Acid hydrolysis and microwave digestion enhanced protein extraction from red seaweed Palmaria palmata
Bibliographic record
Abstract
Palmaria palmata proteins have limited dietary use due to ineffective protein extraction methods. This study explores pretreatments, including homogenization, ultrasonication, microwave digestion, surfactant addition, acid hydrolysis, and viscozyme hydrolysis, before conventional alkaline solubilization and acidic precipitation to improve protein extraction yield and purity. The results showed that microwave digestion (500 W, 30 min) and acid hydrolysis (2 M H 2 SO 4 , 95 °C, 2 h) significantly increased final yield from 10.4 ± 0.4 % to 47.6 ± 3.9 % and 46.5 ± 3.4 %, respectively. Specifically, alkaline solubilization yields reached 99.7 ± 3.7 % (microwave) and 99.2 ± 3.2 % (acid hydrolysis) ( p < 0.05). Protein purity reached 89.8 ± 1.1 % with microwave digestion and 65.3 ± 1.2 % with acid hydrolysis. The protein yield increment can be attributed to effective cell wall disruption, as evidenced by SEM imaging and CLSM imaging. SDS-PAGE showed that the proteins resulting from microwave and acid hydrolysis have molecular weights below 10 kDa. This study developed effective methods with potential for scaling up to enhance protein extraction from Palmaria palmata . • Multiple pre-treatments combined with pH-shift protein extraction were investigated. • Microwave digestion and acid hydrolysis increase the protein yield from 10.4 % to 47.6 % and 46.5 %. • Protein purity reached 89.8 % with microwave digestion and 65.3 % (dw) with acid hydrolysis. • The enhanced yields are attributed to cell wall disruption, protein hydrolysis, and disaggregation. • Extracted proteins are rich in fractions below 10 kDa.
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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".