Separation and Purification of ω-6 Linoleic Acid from Crude Tall Oil
Bibliographic record
Abstract
Crude tall oil (CTO) is the third largest by-product at kraft pulp and paper mills. Due the large presence of value-added fatty and resin acids, CTO has a huge valorization potential as a biobased, readily available, non-food, and low-cost biorefinery feedstock. The objective of this work was to present a method for the isolation of high-value linoleic acid (LA), an omega (ω)-6 essential fatty acid, from CTO using a combination of pretreatment, fractionation, and purification techniques. Following the distillation of CTO to separate the tall oil fatty acids (TOFAs) from CTO, LA was isolated and purified from TOFAs by urea complexation (UC) and low-temperature crystallization (LTC) in the temperature range between −7 and −15 °C. The crystallization yield of LA from CTO in that range was 7.8 w/w at 95.2% purity, with 3.8% w/w of ω-6 γ-linolenic acid (GLA) and 1.0% w/w of ω-3 α-linolenic (ALA) present as contaminants. This is the first report on the isolation of LA from CTO. The approach presented here can be applied to recover other valuable fatty acids. Furthermore, once the targeted fatty acid(s) are isolated, the rest of the TOFAs can be utilized for the production of biodiesel, biobased surfactants, or other valuable bioproducts.
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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) | 0.001 | 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".