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Record W2006602985 · doi:10.1021/jf3000482

Phenolic Acids in Some Cereal Grains and Their Inhibitory Effect on Starch Liquefaction and Saccharification

2012· article· en· W2006602985 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2012
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChemistryHydrolysisPhenolic acidGallic acidFerulic acidTriticaleStarchFood sciencep-Coumaric acidCoumaric acidHydroxybenzoic acidSyringic acidProtocatechuic acidCaffeic acidOrganic chemistryChromatographyBotanyBiology

Abstract

fetched live from OpenAlex

The presence of phenolic acids in cereal grain is thought to influence starch hydrolysis during liquefaction and saccharification of grain flours in the bioethanol industry. As a basis for remodeling starch hydrolysis systems and understanding inhibition mechanisms, the composition and concentration of phenolic acids in whole grain flours of triticale, wheat, barley, and corn were analyzed by high-performance liquid chromatography. The total phenolic acid contents (sum of nine phenolic acids) in the four grains were 1.14, 1.70, 0.90, and 1.25 mg/g, respectively, with more than 90% found in the bound form. Ferulic, coumaric, and protocatechuic acids were the major phenolic acids in triticale and wheat. Gallic acid was also rich in triticale. Ferulic, coumaric, hydroxybenzoic, and gallic acids were predominant in barley. In corn, ferulic, coumaric, gallic, and syringic acids were abundant. On the basis of these profiles, pure phenolic acids were added individually and collectively to isolated starches at amounts either equivalent to or 3 times those in the whole grains for hydrolysis. The degree of starch hydrolysis with α-amylase and amyloglucosidase decreased up to 8% when individual phenolic acids were present in cooked starch slurry. The decreases were more pronounced when phenolic acids were added collectively (4-5% with α-amylase and 9-13% with sequential α-amylase and amyloglucosidase). The study of a phenolic acid-starch-enzyme model system indicated that the interactions of phenolic acid-enzyme and phenolic acid-starch significantly contributed to the inhibitory effect of starch hydrolysis. Heating facilitated the interactions. Phenolic acids thus play a significant role in the resistance of starch to enzyme and/or the loss of enzyme activity during starch hydrolysis.

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.

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 score0.248
Threshold uncertainty score0.247

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 head0.013
GPT teacher head0.219
Teacher spread0.206 · 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