The Effect of Pre-Treatment and the Drying Method on the Nutritional and Bioactive Composition of Sea Cucumbers—A Review
Bibliographic record
Abstract
Sea cucumbers are well demarcated for their valuable role in the food, pharmaceutical, nutraceutical, and cosmeceutical sectors. The demand for well-processed dried sea cucumber retaining quality is prioritized by local markets and industries. There are several techniques for the pre-processing of fresh sea cucumbers, including traditional and modern methods, such as salting, boiling, high-pressure processing, high-pressure steaming, and vacuum cooking, among others, in order to inactivate enzymes and microbial attacks. Further, pre-treated sea cucumbers require post-processing before human consumption, transportation, or industry uses such as hot air, freeze, cabinet, sun, or smoke drying. However, despite the ease, traditional processing is associated with several challenges hampering the quality of processed products. For instance, due to high temperatures in boiling and drying, there is a higher chance of disrupting valuable nutrients, resulting in low-quality products. Therefore, the integration of traditional and modern methods is a crucial approach to optimizing sea cucumber processing to obtain valuable products with high nutritional values and retain bioactive compounds. The value of dried sea cucumbers relies not only on species and nutritional value but also on the processing methods in terms of retaining sensory attributes, including colour, appearance, texture, taste, and odour. Therefore, this review, for the first time, provides insight into different pre- and post-treatments, their perspective, challenges, and how these methods can be optimized for industry use to obtain better-quality products and achieve economic gains from sea cucumber.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".