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

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 <i>Streptococcus, Carnobacterium, Serratia</i> and <i>Photobacterium</i> 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.1. 本研究旨在分析冷藏鸡肉在储存过程中的微生物群落组成变化，并鉴定与肉类新鲜度相关的微生物生物标志物。 2. 本研究采用16S核糖体DNA（16S rDNA）测序技术，追踪冷藏鸡肉储存期间的微生物群落演替。分析了微生物群落组成与储存时间之间的关联，并鉴定了相关微生物生物标志物。 3. 研究结果表明，冷藏鸡肉的微生物多样性随储存时间延长而降低。分别采用正交偏最小二乘（orthogonal partial least squares, OPLS）回归法与随机森林回归法，共鉴定出27种和24种微生物生物标志物。受试者工作特征（receiver operating characteristic, ROC）曲线分析显示，OPLS回归法在鉴定新鲜度相关生物标志物方面表现更优。通过多元逐步回归分析，筛选出包括链球菌属（Streptococcus）、肉杆菌属（Carnobacterium）、沙雷氏菌属（Serratia）以及发光杆菌属（Photobacterium）在内的4个关键微生物生物标志物，并构建了预测模型。 4. 本研究获得了与冷藏鸡肉新鲜度相关的微生物生物标志物及预测模型，上述研究结果为开发冷藏鸡肉新鲜度检测方法提供了理论基础。