Distribution of cereal phytochemicals and micronutrients in whole grains: A review of nutraceutical, industrial, and agricultural implications
Bibliographic record
Abstract
Abstract Background and Objectives There is mounting evidence that whole cereal grains are a source of many micronutrients and phytochemicals that confer several health benefits. This work aimed to investigate the distribution patterns and related implications of selected bioactive compounds from various cereal grains and discuss the techniques used to study their distribution. Findings Hand dissection and imaging techniques are the methods used to locate bioactive compounds in whole grains with high accuracy. Pearling and milling are methods of industrial importance. Phenolic compounds are concentrated in cereal bran, whereas the germ is rich in carotenoids and tocols (particularly tocopherols). Knowing the distribution pattern of compounds allows a better understanding of their bioaccessibility and associated bioactivity, as well as developing means to recover them and enhance their occurrence in the aleurone layer. Conclusions Gradient patterns exist in the distribution of phytochemicals and micronutrients derived from cereal grains. This knowledge can be translated into a number of purposeful and practical applications. Significance and Novelty This study employed a comparative approach to examine the repartition of various compounds in whole grains of assorted cereals with an emphasis on minor crops. The implications are relatable and applicable in diverse sectors to ultimately improve the well‐being of cereal consumers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".