Carotenoid Retention in Immature Corn Ear Grains Subjected to Different Thermal Treatments
Bibliographic record
Abstract
Processed food products may undergo changes in chemical composition during processing, leading to potential losses in nutritional value. The objective of this study was to determine carotenoid retention in immature grains of normal corn (BRS1030) and corn biofortified (BRS4104) with vitamin A precursors subjected to different thermal treatments: cooking in a microwave, cooking in a pressure cooker, cooking in a pot with a lid and cooking in a pot without a lid. The experiment had a completely randomized design in a factorial scheme (cultivar and type of cooking). The carotenoids were extracted in a sequential organic solvent scheme and quantified by high-performance liquid chromatography (HPLC). The results were submitted to analysis of variance (ANOVA), and when significant, the means were compared using the least significant difference (LSD) test (p = 0.05). Despite cultivars, the concentrations of carotenoid vitamin A precursors and total carotenoids in the immature corn grains were reduced after cooking the ears in a microwave or in a pressure cooker. The best treatments for preserving carotenoids according to the conditions studied are cooking in a pot with a lid and in a pot without a lid.
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 | 0.000 |
| 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 | 0.001 |
| 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".