Decoding the Duality of Antinutrients: Assessing the Impact of Protein Extraction Methods on Plant-Based Protein Sources
Bibliographic record
Abstract
This review aims to provide an updated overview of the effects of protein extraction/recovery on antinutritional factors (ANFs) in plant protein ingredients, such as protein-rich fractions, protein concentrates, and isolates. ANFs mainly include lectins, trypsin inhibitors, phytic acid, phenolic compounds, oxalates, saponins, tannins, and cyanogenic glycosides. The current technologies used to recover proteins (e.g., wet extraction, dry fractionation) and novel technologies (e.g., membrane processing) are included in this review. The mechanisms involved during protein extraction/recovery that may enhance or decrease the ANF content in plant protein ingredients are discussed. However, studies on the effects of protein extraction/recovery on specific ANFs are still scarce, especially for novel technologies such as ultrasound- and microwave-assisted extraction and membrane processing. Although the negative effects of ANFs on protein digestibility and the overall absorption of plant proteins and other nutrients are a health concern, it is also important to highlight the potential positive effects of ANFs. This is particularly relevant given the rise of novel protein ingredients in the market and the potential presence or absence of these factors and their effects on consumers' health.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 0.001 |
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".