Muhammad Furqan Siddiqui
Machine learning techniques for microwave food contamination detection.
Rel. Francesca Vipiana, Mario Roberto Casu, Jorge Alberto Tobon Vasquez, Marco Ricci. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2023
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Abstract
Food manufacturers still struggle with the contamination of their products by foreign bodies today. They must first assure customers that their pur- chases are safe. Second, contamination can harm a company’s brand and result in pricey recall efforts. Lastly, because accurate foreign body detec- tion technologies enable manufacturers to acquire essential food certifications that may boost their revenues. The issue of metal object detection is a major concern for a variety of industries. To address this issue, a number of strategies have been imple- mented, such as signal processing filters, detection systems, X-ray scanning, and near-infrared imaging. However, these strategies have some limitations, such as susceptibility to water attenuation, and a trade-off between low spatial resolution and high penetration depth.
Moreover, low-density, non- metallic objects, such as plastic, glass, and wood, remain undetectable
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