[{"volume":6,"publication_status":"published","publisher":"Frontiers Media SA","date_created":"2026-04-30T13:02:33Z","language":[{"iso":"eng"}],"citation":{"havard":"J. Tholen, A. Kirse, A. Voß, G. Schulze Althoff, L. Strotkötter, L. Kreienbrock, M. Upmann, Detection of carcass contamination using video image analysis during industrial pig slaughter, Frontiers in Food Science and Technology. 6 (2026).","mla":"Tholen, Janna, et al. “Detection of Carcass Contamination Using Video Image Analysis during Industrial Pig Slaughter.” <i>Frontiers in Food Science and Technology</i>, vol. 6, 1698416, 2026, <a href=\"https://doi.org/10.3389/frfst.2026.1698416\">https://doi.org/10.3389/frfst.2026.1698416</a>.","apa":"Tholen, J., Kirse, A., Voß, A., Schulze Althoff, G., Strotkötter, L., Kreienbrock, L., &#38; Upmann, M. (2026). Detection of carcass contamination using video image analysis during industrial pig slaughter. <i>Frontiers in Food Science and Technology</i>, <i>6</i>, Article 1698416. <a href=\"https://doi.org/10.3389/frfst.2026.1698416\">https://doi.org/10.3389/frfst.2026.1698416</a>","short":"J. Tholen, A. Kirse, A. Voß, G. Schulze Althoff, L. Strotkötter, L. Kreienbrock, M. Upmann, Frontiers in Food Science and Technology 6 (2026).","chicago-de":"Tholen, Janna, Alina Kirse, Alexander Voß, Gereon Schulze Althoff, Lea Strotkötter, Lothar Kreienbrock und Matthias Upmann. 2026. Detection of carcass contamination using video image analysis during industrial pig slaughter. <i>Frontiers in Food Science and Technology</i> 6. doi:<a href=\"https://doi.org/10.3389/frfst.2026.1698416\">10.3389/frfst.2026.1698416</a>, .","ufg":"<b>Tholen, Janna u. a.</b>: Detection of carcass contamination using video image analysis during industrial pig slaughter, in: <i>Frontiers in Food Science and Technology</i> 6 (2026).","ieee":"J. Tholen <i>et al.</i>, “Detection of carcass contamination using video image analysis during industrial pig slaughter,” <i>Frontiers in Food Science and Technology</i>, vol. 6, Art. no. 1698416, 2026, doi: <a href=\"https://doi.org/10.3389/frfst.2026.1698416\">10.3389/frfst.2026.1698416</a>.","van":"Tholen J, Kirse A, Voß A, Schulze Althoff G, Strotkötter L, Kreienbrock L, et al. Detection of carcass contamination using video image analysis during industrial pig slaughter. Frontiers in Food Science and Technology. 2026;6.","ama":"Tholen J, Kirse A, Voß A, et al. Detection of carcass contamination using video image analysis during industrial pig slaughter. <i>Frontiers in Food Science and Technology</i>. 2026;6. doi:<a href=\"https://doi.org/10.3389/frfst.2026.1698416\">10.3389/frfst.2026.1698416</a>","din1505-2-1":"<span style=\"font-variant:small-caps;\">Tholen, Janna</span> ; <span style=\"font-variant:small-caps;\">Kirse, Alina</span> ; <span style=\"font-variant:small-caps;\">Voß, Alexander</span> ; <span style=\"font-variant:small-caps;\">Schulze Althoff, Gereon</span> ; <span style=\"font-variant:small-caps;\">Strotkötter, Lea</span> ; <span style=\"font-variant:small-caps;\">Kreienbrock, Lothar</span> ; <span style=\"font-variant:small-caps;\">Upmann, Matthias</span>: Detection of carcass contamination using video image analysis during industrial pig slaughter. In: <i>Frontiers in Food Science and Technology</i> Bd. 6, Frontiers Media SA (2026)","bjps":"<b>Tholen J <i>et al.</i></b> (2026) Detection of Carcass Contamination Using Video Image Analysis during Industrial Pig Slaughter. <i>Frontiers in Food Science and Technology</i> <b>6</b>.","chicago":"Tholen, Janna, Alina Kirse, Alexander Voß, Gereon Schulze Althoff, Lea Strotkötter, Lothar Kreienbrock, and Matthias Upmann. “Detection of Carcass Contamination Using Video Image Analysis during Industrial Pig Slaughter.” <i>Frontiers in Food Science and Technology</i> 6 (2026). <a href=\"https://doi.org/10.3389/frfst.2026.1698416\">https://doi.org/10.3389/frfst.2026.1698416</a>."},"publication_identifier":{"issn":["2674-1121"]},"author":[{"last_name":"Tholen","full_name":"Tholen, Janna","first_name":"Janna"},{"last_name":"Kirse","full_name":"Kirse, Alina","first_name":"Alina"},{"full_name":"Voß, Alexander","last_name":"Voß","first_name":"Alexander"},{"last_name":"Schulze Althoff","full_name":"Schulze Althoff, Gereon","first_name":"Gereon"},{"full_name":"Strotkötter, Lea","last_name":"Strotkötter","first_name":"Lea"},{"last_name":"Kreienbrock","full_name":"Kreienbrock, Lothar","first_name":"Lothar"},{"last_name":"Upmann","id":"12666","full_name":"Upmann, Matthias","first_name":"Matthias"}],"intvolume":"         6","type":"scientific_journal_article","title":"Detection of carcass contamination using video image analysis during industrial pig slaughter","doi":"10.3389/frfst.2026.1698416","date_updated":"2026-04-30T13:04:53Z","publication":"Frontiers in Food Science and Technology","status":"public","year":"2026","user_id":"12666","article_number":"1698416","abstract":[{"text":"<jats:p>\r\n                    In this study, we examined the possibility of detecting different types of materials that contaminate carcasses during industrial pig slaughter using video image analysis and artificial intelligence (AI). A camera system was installed between evisceration and\r\n                    <jats:italic>postmortem</jats:italic>\r\n                    meat inspection on an industrial pig slaughter line with a capacity of 12,000 pigs per day. The pigs were photographed using five 2D cameras, and the images were analysed for contamination using an AI-based algorithm. The setup, which was developed and installed by CLK GmbH, performed under industrial conditions. In order to train the system, specifications were created for the most frequently occurring types of contamination, namely, intestinal contents, bile, stomach contents, and tubular rail fat. Afterward, the system was trained using annotated images. In principle, the system was able to recognize all types of contamination on the camera images; even pinhead-sized contaminations were visible. The agreement between the algorithm and the results of an expert assessor who assessed the images online agreed in 60% of the judgements. The agreement between experts using onsite assessment and those using online assessment by images was 73%. Thus, the kappa measure of agreement was κ = 0.1215 (p = 0.0199). Significantly higher recognition rates appear to be possible by adjusting the algorithm and increasing the number of training images. Thus, the system is a useful tool to preselect contaminated carcasses and to support\r\n                    <jats:italic>postmortem</jats:italic>\r\n                    inspection.\r\n                  </jats:p>","lang":"eng"}],"_id":"13721"}]