Plant‐Based Meat Analogues: Processing, Product Safety, Protein Quality, and Contributions to Environmental Sustainability
Bibliographic record
Abstract
ABSTRACT Food production has been intensified significantly to meet food and nutrition security needs of the increasing global population. The environmental impact has been detrimental and thus, sustainable protein alternatives are being explored. Plant proteins are widely used because of their low cost, accessibility, health benefits, and ethical considerations. This has led to the development of plant‐based meat analogues (PBMAs) as a means of widening consumer food choice options because PBMAs are intended to mimic the appearance, mouthfeel, and taste of meat. From the review of available literature, processing methods used in converting amorphous plant protein powders to fibrous meat‐like structures can denature proteins and expose their reactive side chains to interact with other components in the food matrix. These interactions can lead to the formation of complexes that are resistant to enzymatic digestion and reduce the bioavailability of essential amino acids. Based on the amount of protein, the climate impact of PBMAs is estimated to be twice as much as that of peas, three times as that of nuts, and slightly higher than that of other pulses. However, when compared to animal proteins, the difference is remarkable. PBMAs recorded 0.99 kg CO 2 eq/100 g of protein whereas beef recorded 50 kg CO 2 eq/100 g of protein. This review shows that in closely imitating meat structures in PBMAs, the processing methods used can affect protein quality and increase their environmental impact.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".