the period between 2018 and 2025. Inclusion and exclusion criteria were applied to focus on studies evaluating the effect of labeling policies and reformulation strategies in the food and beverage industry. Results: The literature shows that mandatory regulations generate greater changes than voluntary ones. Chile, Mexico, and Canada reported significant reductions in sugar and sodium content, as well as adjustments in product formulations and production processes. Although the industry anticipated increased costs and negative impacts on employment, studies indicate that prices were not consistently passed on to consumers and no adverse macroeconomic effects were observed. FOPNL systems proved to be more understandable than alternatives such as Guideline Daily Amounts (GDA), increasing pressure on the industry to reformulate. Conclusions: FOPNL and product reformulation are key tools to improve public health and foster innovation in the industry. However, gaps remain in the precise quantification of costs, in the long-term effects on supply chains, and in comparative analyses of regulatory frameworks.
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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 | 0.042 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".