Effect of harvest time on sugar content and carotenoid composition in different sweet maize hybrids
Bibliographic record
Abstract
Sweet maize (Zea mays convar. saccharata var. rugosa) is valued for its high sugar content, which gradually converts to starch during kernel maturation. This study evaluated the yield and biochemical composition of five super sweet maize hybrids (D, M, G, S, and N) across four harvest dates (July 19, July 26, August 2, and August 9) over two consecutive growing seasons. Seventeen key nutritional parameters-including sugars, minerals, and carotenoids-were significantly affected by both hybrid and harvest time (p < 0.05). The M hybrid showed the highest levels of β-carotene (5.61 µg/g), phosphorus (3.45 mg/g), and magnesium (1.96 mg/g), while the S hybrid had the greatest concentrations of lutein (2.84 µg/g), zeaxanthin (2.71 µg/g), and β-cryptoxanthin (1.79 µg/g). The D hybrid recorded the highest sucrose content (78.5 mg/g), and the N hybrid was superior in dry matter (32.7%) and fructose (61.4 mg/g). Harvest timing also had a significant impact: early harvest (July 19) resulted in peak concentrations of β-carotene (5.89 µg/g), potassium (4.17 mg/g), and glucose (84.2 mg/g), whereas late harvest (August 9) favored hybrid-specific nutrient accumulation. Principal Component Analysis (PCA) revealed that the first two components explained 75.82% of the total variance, with glucose and potassium identified as key discriminating traits. These findings highlight the critical role of genotype selection and harvest timing in optimizing the nutritional quality and market value of sweet maize under temperate growing conditions.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".