{"id":242891,"date":"2026-10-27T11:15:51","date_gmt":"2026-10-27T10:15:51","guid":{"rendered":"https:\/\/www.alimentibevande.it\/uncategorized\/carne-di-pollo-riconoscimento-non-distruttivo-della-sua-freschezza\/"},"modified":"2026-10-27T11:20:22","modified_gmt":"2026-10-27T10:20:22","slug":"chicken-meat-non-destructive-assessment-of-its-freshness","status":"publish","type":"post","link":"https:\/\/www.alimentibevande.it\/en\/scientific-studies\/chicken-meat-non-destructive-assessment-of-its-freshness\/","title":{"rendered":"Chicken meat: non-destructive assessment of its freshness"},"content":{"rendered":"\n<p>Assessing the freshness of chicken meat is crucial for food safety; however, it presents challenges such as high costs, detection difficulties, and low efficiency.<\/p>\n\n\n\n<p>A recent <strong>study <\/strong>by a group of Chinese researchers (Jian et al., 2025) aimed to use a <strong>deep-learning algorithm<\/strong> as a <strong>non-destructive method<\/strong> for this assessment, based simply on the analysis of images.<\/p>\n\n\n\n<p>Using the total aerobic count (TAC), coliform count (ANC), and pH value, the product\u2019s freshness was categorized into seven levels (fresh, slightly fresh 1, slightly fresh 2, slightly fresh 3, spoiled 1, spoiled 2, and spoiled 3).<\/p>\n\n\n\n<p>The dataset was expanded through eight types of data augmentation, resulting in a total of 34,380 samples.<\/p>\n\n\n\n<p>In summary, the <strong>results <\/strong>demonstrate that the proposed method can classify samples according to their freshness level with an accuracy of 95.6%, under various lighting conditions.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Bibliographic references<\/strong>: <em>R. Jian et al.,<\/em> Scientific Reports, 15, 2025, 27538.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"su-button-center\"><a href=\"https:\/\/www.tecnichenuove.com\/riviste\/macchine-alimentari\" class=\"su-button su-button-style-default su-button-wide\" style=\"color:#121010;background-color:#fab21d;border-color:#c88f18;border-radius:12px\" target=\"_blank\" rel=\"noopener noreferrer\"><span style=\"color:#121010;padding:0px 30px;font-size:22px;line-height:44px;border-color:#fcca61;border-radius:12px;text-shadow:none\"> <strong>Article taken from the November issue of the magazine \u201cMacchine Alimentari\u201d. To continue reading, click here and subscribe<\/strong><\/span><\/a><\/div>\n","protected":false},"excerpt":{"rendered":"<p>To evaluate it, a study used a deep-learning algorithm as a non-destructive method, by simply analyzing images<\/p>\n","protected":false},"author":3836,"featured_media":242889,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[20580],"tags":[20939,23170],"tipologia":[],"coauthors":[20682],"class_list":["post-242891","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-scientific-studies","tag-chicken-meat","tag-deep-learning"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Chicken meat: non-destructive assessment of its freshness - Alimentibevande.it<\/title>\n<meta name=\"description\" content=\"To evaluate it, a study 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