Effects of preheating and drying methods on pyridoxine, phenolic compounds, ginkgolic acids, and antioxidant capacity of <i>Ginkgo biloba</i> nuts
Bibliographic record
Abstract
Abstract Although ginkgo nuts are very nutritious and loaded with numerous bioactive compounds, the nuts contain significant levels of unwanted compounds (ginkolic acids) which are toxic to consumption. To reduce or eliminate these toxic compounds without impacting the nutritional value and the bioactivity of the final product, an appropriate processing technology is needed. Thus, the effect of preheating (90 and 120°C) prior to drying (freeze drying: FD, hot air drying: HAD, and HAD in tandem with FD: HAD‐FD) was evaluated on ginkgolic acids, pyridoxine analogues, phenolic compounds, and antioxidant properties of ginkgo nuts. Our results pointed out a significant decrease (below 50%) of ginkgolic acids in ginkgo nuts samples processed at 90°C compared to the control. The major compounds found after treatments were respectively, kaempferol (36.66‐354.38 µg/g), quercetin (9.04‐183.71 µg/g), and caffeic acid (19.66‐106.88 µg/g). Principal component analysis (PCA) revealed that preheating at 90°C prior to HAD‐FD would be a proper and reasonable approach for preserving the bioactive compounds and antioxidant capacity of ginkgo nuts (EC 50 ranged from 2.25 to 4.60 mg/mL) while significantly reducing their content in toxic compounds.
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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) | 0.001 | 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 | 0.001 |
| 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".