[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127091-en":3,"doc-seo-127091-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127091,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Microwave-Assisted Detection of Physical Intrusions in Commercial Food Packaged Products via Machine Learning - Conference Paper","This study proposes a microwave-assisted sensing framework combined with machine learning to detect physical intrusions inside commercial food packaging. Using a non-invasive, real-time compatible setup, it validates prior work by testing real water-based and oil-based products, focusing on tomato and pesto sauce. Scattering parameters obtained from microwave measurements train binary classifiers using SVM and MLP. Across 200 measurement samples per food type, the system achieves perfect 100% accuracy, demonstrating strong effectiveness for in-line food safety inspection.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nMicrowave-Assisted Detection of Physical Intrusions in Commercial Food Packaged Products via Machine Learning  \nOriginal  \nMicrowave-Assisted Detection of Physical Intrusions in Commercial Food Packaged Products via Machine Learning / Darwish, Ali; Ricci, Marco; Tobon Vasquez, Jorge Alberto; Migliaccio, Claire; Vipiana, Francesca. -ELETTRONICO. -(2024), pp. 573-576. (Intervento presentato al convegno 54th European Microwave Conference (EuMC) tenutosi a Paris (France) nel 24-26 September 2024) [10 .23919/eumc61614 .2024. 10732555] .  \nAvailability:  \nThis version is available at: 11583/2994111 since: 2024-11-07T13:15:37Z  \nPublisher: IEEE  \nPublished  \nDOI:10.23919/eumc61614.2024.10732555  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nIEEE postprint/Author's Accepted Manuscript  \n©2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collecting works, for resale or lists, or reuse of any copyrighted component of this work in other works.  \n(Article begins on next page)  \n17 February 2025  \nMicrowave-Assisted Detection of Physical Intrusionsin Commercial Food Packaged Products via Machine  \nLearning  \nAli Darwish\\#$ , Marci Ricci\\# , Jorge Alberto Tobon Vasquez\\# , Claire Migliaccio $1 , Francesca Vipiana\\#2  \n\\#DET, Politecnico di Torino, Italy  \n$LEAT, Université Côte d’Azur, France  \n[1](1 claire.migliaccio@univ-cotedazur.fr)[ claire.migliaccio@univ-cotedazur.fr](1 claire.migliaccio@univ-cotedazur.fr),2 francesca.vipiana@polito.it  \nAbstract—This study introduces a novel approach using microwaves (MW) combined with a machine learning (ML) classifier to detect physical intrusions within food packaging. Our objective is to validate our previous works [1], [2], and [3], by selecting real commercial water-based and oil-based food packaged products, specifically tomato and pesto sauce. The non-invasive MW sensing system is designed for real-time operation within a food production chain. Experimenting with both the support vector machine (SVM) and the multi-layer perceptron neural network (MLP) algorithms for binary classification, after training on datasets generated from scattering parameters acquired during measurements, resulting in a remarkable precision of 100% accuracy across 200 measurement samples for each food type. This outcome reflects the effectiveness of our previous findings.  \nKeywords—microwave sensing, machine learning, non-destructive techniques, food safety.  \nI. INTRODUCTION  \nThe significant increase in the implementation of automation processes within the food industry is contributing to a high probability of physical contaminants generated during the different production processes inside the food packages. Detecting these contaminants through a non-destructive technology within an in-line production chain poses a challenge for companies in the food industry. To address this challenge, many technologies have been used and explored. X-ray technology is widely regarded as the most prevalent and effective method for addressing this issue [4] . However, it has limitations in detecting low-density materials. Metal detector systems [5] are constrained to identifying metals exclusively, making them ineffective in resolving the problem. Near-infrared [6] and terahertz imaging [7] techniques also have limitations in penetration depth, particularly when dealing with lossy media. In addition to the limitations mentioned before, these technologies are complex and costly systems. The exploration of new technologies has garnered significant interest from food companies.  \nMicrowave (MW) sensing technology, assisted by machine learning tools, is making hea","cbCaij36DgaBO1J5","https://ap.wps.com/l/cbCaij36DgaBO1J5","pdf",5212883,1,5,"English","en",105,"# Introduction\n## Microwave sensing and machine learning motivation\n## Limits of alternative inspection technologies\n# Experimental configurations\n## Sensing system\n## Data acquisition and classification approach\n# Results and discussion\n## Binary classification performance","[{\"question\":\"What does the microwave-assisted system detect in this study?\",\"answer\":\"It detects physical intrusions within commercial food packaging by analyzing how microwaves scatter after interacting with contaminants inside the jar.\"},{\"question\":\"Which machine learning models are used for classification?\",\"answer\":\"The study compares a support vector machine (SVM) and a multi-layer perceptron (MLP) neural network for binary classification.\"},{\"question\":\"How is the sensing system designed for practical food production?\",\"answer\":\"It is built as a non-invasive microwave sensing setup intended for real-time, in-line operation within a food production chain.\"}]","Microwave-Assisted Detection of Physical Intrusions in Commercial Food Packaged Products via Machine Learning - 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