Enhancing the nutritional value of sorghum grains bred for northern Europe through processing: A perspective on phenolic bioaccessibility and protein digestibility
Bibliographic record
Abstract
The effect of dehulling and cooking on the in vitro digestibility, and phenolic profiles was evaluated for four Dutch sorghum varieties (HD7 and HD19, Sorghum bicolor; and HD100 and HD101 Sorghum nigricans) bred in the Netherlands. Protein content ranged from 9 to 14 % and grains with black pericarp were more resistant to dehulling. Essential amino acids composition analysis showed that the lysine chemical score (∼0.6) was lower than that required for adults. Phenolic profiling by UHPLC-ESI-QTOF/MS allowed annotaion of 219 phenolic compounds, with flavonoids as the most representative class (91 %). Dehulling and genotype had stronger influence on the phenolic profiles than cooking; however, hydrothermal treatment was essential for the depolymerization of proanthocyanidin dimers and trimers. The combination of dehulling and boiling improved in vitro protein digestibility and increased in vitro bioaccessibility of key phenolic compounds. These processes are effective for developing high-quality sorghum-based products using Dutch varieties.
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 | 0.000 | 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".