Developments in the Dry Fractionation of Plant Components: A Review
Bibliographic record
Abstract
Over the years, pulses and cereals have been identified as promising sources of plant proteins. The intensive production of these crops and concerns about food security and malnutrition worldwide have intensified research into their separation. While wet extraction remains the standard protein isolation method, the search for more sustainable extraction methods is still ongoing. Two dry fractionation techniques, air classification and tribo-electrostatic separation, have been discussed in this review. This review highlights the design aspects of air classifiers including the cut-off point and flow rate, and for electrostatic separators, factors such as charger materials, the nature of the flow in charger tubes, and the strength of the electric field potential have been discussed in detail. Our analysis revealed that cascading the two techniques should help enhance the concentration and purity of the separated fractions. While limitations such as low purity and low yield exist, current research studies are focused on overcoming such drawbacks. Dry fractionation exhibits potential as a sustainable processing method while also preserving the native functionality of the proteins, making it easier to incorporate the fractions in commercial scale processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".