[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128355-en":3,"doc-seo-128355-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128355,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Electronic nose and machine learning for modern meat inspection","Objective post-mortem meat inspection is essential for ensuring appropriate assessment and quality control of meat for human consumption. Early detection of factors that threaten public health, as well as animal health and welfare, including chemical contaminants, is critical. This study introduces an electronic nose combined with machine learning for pig meat inspection. A metal-oxide chemoresistive sensor array classified 100 samples by odor categories, achieving 96.5% sensitivity and 95.3% specificity with near-perfect Kappa performance. The model also distinguished fresh meat from meat aged 1–2 and correctly identified broader aging ranges, supporting practical slaughterhouse implementation and supply-chain quality assurance.","Shtepliuk et al. Journal of Big Data (2025) 12:96 [https://doi.org/10.1186/s40537-025-01151-4](https://doi.org/10.1186/s40537-025-01151-4)  \nJournal of Big Data  \nRESEARCH Open Access  \nElectronic nose and machine learning for modern meat inspection  \nIvan Shtepliuk1*, Guillem Domènech‑Gil 1,2, Viktor Almqvist3, Arja Helena Kautto3, Ivar Vågsholm3, Sofia Boqvist3, Jens Eriksson 1 and Donatella Puglisi 1*  \n*Correspondence:  \n[ivan.shtepliuk@liu.se](ivan.shtepliuk@liu.se); donatella. [puglisi@liu.se](puglisi@liu.se)  \n1 Department of Physics, Chemistry and Biology, Linköping University, 581  \n83 Linköping, Sweden  \n2 Department of Thematic Studies and Environmental Change, Linköping University, 581 83 Linköping, Sweden  \n3 Department of Animal Biosciences, Swedish University of Agricultural Sciences, 750  \n09 Uppsala, Sweden  \nAbstract  \nObjective and reliable post‑mortem meat inspection is a key factor in ensuring adequate assessment and quality control of meat intended for human consumption. Early identification of issues that may impact public health and animal health and welfare, such as the presence of chemical contaminants in meat, is critical. In this study, we propose a novel method to modernize meat inspection using an electronic nose combined with machine learning (ML), with focus on pig meat as a case study. We explored its potential as a complementary tool to traditional sensory evaluation and analytical methods, aiming to enhance the efficiency and effectiveness of current inspections. We employed a metal‑oxide based gas sensor array of commercially available chemoresistive sensors, functioning as an electronic nose, to differentiate between various categories of 100 pig meat samples collected at a slaughterhouse based on their odor characteristics, including a urine‑like smell and post‑mortem aging. Using the Optimizable Ensemble model, we achieved a sensitivity of 96. 5% and specificity of 95. 3% in categorizing fresh and urine‑contaminated meat samples. The model demonstrated robust predictive performance with a Kappa value of approximately 0 . 926, indicating near‑perfect agreement between the predictions and actual classifications. Furthermore, our developed ML model demonstrated the ability to distinguish between nominally fresh pig meat and meat aged for one to two additional days with an accuracy of 93 . 5% and can also correctly identify meat aged 3–31 days or 17–31 days. Based on the consensus of preliminary decisions from each individual sensor element, the algorithm effectively determined the final status of the meat. This research lays the groundwork for practical applications within the meat inspection process in slaughterhouses and as quality assurance throughout the meat supply chain. As we continue to refine and validate this method, its potential for real‑world implementation becomes increasingly evident.  \nKeywords: Gas sensors, Machine learning, Volatile organic compounds, Odor detection, Meat chain waste, Meat quality assurance, Food safety measures, Chemical contamination, Public health hazards, Animal health and welfare  \nIntroduction  \nChemical contaminants in meat are classified by the European Food Safety Authority as a public health hazard that can impact both human and animal health and welfare [1]. The unpleasant odor of pork, which can arise from various factors such as the  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons lic","cbCaicOlXNBHjRpc","https://ap.wps.com/l/cbCaicOlXNBHjRpc","pdf",2722125,4,1,21,"English","en",105,"# Abstract\n## Objective and motivation\n## Method: electronic nose + machine learning\n## Results and predictive performance\n## Implications for meat inspection","[{\"question\":\"What problem does the study address in meat inspection?\",\"answer\":\"It targets the need for objective and reliable post-mortem meat inspection, especially early identification of issues such as chemical contamination that can affect public health and animal welfare.\"},{\"question\":\"How does the proposed system work?\",\"answer\":\"It uses a metal-oxide gas sensor array as an electronic nose, combined with machine learning to differentiate pig meat odor categories and infer meat status.\"},{\"question\":\"What performance did the Optimizable Ensemble model achieve?\",\"answer\":\"For fresh versus urine-contaminated samples, it reported 96.5% sensitivity and 95.3% specificity, with a Kappa value around 0.926 indicating near-perfect agreement.\"}]","Electronic nose and machine learning for modern meat inspection | 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