Screening of tocols content in different maize kernel type
Bibliographic record
Abstract
Maize (Zea Mays L.) is one of the main crops after rice and wheat commonly grown worldwide and one of the major food sources, especially for developing countries. Pigmented maize (yellow, black, purple, orange, red, and blue) and maize with specific traits contain secondary metabolites that have a positive role and protective impact on human health. Bioactive compounds naturally occurring in maize are tocopherols (α-T, β-T, γ-T and δ-T) and tocotrienols (α-T3, β-T3, γ-T3 and δ-T3), together called tocols or vitamin E. This study aimed to estimate the content of tocols in different maize kernel types and evaluate a possible linkage between them. In our study, the content of tocols in popcorn, white, yellow, red, orange, and sweet maize was determined using high-performance liquid chromatography with fluorescence detection (HPLC-FLD). Obtained results revealed that the sweet maize has the highest content of β + γ-T (55.18 – 94.30 μg/g dry weight (dw)) among all tested maize kernel types. Similarly, in yellow and orange maize kernel types was found the highest content of α-T (9.44 – 20.54 μg/g dw) in comparison to other maize kernel types. According to the Principal Component Analysis (PCA), samples of popcorn, sweet, and red maize were separated on loading plots indicating a unique linkage between kernel types and tocols content. The obtained results indicate that there is a need to improve the nutritional quality of maize kernel in terms of vitamin E content, especially for white and yellow, which are predominantly used in human nutrition in Europe.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".