Paper Title
Spectral Classification in Detection of Foreign Objects Present in Food

The main objective of the present work is to provide a new approach for image recognition using Artificial Neural Networks using spectral classification. Since the different objects possess different spectral properties, collecting the spectral features from each samples and analyzing it for the contaminants detection is the basic idea of the work. Feed forward network is used for the weights calculation and back propagation is used for the error minimization in weights calculated. Training is performed for all the compounds including contaminants. In the testing part compounds with ROI which falls in predefined contaminant boundary which are considered to be contaminants and the execution is done in real time. Keywords— Artificial neural network, spectral properties, spectral features, feed forward network, back propogatio,weights calculation, ROI, Real time.