Exploitation alternatives of olive mill wastewater: production of value-added compounds useful for industry and agriculture
Bibliographic record
Abstract
Countries producing olive oil generate a considerable amount of olive mill wastewater (OMWW), one of the most harmful agro-industrial effluents with a powerful polluting capacity. In fact, owing to its high pollution load, this effluent is extremely toxic to the whole soil-air-water ecosystem as well as to the living organisms inhabiting it (i.e., plants, animals, aquatic organisms, microorganisms, etc.). Currently, OMWW is discarded but since it includes carbohydrates, organic acids and mineral nutrients, as well as elevated contents of phenolics and other natural antioxidants compounds, it could be considered as a potential source of high value-added natural products. Therefore, the valorization of different waste streams including OMWW into fine biochemicals and the recovery of valuable metabolites via biotechnological processes is probably the main challenge faced by the olive oil industry. In light of that, the aim of the present review article is to summarize the state-of-the-art in relation to the exploitation possibilities and the use of OMWW to generate added-value compounds of great significance for the biofuel, pharmaceutical, cosmetic, chemical, food, and agriculture industries. Valorization of this significant waste steam in particular through a biorefinery platform could substantially enhance the environmental sustainability aspects of the whole industry while simultaneously contributing to the improvement of its economic viability.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".