Assessment of carotenoid degradation of grits from orange corn packaged in high barrier thermosealed pouches
Bibliographic record
Abstract
Genetically improved maize varieties with high carotenoid levels and dark orange color have been developed to increase dietary consumption of macular carotenoids. However, postharvest and food processing conditions can cause isomerization and oxidation of carotenoids, reducing their potential impact on consumers' health. The purpose of this study was to assess the effectiveness of high barrier pouches in reducing carotenoid losses during the storage of dry-milled corn products. Orange corn grits were packaged in paper pouch bags, and three types of low-oxygen and low-moisture permeable (LOMP) pouches. Grits were packaged in each type of LOMP pouch with (LOMP-oxy) and without (LOMP-noxy) an oxygen scavenger. For six months, all pouches were stored at semi-controlled environmental conditions (22.5 ± 1.3°C, 32 ± 18% RH). After the storage period, orange corn grits stored in paper pouch bags lost 55% of total xanthophylls, whereas grits packaged in LOMP pouches only lost 8% of total xanthophylls. Orange Corn grits packaged in LOMP-oxy pouches had slightly higher carotenoid content than in LOMP-noxy pouches. Relative humidity fluctuation in the storeroom could have caused fluctuation in moisture content in the orange corn grits packaged in paper pouches, which may affect the rate of carotenoid degradation in the orange corn grits. Therefore, an effective control of the moisture content of the packaged dry-milled product and effective control of the temperature of pouches during storage conditions is essential to maximize carotenoid retention during the storage of dry-milled high carotenoid orange corn grits.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".