Superfruits: Phytochemicals, antioxidant efficacies, and health effects – A comprehensive review
Bibliographic record
Abstract
The term "superfruit" has gained increasing usage and attention recently with the marketing strategy to promote the extraordinary health benefits of some exotic fruits, which may not have worldwide popularity. This has led to many studies with the identification and quantification of various groups of phytochemicals. This contribution discusses phytochemical compositions, antioxidant efficacies, and potential health benefits of the main superfruits such as açai, acerola, camu-camu, goji berry, jaboticaba, jambolão, maqui, noni, and pitanga. Novel product formulations, safety aspects, and future perspectives of these superfruits have also been covered. Research findings from the existing literature published within the last 10 years have been compiled and summarized. These superfruits having numerous phytochemicals (phenolic acids, flavonoids, proanthocyanidins, iridoids, coumarins, hydrolysable tannins, carotenoids, and anthocyanins) together with their corresponding antioxidant activities, have increasingly been utilized. Hence, these superfruits can be considered as a valuable source of functional foods due to the phytochemical compositions and their corresponding antioxidant activities. The phytochemicals from superfruits are bioaccessible and bioavailable in humans with promising health benefits. More well-designed human explorative studies are needed to validate the health benefits of these superfruits.
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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 | 0.001 |
| 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) | 0.004 | 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".