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Record W2000008675 · doi:10.1002/jsfa.5582

Qualitative and quantitative analysis of the major phenolic compounds as antioxidants in barley and flaxseed hulls using HPLC/MS/MS

2012· article· en· W2000008675 on OpenAlexaff
Meili Hao, Trust Beta

Bibliographic record

VenueJournal of the Science of Food and Agriculture · 2012
Typearticle
Languageen
FieldMedicine
TopicPhytoestrogen effects and research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsChemistryCoumaric acidFerulic acidVanillinFood sciencePhenolic acidGlucosidePhytochemicalChromatographyVanillic acidHigh-performance liquid chromatographyPhenolsAntioxidantOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Both qualitative and quantitative analyses of the major phenolic compounds in barley and flaxseed hulls were conducted using reverse phase high-performance liquid chromatography coupled with photodiode array detection and quadrupole time-of-flight mass spectrometry. RESULTS: Ferulic acid, p-coumaric acid, vanillic acid and vanillin were identified and quantified in four barley hull samples. Four ferulate dehydrodimers were also detected. The phenolic compounds of flaxseed hull were distinct from those of barley hull. Three flaxseed hull samples varied significantly (P < 0.05) in their contents of secoisolariciresinol diglucoside (16.38-33.92 g kg(-1) ), coumaric acid glucoside (35.68-49.22 g kg(-1) ) and ferulic acid glucoside (5.07-15.23 g kg(-1) ). The phytochemical profiles of co-extracts featured the major phenolic compounds from both barley and flaxseed hulls. The total phenolic content and 2,2-diphenyl-1-picrylhydrazyl radical-scavenging capacity varied significantly (P < 0.05) among different varieties of flaxseed and barley hulls. CONCLUSION: As agricultural by-products, barley and flaxseed hulls may be utilised as potential sources of functional food ingredients through extraction and concentration of the phytochemicals identified above.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.042
GPT teacher head0.374
Teacher spread0.332 · 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

Citations60
Published2012
Admission routes1
Has abstractyes

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