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应用微生物预测和机器学习模型数据评估温度和盐浓度对重组鸡肉糜中沙门氏菌和替代菌屎肠球菌热灭活的影响。

Applying microbial predictive and machine learning model data to evaluate thermal inactivation of salmonella and the surrogate enterococcus faecium in reconstructed ground chicken meat affected by temperature and salt concentrations.

作者信息

Coe Corey, Wang Xinhao, Rowen Elizabeth, Stearns Rebecca, Katz Joe, Boltz Timothy, Rion Md Shafiul Islam, Long Carly, Freshour Gary, Jaczynski Jacek, Matak Kristen, Freshour Annette, Luo Yangchao, Shen Cangliang

机构信息

School of Agriculture and Food System, West Virginia University, Morgantown, WV 26506, USA.

Department of Nutritional Sciences, University of Connecticut, Storrs, CT 06269, USA.

出版信息

Poult Sci. 2025 Jun 11;104(9):105422. doi: 10.1016/j.psj.2025.105422.

DOI:10.1016/j.psj.2025.105422
PMID:40543262
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12221626/
Abstract

This study was to model the thermal inactivation of Salmonella and the surrogate Enterococcus faecium in reconstructed ground chicken meat as affected by temperatures and salt concentrations. Fresh chicken breast was ground, inoculated with nalidixic-acid (NaL-100 ppm) resistant S. Typhimurium or E. faecium followed by the addition of NaCl (0, 1.0, 3.0 or 5.0 %) + Na-tripolyphosphate (0.5 %) solutions to achieve 8 % pump rate. Samples (10 g) were placed into filtered food sample bags, vacuum packaged and stored at 4°C for 42-h before heating in a circulated water bath at 62, 66, 70, and 74°C for 0 to 240 s, respectively. Surviving cells were enumerated on NaL-tryptic soy agars. Microbial populations and d-values [n = 6, Global-Fit and United States Department of Agriculture (USDA)-Integrated-Predictive-Modeling-Program software] were analyzed by R-software (orthogonal polynomial model). Machine learning (ML) models including Random Forest, Support Vector, and Bayesian Ridge Regressions were used to predict microbial inhibition under various thermal and salt treatments. Thermal dynamic data fit Weibull Model (RMSE=0.48 to 0.71), but not the linear model (RMSE >1.000). Salt (3 and 5 % vs 0 %) significantly (P < 0.05) increased the thermal resistance of S. Typhimurium (reduction:1.02 vs 1.86 logCFU/g) when heated at 62°C whereas when the heating temperature increased to 74°C the pathogen cells become more vulnerable (reduction: 3.04-4.44 vs 1.63 logCFU/g). E. faecium were resistant to heat compared to Salmonella as shown by fewer reductions (1.02-4.44 vs 4.61-6.94 logCFU/g) and higher (P < 0.05) d-values in all tested samples. ML models suggested that heating temperature was the most important predictor of cell reduction, followed by exposure time and salt concentrations. E. faecium is a promising pathogen surrogate could be used in thermal inactivation validation studies. These results will be useful for the poultry meat industry to develop proper thermal processes for control of pathogen in chicken products.

摘要

本研究旨在模拟重构碎鸡肉中沙门氏菌和替代菌屎肠球菌在不同温度和盐浓度影响下的热失活情况。将新鲜鸡胸肉绞碎,接种对萘啶酸(NaL - 100 ppm)耐药的鼠伤寒沙门氏菌或屎肠球菌,随后添加NaCl(0%、1.0%、3.0%或5.0%)+三聚磷酸钠(0.5%)溶液,使泵入率达到8%。将样品(10 g)放入过滤后的食品样品袋中,真空包装,并在4°C下储存42小时,然后分别在62°C、66°C、70°C和74°C的循环水浴中加热0至240秒。在NaL - 胰蛋白胨大豆琼脂上对存活细胞进行计数。通过R软件(正交多项式模型)分析微生物数量和d值[n = 6,全局拟合和美国农业部(USDA)综合预测建模程序软件]。使用包括随机森林、支持向量和贝叶斯岭回归在内的机器学习(ML)模型来预测各种热和盐处理下的微生物抑制情况。热动力学数据符合威布尔模型(RMSE = 0.48至0.71),但不符合线性模型(RMSE > 1.000)。在62°C加热时,盐(3%和5%对比0%)显著(P < 0.05)提高了鼠伤寒沙门氏菌的热抗性(减少量:1.02对比1.86 logCFU/g),而当加热温度升至74°C时,病原菌细胞变得更易受影响(减少量:3.04 - 4.44对比1.63 logCFU/g)。与沙门氏菌相比,屎肠球菌对热具有抗性,所有测试样品中的减少量较少(1.02 - 4.44对比4.61 - 6.94 logCFU/g)且d值更高(P < 0.05)。ML模型表明,加热温度是细胞减少的最重要预测因素,其次是暴露时间和盐浓度。屎肠球菌是一种有前景的病原菌替代菌,可用于热失活验证研究。这些结果将有助于禽肉行业制定适当的热加工工艺,以控制鸡肉产品中的病原菌。

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Predictive Models May Complement or Provide an Alternative to Existing Strategies for Assessing the Enteric Pathogen Contamination Status of Northeastern Streams Used to Provide Water for Produce Production.
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