Sustainable Protein Processing of Flaxseed By‐Product: Nutritional Quality and Functional Properties
Bibliographic record
Abstract
Abstract Overcoming environmental, food security, and growing global population challenges requires exploring sustainable protein production. Promisingly, agro‐industrial by‐products emerge as alternative sources. This study hypothesizes that processing methods can significantly improve the nutritional quality and functional properties of flaxseed meal (FSM) by reducing anti‐nutritional factors and enhancing protein digestibility. This study assessed the chemical and nutritional quality of FSM after oil extraction, focusing on its composition, anti‐nutritional factors, in vitro protein digestibility (IVPD), and amino acid score. Different processes and processing parameters were assessed based on an experimental design. A central composite design supported evaluating the impact of conventional heating, microwave, and ultrasound on the nutritional quality of this meal. Unprocessed FSM exhibited a protein content of 39% and an IVPD of 88%. Through processing, IVPD was elevated to 95% for the conventional heating, with 87.8°C, 37 min, and pH 8.0 as the best conditions. Protein solubility of the FSM significantly improved at pH 8 and 9 (up to 98%). Thermal processing proved effective in completely inactivating phytic acid, while ultrasound reduced trypsin inhibitory activity by 50%. Lysine was the first‐limiting amino acid (AAS = 86%–90%) for all processes and parameters. Processing also enhanced the functional aspects, affirming that treated FSMs represent potential protein sources for the food industry due to their high nutritional quality and viable improvement due to processing.
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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".