Effect of processing on the antioxidant activities of porridges and Pittu prepared from finger millets (Eleusine coracana)
Bibliographic record
Abstract
Abstract Finger millets are important coarse grains that exhibit high levels of phenolic compounds and antioxidant activities among other cereals. Grains are subjected to a number of thermal treatments and this could affect the contents and bioactivities of phenolic compounds. The aim of the present study was to determine the phenolic contents and antioxidant activities of finger millet flour and foods, namely porridges andPittusubjected to several thermal treatments such as roasting, parboiling, and parboiled roasting of flour and open boiling and steaming of foods prepared using flour. The effect of the addition of spices on phenolic content and antioxidant activities to open boiled foods was determined. Antioxidant activities of phenolic extracts obtained from finger millet flour and foods were studied for their total phenolic, flavonoids and proanthocyanidins contents, radical scavenging capacities, reducing power, ferrous ion chelating capacity, and antioxidant activity in aβ-carotene/linoleate emulsion. The roasted flour and foods had higher total phenolic content (TPC) than other counterparts and ranged from 21.58 to 28.63 µmol ferulic acid equivalents/g dry matter. All flour and food preparations exhibited effective inhibition of radicals, reducing power, ferrous ion chelating activity, and antioxidant activity in theβ-carotene/linoleate emulsion and the degree of activity depended on the processing conditions adopted in food preparations. Foods prepared using steaming showed lower phenolic content and antioxidant activities in general than those of open-boiled counterparts. The addition of spices, namely garlic and cinnamon improved the phenolic contents and antioxidant activities of open-boiled porridges. The knowledge generated from this study may help to exploit the use of finger millets as a functional food ingredient to promote health and wellness. Graphical abstract
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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 | 0.000 | 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) | 0.002 | 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".