The impact of consumer in-home cooking methods on the physicochemical, sensory, and nutritional attributes of plant-based meat analogues (PBMAs) and meat: a review
Bibliographic record
Abstract
The global demand for Plant-based meat analogues (PBMAs) is rising due to increasing vegetarianism, health and environmental concerns, and animal-welfare issues. However, PBMAs encounter several challenges that limit widespread acceptance, including inadequate physico-chemical, sensory, and nutritional attributes, and concerns about over-processing and potential food-safety risks. To address these challenges, initiatives such as using high-quality raw materials, blending various plant protein sources, and employing innovative processing techniques are underway. Consumer acceptance of PBMAs also depends on the type of in-home cooking method used to prepare these foods, and the goal in many cases is for the quality attributes after cooking to resemble that of meat. The objective of this review is to compare the effects of in-home cooking methods on the quality of meat products to better understand how PBMAs will perform relative to their animal-based counterparts. Here, research articles that evaluate the impact of different cooking methods (e.g., baking and pan-frying) on the quality of meat and PBMAs were systematically summarized and compared to demonstrate trends in changes related to the physicochemical, sensory, and nutritional attributes of these products. The results revealed that the type of cooking method significantly influenced consumers' acceptance of meat and PBMAs. Changes in physical properties such as cooking loss and water holding capacity (WHC) can affect texture and were dependent on cooking temperatures. However, these changes were not independent of the protein source (PBMAs) or muscle type (meat). For example, cooking at 70 °C decreased the amount of sulfhydryl content in PBMAs compared to pork and beef burgers (20 µmol/g vs. 65 µmol/g protein, respectively). Conversely, when cooking temperatures (> 120 °C) were used, maillard reaction resulted in favourable sensory (appearance and flavour) attributes in both types of products, however, in the presence of sugars and the amino acid asparagine, concerns about the presence of chemical hazards such as acrylamide increased. Microwave cooking also increased WHC and oil absorption properties of plant proteins which improved the mouthfeel of PMBAs. In conclusion, there is a need to optimize in-home cooking techniques to enhance desirable textures and flavours in PBMAs which will improve consumer acceptability of these products.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".