Food Physical Contamination Detection Using AI-Enhanced Electrical Impedance Tomography

Abstract

Physical contamination of food is a prevalent issue within the food production industry. Contamination can occur at any stage of the food processing line. Many techniques are used in the literature for the detection of physical contamination in food. However, these techniques have some limitations when applied to fresh food products, particularly, when samples are characterized by diverse shapes and sizes. In addition, some of these techniques fail to detect hidden contaminants. In this work, we propose a novel approach to detect hidden physical contamination in fresh food products, including plastic fragments, stone fragments, and other foreign food objects, such as different food types that might inadvertently contaminate the sample. Electrical impedance tomography (EIT) is utilized to capture the impedance image of the sample to be used for contamination detection. Four deep learning models are trained using the EIT images to perform binary classification to identify contaminated samples. Three of the models are developed to detect the contaminants, each on its own, while the fourth model is used to detect any of the contaminates put together. The trained models achieved promising results with the accuracy of 85%, 92.9%, and 85.7% detecting plastic, stones, and other food types, respectively. The obtained accuracy when all contaminants put together was 78%. This performance shows the efficacy of the proposed approach over the existing techniques in the field.