Potential Applications of Green Technologies in Olive Oil Industry
Bibliographic record
Abstract
Conventional olive oil production methods create large amounts of waste and by-products.Most production plants do not invest in purification and utilization of those by-products.Purification or conversion methods may add value to those by-products and prevent the environmental pollution.Global trends show that "green" products and technologies are needed.Increasing environmental concerns, government measures and population drive the search for green processes to replace the conventional ones.This search is essential to achieve sustainable processing and to reduce commercial energy use (Clark, 2011).There are several applications for green technology in the olive oil industry.This chapter reviews the potential applications of major green processes such as supercritical fluid extraction, membrane technology, bioconversions and molecular distillation in the olive oil industry. Supercritical fluid technologySupercritical Fluid Technology (SFT) has received growing interest as a green technology, with extraction being the main application in the food industry.Fluids become supercritical by increasing pressure and temperature above the critical point.Supercritical fluids have liquid-like solvent power and gas-like diffusivity.These physical properties make them ideal clean solvents for extraction of lipids.Carbon dioxide (CO 2 ) is the most widely used supercritical fluid due to a lack of toxicity and flammability, low cost, wide availability, tunable solvent properties, and moderate critical temperature and pressure (31.1°C and 7.38 MPa) (Black, 1996).Because of the relatively low viscosity, high molecular diffusivity and low surface tension of the system, mass transfer is improved in supercritical CO 2 (SC-CO 2 ) in comparison to liquid organic solvents (Oliveira & Oliveira, 2000).Moreover, separation of CO 2 from the product can easily be achieved by reduction of pressure, because the products do not dissolve in CO 2 at atmospheric pressure.Another unique property of supercritical fluids is their selectivity.The density of a supercritical fluid is higher than that of a gas, making them better solvents.Extraction selectivity of supercritical fluids can be changed altering density which is done by adjusting www.intechopen.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".