Luca Urbinati
Detection of food contaminants with Microwave Sensing and Machine Learning.
Rel. Mario Roberto Casu, Francesca Vipiana. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Elettronica (Electronic Engineering), 2019
|
Preview |
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (16MB) | Preview |
Abstract
Today, food contamination due to foreign body is still a trouble for food manufacturers. First of all, because they have to guarantee a safe product to consumers. Secondly because a contaminant can damage the company reputation and lead to expensive recall campaigns. Finally, because more accurate foreign body detection systems allow the producers to gain important food certifications that could increase their incomes. For these reasons many techniques are currently adopted to solve this problem, such as mechanical filters, metal detectors, X-rays imaging and near infrared imaging. However, there are still some limitations: nonmetallic and low-density objects, like fragments of plastics, glass and wood, are not currently detectable; high penetration depth and low spacial resolution trade-off is still relevant; some methods are subjected to water attenuation.
The good new is that Microwave Imaging Technology has the potential to overcome those problems in addition to other attractive characteristics
Relatori
Tipo di pubblicazione
URI
![]() |
Modifica (riservato agli operatori) |
