Incorporation of Mycelium (Pleurotus eryngii) in Pea Protein Based Low Moisture Meat Analogue: Effect on Its Physicochemical, Rehydration and Structural Properties
Bibliographic record
Abstract
The protein content of a plant-based ingredient is generally lower than its animal food counterpart, and research into novel alternative protein is required that can provide similar protein content, texture and appearance as meat. This work investigates a mycelium-based low moisture meat analogue (LMMA) approach, by incorporating 0 to 40% w/w mycelium (MY) into pea protein isolate (PPI) via extrusion using a twin-screw extruder at 140 °C die temperature, 40 rpm screw speed, and 10 rpm feeder speed (0.53–0.54 kg/h). Physicochemical, rehydration, and structural properties of LMMA were assessed. The MY incorporation led to a significant change in color attributes due to Maillard reaction during extrusion. Water solubility index and water absorption capacity increased significantly with MY addition, owing to its porous structure. Oil absorption capacity increased due to increased hydrophobic interactions post-extrusion. Protein solubility decreased initially (upto 20% w/w MY), and increased afterwards, while the water holding capacity (WHC) and volumetric expansion ratio (VER) of LMMA enhanced with MY addition upto 30% w/w. Conversely, WHC and VER decreased for 40% w/w which was verified with the microstructure and FTIR analysis. Overall, MY (30% w/w) in PPI produced a fibrous and porous LMMA, showing future potential with an increasingly plant-based product market and decreasing carbon footprint of food production activities.
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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 | 0.000 |
| 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".