Nutritional Facts and Health/Nutrition Claims of Commercial Plant-Based Infant Foods: Where Do We Stand?
Bibliographic record
Abstract
One of the current drivers of the infant food market is the rising demand for vegan products, and thus accurate knowledge of their nutritional composition is required to guide parents and health professionals. Thus, this study aimed to assess the nutritional composition of commercial plant-based infant foods, in addition to analyzing their health/nutrition claims. A selection of infant products launched in the global market (2017-2021) were classified into eight types and each type was divided into vegan and vegetarian products. Based on the ingredients list, cereals, seeds, pseudocereals and/or pulses were the most used ingredients in the retrieved products. The nutritional composition of six out eight types varied significantly among vegan and vegetarian products. When protein, calcium and iron contents differed significantly, vegan products had the highest protein content in all categories, compared to those that were vegetarian. When significant differences were found in sugar content, vegan products have lower amounts in all categories, compared to vegetarian products. Health and nutrition claims were found mostly used in vegetarian products. Strategies to reduce added sodium and sugar, and saturated fatty acids is required to ensure a healthy diet for infants. This study also implies the importance of a complete labelling of infants' foods, especially vegan products to help parents making a reasonable choice.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".