{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T14:01:37Z","timestamp":1790604097184,"version":"4.1.0"},"reference-count":12,"publisher":"SAGE Publications","issue":"11","license":[{"start":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T00:00:00Z","timestamp":1783209600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2022YFF1100700"],"award-info":[{"award-number":["2022YFF1100700"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Foodborne Pathogens and Disease"],"published-print":{"date-parts":[[2026,11,1]]},"abstract":"\n Microbial contamination in ready-to-eat (RTE) meat products poses a significant food safety risk, as evidenced by previous reports of high aerobic plate counts and pathogen prevalence. Understanding the post-unpacking dynamics of bacterial communities is crucial for guiding consumer practices. In this study, 90 RTE meat samples of which the varieties include beef products, chicken products, duck products, pig products, mutton products, and donkey products, were collected to analyze microbial community structure by 16S rRNA amplicon sequencing. These samples were collected on the 7th, 14th and 21st day respectively after unpacking. In all these samples, Proteobacteria and Firmicutes made up more than 98% of total bacteria.\n Pseudomonas<\/jats:italic>\n ,\n Staphylococcus<\/jats:italic>\n ,\n Psychrobacter<\/jats:italic>\n ,\n Carnobacterium<\/jats:italic>\n , and\n Shewanella<\/jats:italic>\n were the most abundant genera on average in the 90 samples. Microbial communities of samples on day 21 were significantly different (\n p<\/jats:italic>\n < 0.05) from those on day 7 and day 14. The patterns of microbial community change over time were similar between the two sampling sites, Shandong and Henan. On day 14 and day 21, the microbial group clusters were significantly different from each other when grouped by unpacking site factor (Permanova index < 0.05). The abundance of potential spoilage and pathogenic bacteria, such as\n Pseudomonas, Staphylococcus,<\/jats:italic>\n and\n Enterobacteriaceae<\/jats:italic>\n , increased after 14 days of storage. Variety-specific operational taxonomic units showed very low abundance in the microbial community. In conclusion, this study demonstrates that the unpacking environment, rather than the meat species, becomes the dominant factor influencing bacterial community succession after 14 days of storage. This finding highlights the critical importance of minimizing post-unpacking contamination and suggests that the storage time after opening should be strictly controlled, particularly beyond 2 weeks, to mitigate the growth of potential spoilage and pathogenic bacteria.\n <\/jats:p>","DOI":"10.1177\/15353141261466183","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:50:26Z","timestamp":1783299026000},"page":"781-788","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Unpacking Environment Overrides Meat Type in Shaping the Bacterial Community and Potential Spoilage Organisms of Ready-to-Eat Meats During Refrigerated Storage"],"prefix":"10.1177","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9750-3645","authenticated-orcid":false,"given":"Shuran","family":"Yang","sequence":"first","affiliation":[{"name":"NHC Key Laboratory of Food Safety Risk Assessment, Chinese Academy of Medical Science Research Unit (2019RU014), China National Center for Food Safety Risk Assessment, Beijing City, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaozhe","family":"Qi","sequence":"additional","affiliation":[{"name":"Standards and Quality Center of National Food and Strategic Reserves Administration, Beijing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixin","family":"Peng","sequence":"additional","affiliation":[{"name":"NHC Key Laboratory of Food Safety Risk Assessment, Chinese Academy of Medical Science Research Unit (2019RU014), China National Center for Food Safety Risk Assessment, Beijing City, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Bai","sequence":"additional","affiliation":[{"name":"NHC Key Laboratory of Food Safety Risk Assessment, Chinese Academy of Medical Science Research Unit (2019RU014), China National Center for Food Safety Risk Assessment, Beijing City, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dajin","family":"Yang","sequence":"additional","affiliation":[{"name":"NHC Key Laboratory of Food Safety Risk Assessment, Chinese Academy of Medical Science Research Unit (2019RU014), China National Center for Food Safety Risk Assessment, Beijing City, China."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,7,5]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.f.303"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijfoodmicro.2022.110042"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1128\/AEM.03006-05"},{"issue":"12","key":"e_1_3_2_5_1","first-page":"222","article-title":"The european union one health 2022 Zoonoses Report","volume":"21","author":"Efsa EFSA","year":"2023","unstructured":"Efsa EFSA, , Ecdc PC. The european union one health 2022 Zoonoses Report. EFSA Journal, 2023; 21(12):222.","journal-title":"EFSA Journal"},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/toxsci\/kfu128"},{"issue":"1","key":"e_1_3_2_7_1","first-page":"1","article-title":"PAST: Paleontological statistics software package for education and data analysis","volume":"4","author":"Hammer Y","year":"2001","unstructured":"Hammer Y, , Harper DA, , Ryan PD. PAST: Paleontological statistics software package for education and data analysis. Palaeontologia Electronica, 2001; 4(1):1\u20139.","journal-title":"Palaeontologia Electronica"},{"key":"e_1_3_2_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btr507"},{"key":"e_1_3_2_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/foods11223733"},{"key":"e_1_3_2_10_1","doi-asserted-by":"publisher","DOI":"10.1128\/aem.00062-07"},{"key":"e_1_3_2_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodcont.2018.03.027"},{"key":"e_1_3_2_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/foods11152307"},{"key":"e_1_3_2_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11684-017-0608-6"}],"container-title":["Foodborne Pathogens and Disease"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15353141261466183","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/15353141261466183","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15353141261466183","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T13:19:57Z","timestamp":1790601597000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/15353141261466183"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,5]]},"references-count":12,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2026,11,1]]}},"alternative-id":["10.1177\/15353141261466183"],"URL":"https:\/\/doi.org\/10.1177\/15353141261466183","relation":{},"ISSN":["1535-3141","1556-7125"],"issn-type":[{"value":"1535-3141","type":"print"},{"value":"1556-7125","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,5]]}}}