Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Симуляція зберігання після збору врожаю× | Індекс стиглості× | |
|---|---|---|
| Галузь | Садівництво | Садівництво |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 2001 | 1970 |
| Автор методу≠ | Luc Tijskens and Bart Nicolaï | Pomology and horticulture research |
| Тип≠ | computational modeling pipeline | multi-parameter assessment pipeline |
| Основоположне джерело≠ | Tijskens, L. M., & Polderdijk, J. J. (2001). A generic model for keeping quality of vegetable produce during storage and distribution. Postharvest Biology and Technology, 23(1), 13–25. link ↗ | Pratt, H. K., & Goeschl, J. D. (2006). Physiological roles of ethylene in plants. Annual Review of Plant Physiology, 20, 541–566. DOI ↗ |
| Інші назви | shelf life prediction, storage modeling, quality decay simulation | maturity index, harvest readiness assessment, fruit maturation scoring |
| Пов'язані | 4 | 4 |
| Підсумок≠ | Postharvest storage simulation uses computational models to predict fruit and vegetable quality degradation during storage and distribution under variable temperature and humidity conditions. Pioneered by Tijskens and Nicolaï in 2001, these mechanistic and empirical models enable logistics optimization, reduce food waste, and improve supply chain transparency. They are integrated into decision support systems for commercial packinghouses and research facilities. | Ripeness index combines multiple quality measurements—soluble solids, firmness, color, starch degradation, ethylene production—into a single composite score indicating fruit maturity and harvest readiness. Unlike single-parameter metrics, this integrated approach accounts for cultivar variation and environmental influence to predict consumer acceptability more reliably. It is widely adopted in export industries and research settings to standardize harvest decisions. |
| ScholarGateНабір даних ↗ |
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