Thermostability of black rice nutraceuticals during a straight-dough bread-making process
Bibliographic record
Abstract
Rice (Oryza sativa) represents an important food source worldwide, especially in many Asian countries. Among all the commercially available rice cultivars, pigmented ones are commonly known to be rich in different phytochemical compounds, such as flavonoids, carotenoids and chlorophylls. Recent studies showed that black rice, whose colour is due to the presence of several anthocyanins such as cyanidin 3-glucoside, is characterised by the highest content of many bioactive compounds among all the rice cultivars. Thanks to these natural antioxidants, the consumption of black rice significantly reduces the risk of pathologies related to oxidative stress, which are caused mostly by reactive oxygen species such as lipid peroxide and superoxide anion radicals. Moreover, it has been noted a reduction in the risk of chronic diseases, such as diabetes, obesity and cardiovascular diseases associated to an increased consumption of grain products. \nThe aim of this study was to investigate the thermostability of some of these antioxidant compounds during a straight-dough bread-making process. \nBlack rice flour and wheat Manitoba flour were firstly characterized in term of total carotenoid, phenolic, flavonoid and anthocyanin content, evaluating also their respective antioxidant activity. An experimental bread-making process was then carried out, mixing the black rice flour and the Manitoba flour in 1:2 and 1:1 ratios, respectively. A dough obtained using only black rice flour was also tested. The baked breads were immediately freeze-dried and milled. Carotenoids, phenolics, flavonoids and anthocyanins were quantified as before to evaluate their respective loss during the baking step. \nThe black rice flour showed a considerably higher content of all the nutraceuticals considered, if compared to the Manitoba flour (e.g., 6269 μg/g total phenolic in the black rice flour and 405 μg/g in the Manitoba flour). After baking, although a reasonably expected loss of these phytochemicals was detected, the bread still showed a considerable amount of most of the bioactive compounds analysed (e.g., in the black rice flour:Manitoba flour 1:2 bread, the total phenolic content was 1382 μg/g). \nThe thermostability of these nutraceuticals gives the concrete possibility to fortify not only bread, but also many other baked goods with black rice flour, maintaining most of their antioxidant and healthy properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".