Borneman Darand L, Ingham Steven C, Ané Cécile
Department of Food Science, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA.
J Food Prot. 2009 Jun;72(6):1190-200. doi: 10.4315/0362-028x-72.6.1190.
This study was done to optimize accuracy of predicting growth of Salmonella serovars, Escherichia coli O157:H7, and Staphylococcus aureus in temperature-abused raw beef, poultry, and bratwurst (with salt but without added nitrite). Four mathematical approaches were used with experimentally determined lag-phase duration (LPD) and growth rate (GR) values to develop 12 versions of THERM (Temperature History Evaluation for Raw Meats; http://www.meathaccp.wisc.edu/ THERM/calc.aspx), a computer-based tool that calculates elapsing lag phase or growth that occurs in each entered time interval and sums the results of all intervals to predict growth. Each THERM version utilized LPD values calculated by linear interpolation, quadratic equation, piecewise linear regression, or exponential decay curve and GR values calculated by linear interpolation, quadratic equation, or piecewise linear regression. Each combination of mathematical approaches for LPD and GR calculations was defined as another THERM version. Time, temperature, and pathogen level (log CFU per gram) data were obtained from 26 inoculation experiments with ground beef, pork sausages, and poultry. Time and temperature data were entered into the 12 THERM versions to obtain pathogen growth. Predicted and experimental results were qualitatively described and compared (growth defined as > 0.3-log increase) or quantitatively compared. The 12 THERM versions had qualitative accuracies of 81.4 to 88.6% across 70 combinations of product, pathogen, and experiment. Quantitative accuracies within +/-0.3 log CFU were obtained for 51.4 to 67.2% of the experimental combinations; 82.9 to 88.6% of the quantitative predictions were accurate or fail-safe. Piecewise linear regression or linear interpolation for calculating LPD and GR yielded the most accurate THERM performance.
本研究旨在优化对温度滥用的生牛肉、家禽和德式小香肠（含盐但未添加亚硝酸盐）中沙门氏菌血清型、大肠杆菌O157:H7和金黄色葡萄球菌生长情况的预测准确性。使用了四种数学方法，结合实验测定的滞后期持续时间（LPD）和生长速率（GR）值，开发了12个版本的THERM（生肉温度历史评估；http://www.meathaccp.wisc.edu/THERM/calc.aspx），这是一种基于计算机的工具，可计算每个输入时间间隔内发生的滞后期或生长情况，并将所有间隔的结果相加，以预测生长。每个THERM版本利用通过线性插值、二次方程、分段线性回归或指数衰减曲线计算的LPD值，以及通过线性插值、二次方程或分段线性回归计算的GR值。LPD和GR计算的每种数学方法组合都被定义为另一个THERM版本。从26个关于碎牛肉、猪肉香肠和家禽的接种实验中获取了时间、温度和病原体水平（每克菌落形成单位对数）数据。将时间和温度数据输入到12个THERM版本中，以获得病原体生长情况。对预测结果和实验结果进行了定性描述和比较（生长定义为增加>0.3对数）或定量比较。在产品、病原体和实验的70种组合中，12个THERM版本的定性准确率为81.4%至88.6%。对于51.4%至67.2%的实验组合，获得了±0.3对数CFU以内的定量准确率；82.9%至88.6%的定量预测是准确的或可靠的。用于计算LPD和GR的分段线性回归或线性插值产生了最准确的THERM性能。
