Image analysis applied to control postharvest maturity of papayas (Carica papaya L.). Issue 11 (22nd August 2022)

• Record Type: Journal Article
• Authors: Chacon, Wilson Daniel Caicedo; dos Santos Alves, Maria Jaízia; Monteiro, Alcilene Rodrigues; González, Sergio Yesid Gómez; Valencia, Germán Ayala
• Is Part Of: Journal of food processing and preservation. Volume 46:Issue 11(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-08-22
• Subjects: Food -- Preservation -- Periodicals; Food industry and trade -- Periodicals; 664.005
• DOI: 10.1111/jfpp.16999
• Languages: English
• ISSNs: 0145-8892

Abstract

Abstract: This study aims to develop a nondestructive methodology using image analysis to classify papayas ( Carica papaya L.) based on their skin color, volume, and mass. First, papayas were classified visually according to their skin color, and the volume and mass were determined experimentally. In sequence, images from papayas were acquired and processed using image analysis. Papayas were classified according to their maturity using a computational methodology based on the CIELab scale. Furthermore, the volume and mass were assessed using a Python code developed to analyze the fruit images. The prediction of the maturity stage had a global estimate of 95.0%, and the estimated volume and mass did not differ statistically from the experimental values. This research reports a novel image analysis methodology focused on papaya classification during its postharvest. Practical applications: Image analysis can be used to classify fruits during their postharvest. In the current research, a nondestructive methodology using image analysis was developed and then used to classify papayas according to their skin color, volume, and mass. Results suggest that papayas can be classified based on their maturity (unripe, partially ripe, and ripe), volume, and mass. The developed methodology could be used to replace the manual sorting of papayas during their classification.