|Obenland, David - Dave|
Submitted to: Postharvest Biology and Technology
Publication Type: Peer reviewed journal
Publication Acceptance Date: 9/1/2007
Publication Date: 6/1/2008
Citation: Slaughter, D.C., Obenland, D.M., Thompson, J.F., Arpaia, M.L. 2008. Non-destructive freeze damage detection in oranges using machine vision and ultraviolet fluorescence. Postharvest Biology and Technology. 48(3) 341-346. Interpretive Summary: Oranges subjected to freezing conditions are frequently unsuitable for consumption because they develop off-flavors or dehydrated flesh. Current methods for detection of freeze damage in oranges immediately after a freeze rely on cutting the fruit and are subjective and inaccurate. It was discovered that when freeze-damaged oranges are illuminated with longwave ultraviolet light following a freeze there are small yellow fluorescing dots often visible on the peel. Testing with fruit frozen using a simulated freeze in the laboratory indicated that detection of damage using ultraviolet light had an overall accuracy of 70% as compared to the existing USDA cutting method. Ultraviolet light shows great promise as a means to rapidly and accurately determine which oranges are damaged following a freeze event.
Technical Abstract: A non-contact, non-destructive, and rapid method of detecting freeze damaged oranges based on ultraviolet (UV) fluorescence of the peel oil constituents visible on the peel surface was investigated. The visual appearance is different from oleocellosis in that freeze damaged oranges exhibit a fine pattern of 1 to 2 mm bright yellow dots on the peel when viewed under longwave UV light. Visual and machine vision based methods were evaluated to determine their ability to detect freeze damage in Californian navel oranges (Citrus sinensis L. Osbeck) subjected to laboratory simulated freeze conditions of -7 C for 0 h, 8 h, or 16 h periods. The study focused on the period within the first few days (i.e., prior to fruit dehydration) after a freeze event has occurred because there are currently no rapid, objective, and non destructive methods of freeze damage detection available for use during that time period. Using the USDA segment cut method to determine the presence of internal freeze damage, the classification rates for both UV fluorescence methods varied with the level of freeze damage. Using machine vision, a classification accuracy of 87.9% was obtained for unfrozen and moderately or severely frozen fruit, dropping to 64.4% for fruit with low levels of freeze damage. UV fluorescence shows promise for both visual inspection using existing black light inspection rooms or for automation using on line machine vision techniques for separating freeze damaged fruit subjected to moderate or severe freeze conditions.