Potential of X-ray imaging to detect citrus granulation in different cultivars with progress in harvesting time

Abstract

Granulation, a physiological disorder of citrus is manifested by shriveled juice sacs and internal dryness. Extractable juice in granulated tissue is drastically reduced as a consequence of gelatinization and secondary epidermis formation. Since, the defect cannot be detected externally it leads to consumer dissatisfaction and poor returns to farmers. Processing industry also faces huge economic loss due to reduction in the juice recovery from granulated fruit. In this context, here, we studied the possibility of developing an image processing algorithm through MATLAB software to detect granulation with advancement of maturity via X-ray micrographs. Fruit of eight citrus cultivars comprising of granulation susceptible and tolerant varieties harvested at four different intervals were exposed to X-rays. Voltage of 46 kV and current of 6.5 mA given to fruit for an exposure time of 320 mAs gave the best X-ray image contrasts. The developed algorithm could effectively distinguish the healthy and granulated fruit with an accuracy of 90% as validated by subsequent destructive analysis when estimated for four different harvesting dates. The imaging technique can be employed by the processors to determine the severity of granulation and to sort out fruit online which will help in saving economic losses.