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The potential effect of microbiota in predicting the freshness of chilled chicken.
Zhang, T; Chen, L; Ding, H; Wu, P F; Zhang, G X; Pan, Z M; Xie, K Z; Dai, G J; Wang, J Y.
Afiliación
  • Zhang T; College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China.
  • Chen L; Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou, Jiangsu, China.
  • Ding H; Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou, Jiangsu, China.
  • Wu PF; College of Veterinary Medicine, Yangzhou University, Yangzhou, Jiangsu, China.
  • Zhang GX; College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China.
  • Pan ZM; Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou, Jiangsu, China.
  • Xie KZ; College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China.
  • Dai GJ; Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou, Jiangsu, China.
  • Wang JY; College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China.
Br Poult Sci ; 63(3): 360-367, 2022 Jun.
Article en En | MEDLINE | ID: mdl-34747672
1. The goals of this study were to analyse the changes in microbiota composition of chilled chicken during storage and identify microbial biomarkers related to meat freshness.2. The study used 16S rDNA sequencing to track the microbiota shift in chilled chicken during storage. Associations between microbiota composition and storage time were analysed and microbial biomarkers were identified.3. The results showed that microbial diversity of chilled chicken decreased with the storage time. A total of 27 and 24 microbial biomarkers were identified by using orthogonal partial least squares (OPLS) and the random forest regression approach, respectively. The receiver operating characteristic (ROC) curve analysis indicated that the OPLS regression approach had better performance in identifying freshness-related biomarkers. The multiple stepwise regression analysis identified four key microbial biomarkers, including Streptococcus, Carnobacterium, Serratia and Photobacterium genera and constructed a predictive model.4. The study provided microbial biomarkers and a model related to the freshness of chilled chicken. These findings provide a basis for developing detection methods of the freshness of chilled chicken.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Pollos / Microbiota Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: Br Poult Sci Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Pollos / Microbiota Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: Br Poult Sci Año: 2022 Tipo del documento: Article País de afiliación: China