DETECTION OF PINHOLES IN ALMONDS THROUGH X–RAY IMAGING

Pinhole insect damage in natural almonds (i.e., in nuts with the brown skin intact) is very difficult to detect on line. For quality reasons, methods to detect and remove such damaged nuts are of great importance. In this study, we explored the possibility of using X–ray imaging to detect pinhole damage in almonds by insects. X–ray film and x–ray line–scanned images of 522 pinhole–damaged almonds were obtained. Of these film images, 505 were successfully digitized to 8 bits by use of a film scanner with a 0.173–mm detector pitch, and 499 of the 505 were collected by a line–scan unit with 0.5–mm pitch photodiodes, again at 8 bits. The pinhole–damaged region appeared slightly darker than the non–damaged region in X–ray negative images. A machine–recognition algorithm was developed to detect these darker regions. The algorithm used first–order (pixel intensity) and second–order (intensity change) information to identify the damaged region. To reduce the number of false positive results due to germ regions in high–resolution images, germ detection and removal routines were also included. With scanned film images, the algorithm showed approximately an 81% correct recognition ratio with only 1% false positives. With line–scanned images, 65% of the pinholes were correctly recognized with less than 12% false positives. The algorithm was very fast and efficient, requiring only minimal computation time. The computation rate, if implemented on line, was estimated to be 66 nuts/s, while the x–ray line scanner could achieve a scan rate of 24 nuts/s.