Image processing methods for food inspection

Abstract

With the advances in computer technology, signal processing techniques are widely applied to many food safety applications. In this thesis, new methods are developed to solve two food safety problems using image processing techniques. First problem is the detection of fungal infection on popcorn kernel images. This is a damage called blue-eye caused by a fungus. A cepstrum based feature extraction method is applied to the kernel images for classification purposes. The results of this technique are compared with the results of a covariance based feature extraction method, and previous solutions to the problem. The tests are made on two different databases; reflectance and transmittance mode image databases, in which the method of the image acquisition differs. Support Vector Machine (SVM) is used for image feature classification. It is experimentally observed that an overall success rate of 96% is possible with the covariance matrix based feature extraction method over transmittance database and 94% is achieved for the reflectance database. The second food inspection problem is the detection of acrylamide on cookies that is generated by cooking at high temperatures. Acrylamide is a neurotoxin