The nutritional value, bioactive availability and functional properties of garlic and its related products during processing
Bibliographic record
Abstract
Garlic, a common culinary spice, is cultivated and used around the globe. Consumption of garlic and its supplements reduces the risk of diabetes and cardiovascular disease and boosts the immune system with antibacterial, antifungal, anti-aging, and anti-cancer properties. Diallyl sulfide, diallyl disulfide, triallyl trisulfide, phenolics, flavonoids, and others are the most commercially recognized active ingredients in garlic and its products. In recent years, global demand for medicinal or functional garlic has surged, introducing several products such as garlic oil, aged garlic, black garlic, and inulin into the market. Garlic processing has been demonstrated to directly impact the availability of bioactive ingredients and the functionality of products. Depending on the anticipated functional qualities, it is also recommended that one or a combination of processing techniques be deemed desirable over the others. This work describes the steps involved in processing fresh garlic into products and their physicochemical alterations during processing. Their nutritional, phytochemical, and functional properties are also reviewed. Considering the high demand for functional food, this review has been compiled to provide guidance for food producers on the industrial utilization and suitability of garlic for new product development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".