FRESHNESS GRADING OF SHELL EGGS USING A DIELECTRIC TECHNIQUE AND ARTIFICIAL NEURAL NETWORK METHOD

Abstract

The increase in size of the air cell is related to the aging process of the eggs. According to the European Commission Regulation, eggs must be classified in A (air cell size higher than 4 mm), and A “extra” (air cell size lower than 4 mm) categories by candling inspection. This technique is unable to non-destructively assess the size of the air cell during egg grading. The present research studies the possibility to nondestructively grading shell eggs from dielectric parameters obtained by means of a sine wave RF oscillator, a parallel inductance and capacitance circuit. In particular, dielectric parameters and egg dimensional characteristics were used to set up multi-layer (MLP) artificial neural networks. Using MLP with two hidden layers, eggs can be correctly graded in A and A “extra” categories (test validation) within a mean performance close to 90%.

Keywords

• artificial neural networks, dielectric properties, eggs.