Physico-chemical analysis of newly prepared prebiotic chocolates by using Galacto Oligosaccharides (GOS)
Bibliographic record
Abstract
In India, chocolate is very famous mood freshening sweet food product specially for children. But high consumption of chocolate may be harmful for human health, because most of the chocolates contain high fat. This research has been done to increase the health benefits of chocolate by using cocoa and Galacto Oligosaccharides (GOS). Many researchers have shown their research that GOS is very beneficial for human health such as it helps to lower down the cholesterol levels in the blood, to prevent colon cancer, and helps to improve mineral absorption in human body. Basically, GOS is a type of prebiotic e.g., food for probiotic bacteria. Prebiotics are non-digestible ingredients that benefit the host by boosting the growth and activity of one or a few bacterial species that are already present in the colon. It was also studied by many researchers that cocoa powder contains a lot of caffeine, flavanols, which are polyphenolic compounds. Flavanols, particularly epicatechin monomer and oligomer cocoa flavanols have been linked to a number of health advantages, including boosting nitric oxide synthase, enhancing blood flow and arterial flexibility, lowering blood pressure and platelet aggregation, and reducing inflammation. The main objectives of this research are to develop the formulation of prebiotic chocolate and to evaluate the physicochemical parameters of the newly prepared chocolate. After physico-chemical analysis of carbohydrate, fat, crude fibre and pH, it was found that both the newly prepared prebiotic enriched chocolates (T1 and T2) were significantly different (p≤0.01) from control (T0) and after analysis of protein content, it was also found that both the newly prepared prebiotic enriched chocolates (T1 and T2) were insignificantly different (p≤0.01) from control (T0).
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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 | 0.001 | 0.001 |
| 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".