Comparative Studies on Composition and Distribution of Phenolic Acids in Cereal Grain Botanical Fractions
Bibliographic record
Abstract
ABSTRACT The phenolic acid composition and concentration of four manually separated fractions (pericarp, aleurone layer, germ, and endosperm fractions) as well as whole grains of yellow corn, wheat, barley, and oats were analyzed by HPLC‐MS/MS following microwave‐assisted alkaline aqueous extraction. Phenolic acid compositions in whole grains and their fractions were similar, with minor differences among the grain fractions. Significant differences (P < 0.05), however, were observed in phenolic acid concentrations among cereal types, within cereal varieties, and among grain fractions, with yellow corn exhibiting the highest values. The concentrations of p‐coumaric and syringic acid in the pericarp were 10‐ to 15‐fold and 6‐ to 10‐fold higher, respectively, in yellow corn than in wheat, barley, and oats. In the aleurone layer, sinapic and vanillic acids in yellow corn were about 8‐ and 30‐fold more than in wheat. The germ fraction of wheat had 1.8 times more syringic acid than yellow corn germ. Grain fractions, excluding endosperm, had enhanced levels of phenolic acids compared with whole grain. Sinapic acid was more concentrated in the pericarp and germ of wheat, whereas isoferulic acid was concentrated in the germ of purple barley. Syringic and vanillic acids were concentrated in the pericarp and sinapic acid in the aleurone layer of yellow corn. These findings are important in understanding the composition and distribution of phenolic acids, and they act as a guide in identification of grain fractions for use as food ingredients. In addition, yellow corn fractions (aleurone and pericarp) may be potential alternative phenolic‐rich functional food ingredients in grain‐based food 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 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 | 0.001 |
| 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".