[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123949-en":3,"doc-seo-123949-105":30,"detail-sidebar-cat-0-en-105":83},{"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},123949,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Improved quality control and sustainability in food production by machine learning - Conference paper","The food industry faces intertwined challenges in quality control and sustainability, where consumer safety and satisfaction depend on strict standards while variability in raw materials, processing methods, and storage conditions continually affects outcomes. Managing this variability requires analysing production drivers to predict and minimise food waste, enabling more sustainable processes. Machine learning supports this convergence through defect detection, process optimisation, resource allocation, and predictive maintenance. The work reviews machine learning applications and uses industrial corn cake production as a case study.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nImproved quality control and sustainability in food production by machine learning  \nOriginal  \nImproved quality control and sustainability in food production by machine learning / Puttero, Stefano; Verna, Elisa; Genta, Gianfranco; Galetto, Maurizio. -122:(2024), pp. 533-538. (Intervento presentato al convegno 31st CIRP Conference on Life Cycle Engineering (LCE 2024) tenutosi a Torino (Italia) nel 19-21 Giugno 2024)[10 . 1016/j. procir.2024.01.078] .  \nAvailability:  \nThis version is available at: 11583/2988924 since: 2024-05-22T15:20:18Z  \nPublisher: Elsevier  \nPublished  \nDOI:10.1016/j.procir.2024.01.078  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n18 September 2024  \n[Available online at www.sciencedirect.com](Available online at www.sciencedirect.com)  \nScienceDirect  \nProcedia CIRP 122 (2024) 533–538  \n31st CIRP Conference on Life Cycle Engineering (LCE 2024)  \nImproved quality control and sustainability in food production by machine  \nlearning  \nStefano Putteroa, *, Elisa Vernaa, Gianfranco Gentaa, Maurizio Galettoa  \naDepartment of Management and Production Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy  \n* Corresponding author. Tel.: +39 0110907236; E-mail address: stefano.puttero@polito.it  \nAbstract  \nIn recent years, the food industry has faced a number of complex challenges related to both quality control and sustainability. Ensuring consumer safety and satisfaction remains a cornerstone of the food industry, supported by stringent standards that address the risks of contamination and spoilage. However, variability in raw materials, processing techniques and storage conditions are just some of the factors that affect quality in the food industry. To manage this high variability, it is essential to analyse the production process and factors that most influence food quality, aiming to predict and minimise food waste, thereby ensuring a sustainable process. This convergence of quality control and sustainability goals provides fertile ground for machine learning applications. By improving defect detection, process optimisation, resource allocation and predictive maintenance, these models help to improve product quality and reduce environmental impact. This article aims to explore the various applications of machine learning models in the food industry, where the variability of raw materials and the difficulty of controlling production and environmental factors challenge the use of traditional methods. The quality control and sustainability of an industrial corn cakes production process is used as a case study.  \n© 2024 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer-review under responsibility of the scientific committee of the 31st CIRP Conference on Life Cycle Engineering (LCE 2024)  \nKeywords: Sustainable manufacturing; Quality control; Food industry; Machine learning  \n1. Introduction  \nOver the last decades, the food industry has faced several critical global challenges, both in terms of environmental sustainability and economic viability. Population growth, climate change, resource scarcity and efficient food waste management are just some of these challenges. In particular, food waste is one of the most worrying factors in the food sector. To date, it is estimated that nearly one-third of all food produced for human consumption is wasted each year, totalling about 1.3 billion tonnes worldwide [1,2] . This waste not only represents a significant economic loss, but also contributes to greenhouse gas emissions, land degradation and natural resource depletion. Accordingly, food waste management is a  \nc","cbCaicf4wDjKYxLA","https://ap.wps.com/l/cbCaicf4wDjKYxLA","pdf",744976,1,7,"English","en",105,"# Introduction\n## Food industry challenges and food waste\n## Circular economy and need for prediction tools\n## Role of machine learning in quality control and sustainability","[{\"question\":\"What case study is used to demonstrate the approach?\",\"answer\":\"The paper uses the quality control and sustainability of an industrial corn cakes production process as a case study, applying the discussion of machine learning applications to this specific production context.\"}]","Improved quality control and sustainability in food production by machine learning - Conference paper | PDF",1785819397,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"improved-quality-control-and-sustainability-in-food-production-by-machine-learning-conference-paper","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/improved-quality-control-and-sustainability-in-food-production-by-machine-learning-conference-paper/123949/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What case study is used to demonstrate the approach?","Question",{"text":75,"@type":76},"The paper uses the quality control and sustainability of an industrial corn cakes production process as a case study, applying the discussion of machine learning applications to this specific production context.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,111,114,119,122,126],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]