Effects of Various Cooking Methods on Content, Oxygen Radical Absorbance Capacity, and Bioaccessibility of Caffeoylquinic Acids in Ligularia fischeri (Ledeb.) Turcz, Gom-Chi
Bibliographic record
Abstract
We hypothesized that cooking methods generally applied to Gom-chi preparation such as blanching, pan frying, and microwaving could change content, oxygen radical absorbance capacity (ORAC), and bioaccessibility of caffeoylquinic acids (CQAs). Mono-, di-, and tri-CQAs were identified and quantified by using an ultra-performance liquid chromatograph-photodiode array detector. In vitro biomimick system was performed for assessing the bioaccessibility of CQAs. The free radical scavenging capacity was measured by ORAC assay, expressed as trolox equivalents (TE). The amount of 5-CQA, 4-CQA, 3-CQA, and 3,4-di-CQA were 136.72, 2144.44, 16.81, and 421.93 ?g, respectively, in g of fresh Gom-chi. The highest amount of CQAs and TE was observed in microwaving for 3 min and pan frying for 3 min, respectively. The ranking of total CQAs were found in the order of microwaving 3 min (239.96 ?g/g) > microwaving for 2 min (206.11 ?g/g) > blanching for 3 min (191.94 ?g/g) > blanching for 5 min (180.32 ?g/g) > pan for frying 5 min (161.20 ?g/g) > pan frying for 3 min (115.83 ?g/g). The bioaccessibility of total CQAs were 16.42, 17.64, 14.39, 13.29, and 12.43 (%) in fresh, blanching for 3 min, blanching for 5 min, pan frying for 3 min, and pan frying for 5 min, respectively. Among the cooking methods, blanching for 3 min had the highest amount of CQAs after digestion. Our results suggest that blanching 3 min could be a suggested optimal cooking method for Gom-chi to improve CQAs absorption.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".