Recent trends in biological extraction of chitin from marine shell wastes: a review
Bibliographic record
Abstract
The natural biopolymer chitin and its deacetylated product chitosan are widely used in innumerable applications ranging from biomedicine, pharmaceuticals, food, agriculture and personal care products to environmental sector. The abundant and renewable marine processing wastes are commercially exploited for the extraction of chitin. However, the traditional chitin extraction processes employ harsh chemicals at elevated temperatures for a prolonged time which can harm its physico-chemical properties and are also held responsible for the deterioration of environmental health. In view of this, green extraction methods are increasingly gaining popularity due to their environmentally friendly nature. The bioextraction of chitin from crustacean shell wastes has been increasingly researched at the laboratory scale. However, the bioextraction of chitin is not currently exploited to its maximum potential on the commercial level. Bioextraction of chitin is emerging as a green, cleaner, eco-friendly and economical process. Specifically in the chitin extraction, microorganisms-mediated fermentation processes are highly desirable due to easy handling, simplicity, rapidity, controllability through optimization of process parameters, ambient temperature and negligible solvent consumption, thus reducing environmental impact and costs. Although, chitin production from crustacean shell waste through biological means is still at its early stage of development, it is undergoing rapid progress in recent years and showing a promising prospect. Driven by reduced energy, wastewater or solvent, advances in biological extraction of chitin along with valuable by-products will have high economic and environmental impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".