Investigating the microstructural and cryoprotective superiority of fructooligosaccharides and sugar alcohols for maintaining peeled white shrimp (Litopenaeus vannamei) quality after repeated freeze-thaw cycles
Bibliographic record
Abstract
For the food industry, a significant consideration is the reduction in the use of phosphate and NaCl in frozen shrimp. This study, therefore, investigated the impact of various cryoprotectant treatments on the physicochemical, structural, and visual quality of shrimp after repeated freeze-thaw cycles. Shrimp samples were subjected to 1, 3, and 5 freeze-thaw cycles and treated with different cryoprotectants, including sorbitol, xylitol, fructooligosaccharides (FOS), trisodium citrate, and NaCl, compared to DI water (control). The quality parameters evaluated included thawing loss, water-holding capacity, cooking yield and loss, protein content, pH, lipid oxidation, texture, microstructure, and color parameters. The findings indicated that repeated freeze-thaw cycles significantly increased moisture and protein loss, weakened texture and color stability, and led to the disruption of muscle structure. Saccharide-based cryoprotectants, particularly FOS, effectively reduced quality deterioration by stabilizing muscle proteins, minimizing water release, limiting lipid oxidation, and preserving microstructure and pigment integrity. These results demonstrate that FOS offers promising cryoprotective benefits, with potential applications in clean-label seafood preservation and enhanced product stability during cold chain distribution. • FOS, sorbitol, and xylitol exhibited cryoprotective activity comparable to that of trisodium citrate and NaCl. • FOS help preserve muscle integrity and reduce protein denaturation. • FOS-treated samples exhibited a tightly packed fiber arrangement and minimal structural change. • Sorbitol, xylitol, and FOS effectively preserve the raw and cooked appearance of shrimp across repeated freeze-thaw cycles.
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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) | 0.000 | 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".