Internal Defect Detection of Whole Pickles Using Hyperspectral Transmittance and Reflectance Imaging

Abstract

Hyperspectral imaging technique in simultaneous reflectance and transmittance modes was investigated for detection of hollow or bloater damage on whole pickles that was caused by mechanical injury during harvesting and handling or developed during the brining process. Normal and bloated pickle samples were collected from a commercial pickle processing plant. Hyperspectral images were acquired from the pickle samples using a hyperspectral reflectance and transmittance imaging system covering the spectral range of 400-1000 nm. Principal component analysis was applied to the hyperspectral images of the pickle samples, and the second principal component score images were used for defect detection by means of image segmentation method. An overall classification accuracy of 86% was achieved using this method. Transmittance images at 675-1000 nm were much more effective for internal defect detection compared to reflectance images for the visible region of 500-675 nm. With further improvement, the hyperspectral imaging system could meet the need of bloated pickles detection in a commercial plant setting.