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Porosity Changes and Retention of Ginsenosides in North American Ginseng Root Using Different Dehydration Processes

2010· article· en· W2140260782 on OpenAlexafffundabout
Monica Purnama, Parastoo Yaghmaee, T.D. Durance, David D. Kitts

Bibliographic record

VenueJournal of Food Science · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGinseng Biological Effects and Applications
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaAdvanced Foods and Materials Network
KeywordsGinsengGinsenosideAmerican ginsengChemistryPorosityDehydrationSignificant differenceWater contentAnimal scienceBiologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

Air drying (AD), freeze-drying (FD), and vacuum-microwave drying (VMD) were applied to fresh North American ginseng roots to evaluate the effect of different drying techniques on pore characteristics and the subsequent recovery of ginsenoside content. FD ginseng root produced the lowest reductions in both total moisture content and water activity (P < 0.05), with no differences noted between Ontario or British Columbia ginseng. Ginseng roots from Ontario and British Columbia sources were therefore pooled to conduct the root porosity and ginsenoside measurements. Among samples, FD ginseng obtained the highest total porosity followed by VMD and AD, respectively (P < 0.05). All dehydrated samples had a porous structure with sizes that ranged from 0.002 μm to 172 μm, dominated by macropores (>1.5 μm). Pore characteristics of dried ginseng root were shown to affect recovery of ginsenosides, with the general trend being an increase in total porosity resulting in an increase in total ginsenoside recovered. High performance liquid chromatography results obtained on specific ginsenosides showed that AD of ginseng root resulted in the lowest recovery of total ginsenosides, most notably, Rg1 and Rb1, followed by VMD and FD, respectively. There was no specific difference in total ginsenoside recovery from roots dried at increasing power of VMD.

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 score0.383
Threshold uncertainty score0.147

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 head0.019
GPT teacher head0.271
Teacher spread0.252 · 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

Citations7
Published2010
Admission routes3
Has abstractyes

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