AI-Assisted Design and Experimental Testing of a Compact UWB Antenna for the Inspection of Food and Beverage Products

Detecting physical contamination caused by low-density foreign bodies is an ongoing challenge faced by the food and beverage industries. To overcome the limitations of existing devices, a novel detection principle based on microwave imaging (MWI) has been assessed. MWI enables safe and non-invasive analysis of the sample under test through a 3-D reconstruction obtained from the alteration that the electromagnetic scattered waves undergo due to the presence of a foreign body. To make the application of this technology more appealing in real-world scenarios, we propose an antenna that can cover a broad set of food types, permitting the adaptability of the system's operating frequency depending on the products' dielectric properties and the containers' type or shape. The proposed antenna is designed with the help of artificial intelligence (AI). Thanks to its low cost and small dimensions, we can increase the quantity of acquired information by increasing the number of antennas placed around the product. A complete functioning system using the designed antenna is presented, assessing the image reconstruction in a case with realistic products and contaminants.