Quantification of palm oil bioactive compounds by ultra‐high‐performance supercritical fluid chromatography and chemometrics
Bibliographic record
Abstract
Abstract Lycopene, beta‐carotene, coenzyme Q10, and lutein are minor constituents of palm oil that are removed during biodiesel production to produce light‐coloured oils. With the aim to investigate the recovery of these valuable compounds, a separation method was developed to quantify carotenoids and coenzyme Q10 in palm oil by ultra‐high‐performance supercritical fluid chromatography. Due to the presence of interferents, different clean‐up procedures were evaluated; however, these approaches were ineffective and the separation method was developed without this step. The chemometric method multivariate curve resolution‐alternating least squares was employed to properly quantify lycopene, beta‐carotene, and coenzyme Q10 in the presence of interferents. Lutein was sufficiently resolved to be quantified by a univariate method. Lycopene concentration was below the limit of quantification 3.12 μg/mL (3.12 × 10 −3 kg/m 3 ). Beta‐carotene concentration was determined as being 183.48 ± 1.66 μg/mL (183.48 ± 1.66 × 10 −3 kg/m 3 ). Coenzyme Q10 concentration was lower than the limit of detection 4.22 μg/mL (4.22 × 10 −3 kg/m 3 ) and lutein concentration 9.24 μg/mL (9.24 × 10 −3 kg/m 3 ) was below the limit of quantification. The study showed the analytical challenges associated with the separation and quantification of minor constituents of a highly complex matrix such as palm oil and demonstrated that the recovery of beta‐carotene could be economically viable due to its wide range of application in industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".