Effects of Production and Processing Factors on Major Fruit and Vegetable Antioxidants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT: Public awareness of the purported health benefits of dietary antioxidants has increased the demand for fruit and vegetable products with recognized and improved antioxidant quality and has created new opportunities for the horticulture and food industry to improve fruit and vegetable quality by enhancing antioxidant content. This review describes the production and processing factors that influence the content of the major fruit and vegetable antioxidants, namely vitamin C, carotenoids, and phenolics. There is substantial genetic variation in the content of each of these antioxidant types among fruit and vegetable cultivars. Compared with vitamin C and carotenoids, the levels of phenolic antioxidants appear to be more sensitive to environmental conditions both before and after harvest. Although vitamin C can be readily lost during fresh storage, the content of certain carotenoids and phenolics can actually increase during suitable conditions of fresh storage. Vitamin C and phenolics are more susceptible to loss during processing, especially by leaching from plant tissues into processing water. The combination of cultivar variation and responsiveness to specific environmental conditions can create opportunities for the production and processing of fruits and vegetables with improved antioxidant properties.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it