An investigation of the formulation and nutritional composition of modern meat analogue products
Bibliographic record
Abstract
Meat analogues, or plant-based products that simulate the properties of traditional meat products, have secured a position in the conversation of protein foods. Rapid growth of the meat analogue industry is occurring in the global food marketplace in both the retail and food service sectors. The purpose of this review was to investigate the ingredients used in the formulation of modern meat analogues, evaluate the nutrient specifications of modern meat analogue products, and then form a comparison with traditional meat products. Based on this investigation, it was determined – firstly, the ingredients used in the formulation of modern meat analogue products make these products fit under the classification of ultra-processed foods; and secondly, the nutrient specifications of popular meat analogue products can effectively simulate the nutrient specifications of the meat products they are attempting to simulate. Therefore, based on these findings, modern meat analogue products can offer roughly the same composition of nutrients as traditional meat products, albeit with many different ingredients and a high level of further processing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".