Estimation of Postharvest Quality of “Red Delicious” Apple Fruits Based on Fruit Nutrient Elements Composition
Bibliographic record
Abstract
Fruit nutrient elements content during harvest could considerably effect on postharvest life of apples. In this study, apple fruits cultivar Red Delicious were harvested at the commercial maturity stage at 20 commercial orchards. Fruits were divided into three groups according to peel color; dark red, medium and light red. The mineral elements nutrient content such as nitrogen, phosphorus, potassium, calcium and magnesium and their ratios were measured in the harvested fruits. Thereafter, fruits were places in the cold storage at 0 °C and relative humidity of 90% for 4 months. The characteristics such as fruits weight loss, firmness, TSS, dry matter, total anthocyanin content, antioxidant activity, respiration rate and ethylene production were measured at the end of storage. The results showed a positive significant correlation between fruit firmness at the end of storage time with N+K:Ca, K+Mg:Ca, K:Ca and Mg:Ca ratios. Furthermore, a negative significant correlation was found between total anthocyanin with Nitrogen content, Mg:Ca and N:Ca ratios. The results also showed a positive significant correlation between fruit respiration rate and Nitrogen, N+K:Ca and N:Ca ratios. The analysis of regression based on mean values of three red apple groups showed a significant negative correlation between total anthocyanin with fruit ethylene production at the storage time and Mg:Ca ratio. The relationship between these two variables and Anthocyanin was expressed by the equation of regression: Anthocyanin = 100.22–1.651 Ethylene–43.963 Mg:Ca. Overall, the results confirm that measurement of fruit mineral composition during harvest time could be a strategy for predicting postharvest behaviors of apple fruits at the cold storage. Also the results showed that a relationship between mineral nutrient composition and the characteristics such as firmness, anthocyanin, respiratory rate in apple fruits.
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 | 0.000 |
| 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".