Use of ComBase data to develop an artificial neural network model for nonthermal inactivation of Campylobacter jejuni in milk and beef and evaluation of model performance and data completeness using the acceptable prediction zones method
Use of ComBase data to develop an artificial neural network model for nonthermal inactivation of Campylobacter jejuni in milk and beef and evaluation of model performance and data completeness using the acceptable prediction zones method
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DOI：
10.1111/jfs.12983
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发表时间：
2022-05-04
影响因子：
2.4
通讯作者：
Oscar, Thomas P.
中科院分区：
文献类型：
--
作者：
Boleratz, Bethany L.;Oscar, Thomas P.
ComBase是一个应用广泛的微生物模型数据库。ComBase数据可用于开发和验证模型，并测试新的建模方法，如人工神经网络(ANN)和可接受预测区(APZ)，这些方法已被证明优于传统方法。在这里，Combase数据被用来评估ANN和APZ方法对牛奶和牛肉中空肠弯曲杆菌非热灭活的建模方法，作为时间、温度(-20、1、10、20、30和40摄氏度)和应变(18177，ATCC 29428)的函数。使用Excel和NeuralTools开发了4个神经网络，其中性能最好的是通用回归神经网络(GRNN)，其性能和数据完整性使用APZ方法进行了评估。在GRNN模型中，时间、温度、食物和应变的相对变量影响分别为42.5%、31.5%、20.1%和5.9%。空肠弯曲菌在常温下的非热灭活比在低温下更快、更强，在牛奶中比在牛肉中更快、更大，但在1摄氏度时情况相似。对于单独的非热灭活曲线，APZ中的残留物比例(PAPZ)在0.77到1之间。尽管该模型具有可接受的性能(Papz>；=0.7)，但它没有通过验证，因为每个自变量组合有一个而不是四个重复的数据差距，并且在零下10摄氏度没有数据。因此，在该模型可以自信地用于预测空肠弯曲菌在牛奶和牛肉中的行为之前，需要填补这些和其他已确定的数据差距。然而，结果表明，ANN和APZ方法可以用来对食品中空肠弯曲菌的非热灭活数据进行建模。
ComBase is a widely used microbial modeling database. ComBase data can be used to develop and validate models and to test novel modeling methods like artificial neural networks (ANN) and acceptable prediction zones (APZ), which have been shown to outperform traditional methods. Here, ComBase data were used to evaluate the ANN and APZ methods for modeling nonthermal inactivation of Campylobacter jejuni in milk and beef as a function of time, temperature (-20, 1, 10, 20, 30, and 40 degrees C), and strain (18177, ATCC 29428). Four ANN were developed using Excel and NeuralTools, and the best-performing was a general regression neural network (GRNN) whose performance and data completeness were evaluated using the APZ method. Relative variable impacts in the GRNN model were 42.5%, 31.5%, 20.1%, and 5.9% for time, temperature, food, and strain, respectively. Nonthermal inactivation of C. jejuni was faster and greater at ambient than at cold temperatures and in milk than in beef except at 1 degrees C where it was similar. The proportion of residuals in the APZ (pAPZ) ranged from 0.77 to 1 for individual nonthermal inactivation curves. Although the model had acceptable performance (pAPZ >= 0.7), it failed validation because of data gaps like one instead of four replicates per combination of independent variables and no data at -10 degrees C. Thus, these and other data gaps identified need to be filled before the model can be used with confidence to predict behavior of C. jejuni in milk and beef. Nonetheless, results indicated that ANN and APZ methods can be used to model data for nonthermal inactivation of C. jejuni in food.