Supercritical Carbon Dioxide Extraction of Polyphenols from Pomegranate (Punica granatum L.) Leaves: Chemical Composition, Economic Evaluation and Chemometric Approach
Bibliographic record
Abstract
The increasing demand for high-quality products and economically and environmentally friendly technologies, as well as restrictive legislative actions, has stimulated scientific research on the extraction, purification and identification of bioactive compounds from natural sources. Pomegranate (Punica granatum L.) is commonly used in traditional medicine due to its pharmacological properties, such as its anti-inflammatory, antihepatotoxicity, anti-lipoperoxidation, antidiabetic, anti-cancer and antimicrobial activities. The use of industrial residues as sources of bioactive compounds has emerged as an economically viable solution to the problem of solid waste treatment. In this context, this work aimed to evaluate the SC-CO2 extraction of polyphenols from pomegranate leaves, evaluating the influence of temperature (40 and 50°C) and pressure (10-30 MPa) on extraction yield (EY), total phenolic content (TPC), antioxidant activity (AA) and the cost of manufacturing (COM) of the extracts. Principal component analysis (PCA) was used to reduce the dimensionality of multivariate data, making the visualization more straightforward and manageable. A high EY and TPC and low COM were obtained at the most effective operational conditions of 50°C and 30 MPa. The lack of correlation between EY-AA and TPC-AA indicated the coextraction of non-phenolic compounds. This assumption was corroborated by GC-MS analysis, which showed high levels of eicosanol, squalene, linoleic acid and tocols. Even though SC-CO2 extraction resulted in a high TPC (257-389 mg.g-1) compared to the literature data, the low EY (0.21-0.67 %) and non-phenolic presence suggest that SC-CO2 extraction may be a good purification pretreatment for the removal of non-polyphenolic compounds prior to further polyphenol extraction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".