Phenolic Acids in Some Cereal Grains and Their Inhibitory Effect on Starch Liquefaction and Saccharification
Bibliographic record
Abstract
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.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".