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预测冷藏鸡肉新鲜度的微生物菌群的潜在影响。

The potential effect of microbiota in predicting the freshness of chilled chicken.

机构信息

College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China.

Joint International Research Laboratory of Agriculture and Agri-Product Safety, Ministry of Education, Yangzhou University, Yangzhou, Jiangsu, China.

出版信息

Br Poult Sci. 2022 Jun;63(3):360-367. doi: 10.1080/00071668.2021.2003753. Epub 2021 Dec 13.

Abstract
  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 and 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.
摘要
  1. 本研究旨在分析冷藏鸡肉在储存过程中微生物群落组成的变化,并鉴定与肉品新鲜度相关的微生物生物标志物。

  2. 研究采用 16S rDNA 测序技术跟踪冷藏鸡肉在储存过程中的微生物群落变化。分析了微生物群落组成与储存时间之间的相关性,并鉴定了微生物生物标志物。

  3. 结果表明,冷藏鸡肉的微生物多样性随储存时间的延长而降低。分别使用正交偏最小二乘(OPLS)和随机森林回归方法鉴定出 27 种和 24 种微生物生物标志物。接收器操作特征(ROC)曲线分析表明,OPLS 回归方法在鉴定与新鲜度相关的生物标志物方面具有更好的性能。多元逐步回归分析鉴定出 4 个关键的微生物生物标志物,包括 和 属,并构建了预测模型。

  4. 本研究提供了与冷藏鸡肉新鲜度相关的微生物生物标志物和模型。这些发现为开发冷藏鸡肉新鲜度检测方法提供了依据。

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