The influence of thermal treatments on the antioxidant activity and colour of chokeberry (aronia menocarpa) extract
Bibliographic record
Abstract
Natural ingredients are an important trend in the present day food industry since manufacturers are demanding natural food dyes originating from concentrates of plant origin. However, if the use of plant extracts as technological additives is intended, their stability should be evaluated. This paper presents research on the stability of a 50% ethanolic extract of chokeberry (Aronia melanocarpa) from the Republic of Moldova. The extract was subjected to various thermal regimes, i.e. -2 oC for 12 hours; 4 oC for 12 hours; 40 oC for 15 minutes, 60 oC for 15 minutes, 80 oC for 15 minutes and 100 oC for 2 minutes, after which the antioxidant activity and the colour parameters (CIELab) were evaluated. Three sets of extracts were also stored for 2 weeks at -2 oC; 4 oC; and 25-30 oC and afterwards the parameters mentioned above were measured once again. Furthermore, total content of polyphenols was determined using the Folin-Ciocalteu method. The antioxidant activity was stable at temperatures under 0oC and temperatures as high as 100oC, as well as during storage at -2oC, 4oC and 25-30oC. High temperatures however decreased the value of luminosity, and increased the redness, the yellowness and the chroma value which reached a maximum value of 55.66 after the extract was subjected to 100oC for 2 minutes. Storage at room temperature significantly affected the luminosity of the extract by increasing its value from 42.36 to 50.22. For most treatments, the colour was stable and was not significantly modified.
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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 | 0.001 |
| 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".