Structural and functional properties of dried fish protein isolates
Bibliographic record
Abstract
Dried fish (DF) are rich in protein and widely available worldwide; however, they have long been limited to being used as a traditional food. This study investigated the structural and functional properties of dried fish protein isolates (DFPIs) extracted from seven commonly consumed DF species in Bangladesh, including both sun-dried and fermented varieties. The isolates were prepared via isoelectric precipitation. Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) analysis revealed that the DFPIs lacked intact muscle protein bands and were primarily composed of peptides of < 70 kDa. Circular dichroism spectroscopy showed extensive protein unfolding and hydrolysis, with only the Ganges River sprat DFPI retaining some ordered tertiary structure. The drying and fermentation processes significantly disrupted the secondary structure, resulting in low α-helix content and high proportions of β-sheets and random coils. Consequently, protein yield during extraction was relatively low, with a maximum of 36%. At neutral pH (7.0), DFPIs exhibited low heat-induced coagulation (maximum 23%) but showed excellent oil-holding capacity (up to 20 g/g), likely due to exposed hydrophobic groups. They also demonstrated good gelation abilities (minimum gelling concentration of 3–7%) and emulsifying properties, with Bombay duck DFPI forming stable emulsions with droplet sizes as small as 2 µm. These findings highlight the potential of DFPIs as functional ingredients in heat-processed food formulations, particularly as heat-stable emulsifiers.
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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".