Non-Destructive Assessment of Mango Firmness and Ripeness Using a Robotic Gripper

The final publication is available at link.springer.com [EN] The objective of the study was to evaluate the use of a robot gripper in the assessment of mango (cv. BOsteen^) firmness as well as to establish relationships between the nondestructive robot gripper measurements with embedded accelerometers in the fingers and the ripeness of mango fruit. Intact mango fruit was handled and manipulated by the robot gripper, and the major physicochemical properties related with their ripening index were analyzed. Partial least square regression models (PLS) were developed to explain these properties according to the variables extracted from the accelerometer signals. Correlation coefficients of 0.925, 0.892, 0.893, and 0.937 with a root-mean-square error of prediction of 2.524 N/ mm, 1.579 °Brix, 3.187, and 0.517, were obtained for the prediction of fruit mechanical firmness, total soluble solids, flesh luminosity, and ripening index, respectively. This research showed that it is possible to assess mango firmness and ripeness during handling with a robot gripper. The authors thanks MANI-DACSA projects (ref. RTA2012-00062-C04-02 and RTA2012-00062-C04-03), Spanish Government (Ministerio de Economia y Competitividad), AICO/2015/122 project, Conselleria de Educacion, Cultura y Deporte, Generalitat Valenciana and PAID-05-11-2745 project, Vicerectorat d'Investigacio, Universitat Politecnica de Valencia. Victoria Cortes thanks Spanish Ministerio de Educacion, Cultura y Deporte for a FPU grant (FPU13/04202). Blanes Campos, C.; Cortés López, V.; Ortiz Sánchez, MC.; Mellado Arteche, M.; Talens Oliag, P. (2015). Non-Destructive Assessment of Mango Firmness and Ripeness Using a Robotic Gripper. Food and Bioprocess Technology. 8(9):1914-1924. https://doi.org/10.1007/s11947-015-1548-2 Abdi, H. (2010). Partial least squares regression and projection on latent structure regression (PLS regression). Wiley Interdisciplinary Reviews: Computational Statistics, 2(1), 97–106. Bandyopadhyaya I., Babu D., Bhattacharjee S., & Roychowdhury J. (2014). Vegetable grading using tactile sensing and machine learning. In: Anonymous (ed) Advanced Computing, Networking and Informatics-Volume 1. Smart Innovation, Systems and Technologies. Springer 27, 77-85. doi: 10.1007/978-3-319-07353-8_10 . Blanes, C., Mellado, M., Ortiz, C., & Valera, A. (2011). Technologies for robot grippers in pick and place operations for fresh fruits and vegetables. Spanish Journal of Agricultural Research, 9(4), 1130–1141.