Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Augļu krāsas analīze× | Simulācija pēc ražas novākšanas× | |
|---|---|---|
| Nozare | Dārzkopība | Dārzkopība |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1976 | 2001 |
| Autors≠ | Commission Internationale de l'Eclairage (CIE) | Luc Tijskens and Bart Nicolaï |
| Tips≠ | optical measurement pipeline | computational modeling pipeline |
| Pirmavots≠ | McGuire, R. G. (1992). Reporting objective color measurements. HortScience, 27(12), 1254–1255. DOI ↗ | 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 ↗ |
| Citi nosaukumi≠ | color grading, chromatic analysis, colorimetry, ripeness grading | shelf life prediction, storage modeling, quality decay simulation |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | Fruit color analysis employs spectrophotometric measurement to quantify ripeness and quality based on chromatic properties. Using the CIE L*a*b* color space, introduced in 1976, this non-destructive method objectively grades fruit maturity and predicts sensory acceptability. It is widely applied in commercial sorting lines and research settings for precision quality control. | 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. |
| ScholarGateDatu kopa ↗ |
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