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Record W2986565871 · doi:10.3390/antiox8110527

Enhancement of Minor Ginsenosides Contents and Antioxidant Capacity of American and Canadian Ginsengs (Panax quinquefolius) by Puffing

2019· article· en· W2986565871 on OpenAlexaboutno aff
Min‐Soo Kim, Sung-Joon Jeon, So Jung Youn, Hyungjae Lee, Young‐Joon Park, Dae‐Ok Kim, Byung‐Yong Kim, Wooki Kim, Moo‐Yeol Baik

Bibliographic record

VenueAntioxidants · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGinseng Biological Effects and Applications
Canadian institutionsnot available
FundersRural Development Administration
KeywordsChemistryDPPHABTSFood scienceChromatographySaponinAntioxidantFlavonoidOrganic chemistry

Abstract

fetched live from OpenAlex

The effects of puffing on ginsenosides content and antioxidant activities of American and Canadian ginsengs, Panax quinquefolius, were investigated. American and Canadian ginsengs puffed at different pressures were extracted using 70% ethanol. Puffing formed a porous structure, inducing the efficient elution of internal compounds that resulted in significant increases in extraction yields and crude saponin content. The content of minor ginsenosides (Rg2, Rg3, compound K) increased with increasing puffing pressure, whereas that of major ginsenosides (Rg1, Re, Rf, Rb1, Rc, Rd) decreased, possibly due to their deglycosylation and pyrolysis. Furthermore, 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) radical scavenging activity, total phenolic content, total flavonoid content, amount of Maillard reaction products, and acidic polysaccharides content increased with increasing puffing pressure, but 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity did not. There was no substantial difference in the results between puffed American and Canadian ginsengs. Consequently, these results suggest that puffing can be a promising novel technology for processing P. quinquefolius to achieve higher levels of minor ginsenosides and obtain value-added products.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.227
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2019
Admission routes1
Has abstractyes

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