Palm | Kelapa Sawit

ABSTRACT: Tomato processors are increasingly interested in being able to predict whether tomatoes will peel well, and therefore, yield high-value processed tomatoes. We describe two statistical models for peeling applied to multiple years of data. One model is appropriate for perfect or defect-free tomatoes, and the second model is valid for the normal population of tomatoes obtained following mechanical harvesting. The ability to peel perfect tomatoes was significantly affected by exposure of tomatoes to temperatures greater than 100F, by fruit weight and by pericarp wall thickness. The peelability of a normal population of tomatoes was influenced by tomato weight and width as well as degree-days and exposure to temperatures greater than 90F. Thickness of the pericarp walls and red layer positively affected the peelability of normal tomatoes. The ability to predict tomato peelability using statistical models may improve the quality of processed tomatoes and may result in more efficient commercial peeling operations.