Nutritional quality and price of plant‐based dairy and meat analogs in the Canadian food supply system
Bibliographic record
Abstract
There has been an increased consumer interest and public health emphasis on plant-based protein foods, resulting in a rise in the availability of highly processed plant-based analogs. The objectives of this study were to assess the nutritional quality and the price of plant-based dairy and meat analogs compared to their respective animal-derived products and to examine the association between processing levels and the nutritional quality among these products. Using a branded food composition database, products in cheese, yogurt, milk, and meat categories were examined (n = 3231). Products were categorized as plant-based analogs versus animal-derived products using the ingredient list. Products were examined for their nutrient content, overall nutritional quality using the Food Standards Australia New Zealand nutrient profiling model, price, and processing levels using the NOVA classification. All plant-based analogs had lower protein and higher total carbohydrate, sugar, and fiber content compared to their respective animal-derived products. Compared to their respective animal-derived products, plant-based milk and meat analogs had lower energy, total fat, and saturated fat content; plant-based yogurt and meat analogs had lower sodium content; and all plant-based dairy analogs had lower calcium content. Plant-based cheese and yogurt analogs were more expensive than animal-based products; however, there was no significant difference among milk and meat products. There was no association between processing levels and overall nutritional quality among dairy and meat products. Plant-based analogs may be part of a healthy and affordable diet to reduce the intakes of nutrients of concern; however, additional compositional guidelines and/or labeling may be needed to highlight the differences in the levels of nutrients to encourage.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| 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".