Physical contaminants continue to be a major challenge for the food industry. These intrusions elude controls and end up in consumer’s hands and bodies, posing a serious health issue and a high level of media coverage: accidental ingestion of foreign bodies can cause choking and seriously damage the digestive tract. Precautionary methods and systems already in use, such as X-rays, metal detectors and near-infrared imaging, may fail to detect particular kinds of materials, especially low-density ones, and the increase of plastic employment in industries, together with the rise of automation in production facilities, may cause additional foreign bodies contaminations. This thesis presents a novel detection principle based on microwave-sensing technology and explores deeply the field of machine learning (ML) in search of a fruitful combination between these two.