Postharvest bitter pit detection and progression evaluation in ‘Honeycrisp’ apples using computed tomography images
| Autor: | Lav R. Khot, Sindhuja Sankaran, Carlos Zúñiga Espinoza, Sanaz Jarolmasjed |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0106 biological sciences
biology medicine.diagnostic_test fungi food and beverages Computed tomography 04 agricultural and veterinary sciences biochemical phenomena metabolism and nutrition Horticulture equipment and supplies biology.organism_classification 01 natural sciences 040501 horticulture Honeycrisp medicine Postharvest bacteria 0405 other agricultural sciences Agronomy and Crop Science 010606 plant biology & botany Food Science Bitter pit |
| Zdroj: | Postharvest Biology and Technology. 118:35-42 |
| ISSN: | 0925-5214 |
| DOI: | 10.1016/j.postharvbio.2016.03.014 |
| Popis: | Bitter pit is a physiological disorder that is defined as brown, corky and roundish lesions, which can develop in apples before and after harvest. This disorder greatly reduces the product utilization value of the fruit, and can result in several million dollar economic loss to the apple industry. Computed Tomography (CT) imaging is a non-destructive and rapid sensing technique that can be applied to packaged apples. In this study, healthy and bitter pit Honeycrisp apples were harvested from two field sites and stored for 63 days. CT images of the sampled apples were collected on 0, 7, 14, 21, 35 and 63 days after harvest. Images were analyzed to estimate the total pit area in each of the individual apples and were related to pit incidence and progression in different stages of storage. Results showed pit development during the storage period in bitter pitted apples. The rate of progression differed in samples collected from different field sites. Further analysis for pit distribution along each of the bitter pit affected apples showed 54% of pits located at the calyx-end of apples in comparison with middle and stem-end. Classification of healthy and bitter pitted apples using logistic regression based method resulted in false negative of 7–21%. |
| Databáze: | OpenAIRE |
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