Methodology for the Determination of Fruit, Vegetable, Nut and Legume Points for Food Supplies without Quantitative Ingredient Declarations and Its Application to a Large Canadian Packaged Food and Beverage Database
Bibliographic record
Abstract
Nutrient profiling (NP) models are useful tools for objectively and transparently quantifying the nutritional quality of packaged foods and beverages. Many NP models incorporate ingredients beneficial for health (e.g., fruits, vegetables, nuts, legumes (FVNL)) in addition to less healthful nutrients or components, assigning points based on the proportion of the product that contains FVNL ingredients. However, with food labelling in most countries lacking mandatory quantitative ingredient declarations (QUIDs), there is potential for the estimation of FVNL points to be ambiguous and inconsistent. The purpose of this article was to describe the development and application of methodology for estimating FVNL points for products without QUIDs, based on the position of FVNL components within the ingredients list. Using this method, FVNL points were calculated for packaged foods and beverages in the University of Toronto Food Label Information Program 2017 database (n = 17,337). Distributions of FVNL points were examined overall and by food category. This study provides evidence of the feasibility of this method in distinguishing between products with differing amounts of FVNL ingredients. This method will be valuable for researchers and policymakers in ensuring consistent, objective and reproducible estimations of FVNL points—and consequently, assessments of product healthfulness—for food supplies without QUIDs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".