Profiling of Phenolic Compounds in Sprouted Common Beans and Bambara Groundnuts
Bibliographic record
Abstract
Consumption of sprouted legumes is a popular trend at household level in Africa at the moment. This study investigated the effect of sprouting on the phenolic phytochemicals of the red bambara groundnuts and red beans. Plant-derived phenolic compounds are important as antioxidants in our diet. Phenolic phytochemical profiling was performed using HPLC-PDA-ESI-MS and Folin Ciocalteu assay. There were noticeable changes in the phenolic profiles due to sprouting of the two legumes. The total polyphenol content increased by 1.3-fold and 3-fold after sprouting in red bambara groundnuts and red beans respectively. The HPLC-PDA-ESI-MS profiling of the methanolic extracts of the sprouts revealed new emerging compounds. In red bambara groundnuts, eleven new compounds emerged. The new compounds identified include caffeic acid hexoside, resveratrol glucoside, a caffeic acid derivative, naringenin and kaempferol glucoside. In red beans, eight new compounds emerged. Catechin glucoside, quercetin-3-O-glucoside, quercetin-3-rutinoside, luteolin hexoside, quercetin glucoside acylated and p-coumaric acid hexoside were new compounds identified. Sprouting therefore enhances the polyphenolic profiles of the two legumes.
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 | 0.000 |
| 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) | 0.000 | 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".