Bibliographic record
Abstract
The fruit of the mangosteen tree (Garcinia mangostana L.) has gained increasing acceptance as a distinctively flavored commodity that is also a rich source of nutrients and health-promoting phytochemicals, including prenylated and oxygeneated xanthones, flavonoids, flavanols, tannins, anthocyanins, ascorbic acid, carotenoids, and other bioactive compounds. The short shelf-life of fresh mangosteen fruit hinders distribution from producing regions in tropical or sub-tropical zones to distant markets. Consequently, several processing techniques are applied in the manufacture of food products that exploit the unique gustatory and nutritional properties of mangosteen. The present review summarizes the chemical properties of mangosteen and processing technologies applied in the manufacture of minimally processed (fresh- cut), frozen, canned, juiced, fermented (wine), dried, and miscellaneous other food products derived from this unique tropical fruit. Practical applications The review summarizes current processing technologies applied in the manufacture of food products from mangosteen fruit, including minimally processed (fresh-cut), frozen, canned, juiced, fermented (wine), dried, and miscellaneous other food products. It is intended to provide a single source of information to guide research and development of food products formulated with mangosteen as a primary ingredient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".