Selective Enhancement of Caffeoylquinic Acid Derivative via UV Irradiation and Validation of Analytical Method in the Aerial <i>Aster</i> × <i>chusanensis</i> Y. S. Lim
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Aster × chusanensis Y.S.Lim is equipped with mass plant growth strategies that can potentially develop into functional foods. A. chusanensis was grown on a vertical farm until it was budding. The plants were transported to a growth chamber equipped with ultraviolet (UV)-A and UV-B lights and irradiated for 48 h. The base peak intensity (BPI) of the A. chusanensis control displayed eight predominant metabolites, namely, 3-O-caffeoylquinic acid, rutin, 3,4-di-O-caffeoylquinic acid, 3,5-di-O-caffeoylquinic acid, biorobin, luteolin-7-O-β-glucoside, 4,5-di-O-caffeoylquinic acid, and luteolin, as identified using LC-Q-TOF/MS analysis. UV-A-irradiated A. chusanensis showed an increase in the content of caffeoylquinic acid (CQA) derivatives (peaks 1, 3, 4, and 7). Thus, CQA-enhanced A. chusanensis, treated with UV-A irradiation, was used to develop analytical validation methods using HPLC-DAD. Quantitative analysis of the CQA derivatives was conducted based on the developed analytical method. The requirements for specificity, linearity, accuracy, and precision were met in accordance with the Korea Food and Drug Administration (KFDA) and the Association of Official Agricultural Chemists (AOAC) guidelines. The total contents of CQA derivatives in A. chusanensis were improved by ∼2.2 times (from 17,081 to 37,243 μg/g) following the UV-A irradiation treatment.
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) | 0.000 | 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 | 0.000 |
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".