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Mathematical Models for the Effects of pH Temperature and Sodium Chloride on the Growth of Bacillus stearothermophilus in Salty Carrots

机译:pH温度和氯化钠对咸胡萝卜中嗜热脂肪芽孢杆菌生长的影响的数学模型

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摘要

Estimating the shelf life and safety of any food product is an important part of food product development. Predictive food microbiology reduces the time and expense associated with conventional challenge and shelf life testing. The purpose of this study was to characterize and model germination, outgrowth, and lag (GOL) time and the exponential growth rate (EGR) of Bacillus stearothermophilus in salty carrot medium (SCM) as a function of pH, temperature, and NaCl concentration. B. stearothermophilus is a spore-forming thermophilic organism associated with flat sour spoilage of canned foods. A split-split plot design was used to measure the effects and interactions of pH (5.5 to 7.0), temperature (45 to 60(deg)C), and NaCl (0 to 1%) on the growth kinetics of B. stearothermophilus in SCM. A total of 96 experiments were analyzed, with individual curve parameters determined by using the Gompertz equation. Quadratic polynomial models for GOL time and EGR of B. stearothermophilus in terms of temperature, pH, and NaCl were generated by response surface analysis. The r(sup2) values for the GOL time and EGR models were 0.917 and 0.916, respectively. These models provide an estimate of bacterial growth in response to combinations of the variables studied within the specified ranges. The models were used to predict GOL times and EGRs for additional experimental conditions. The accuracy of these predictions validated the model's predictive ability in SCM.
机译:估计任何食品的保质期和安全性是食品开发的重要组成部分。预测性食品微生物学减少了与常规挑战和保质期测试相关的时间和费用。这项研究的目的是表征和建模盐渍胡萝卜培养基(SCM)中发芽嗜热芽孢杆菌的发芽,生长和滞后(GOL)时间以及指数生长速率(EGR)与pH,温度和NaCl浓度的关系。嗜热脂肪芽孢杆菌是形成孢子的嗜热生物,与罐头食品的扁平酸变质有关。使用分块图设计测量pH(5.5至7.0),温度(45至60°C)和NaCl(0至1%)对嗜热脂肪芽孢杆菌生长动力学的影响和相互作用。单片机。总共分析了96个实验,并使用Gompertz方程确定了各个曲线参数。通过响应面分析生成了嗜热脂肪芽孢杆菌的GOL时间和EGR的二次多项式模型,包括温度,pH和NaCl。 GOL时间模型和EGR模型的r(sup2)值分别为0.917和0.916。这些模型根据指定范围内研究的变量组合提供细菌生长的估计值。该模型用于预测其他实验条件下的GOL时间和EGR。这些预测的准确性验证了模型在SCM中的预测能力。

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