Association between nutritional quality and the degree of naturalness in animal-based and plant-based food products
Bibliographic record
Abstract
Abstract Consumers tend to perceive certain foods as more natural and in turn as more nutritious. Thus, this study aimed to evaluate the nutritional quality, the degree of naturalness, and their association with animal-based and plant-based food products. A total of 1275 food products were collected by the Food Quality Observatory in Québec (Canada) between 2018 and 2022. These products were divided into five categories: sliced processed meats ( n = 477), yogurts and dairy desserts ( n = 344), sausages ( n = 266), processed cheese products ( n = 96) and plant-based alternatives ( n = 92) within these four categories. The overall nutritional quality was evaluated using the Nutri-Score and the front-of-package (FOP) nutrition symbol recently implemented in Canada, while the degree of naturalness was measured using the Food Naturalness Index (FNI). Yogurts and dairy desserts as well as plant-based alternatives had lower Nutri-Score and thus, higher nutritional quality compared to other food categories. The FOP symbol for foods high in saturated fat or sodium was more common in sliced processed meats and sausages. FNI scores were higher in processed cheese products than in other categories, indicating a greater degree of naturalness. Correlations between nutritional quality and food naturalness varied depending on the food category and the nutrient profiling model, with Spearman coefficients being positive or negative and ranging from weak to moderate. This study supports the idea that food naturalness and nutritional quality offer complementary information depending on the food category. Further research in other food categories would help to better understand the associations between the two concepts.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".