基于神经网络的微生物生长预测模型

Prediction model of microbial growth based on neural network

鉴于现有大多数预测模型都是经验型模型,含有过多没有生物解释的参数,提出一个基于神经网络的非经验型的微生物生长预测模型,并以李斯特菌为研究实例,利用其试验环境的温度、pH值和Aw值建立BP神经网络二级生长模型,在不同环境条件下拟合微生物的生长速率和倍增时间,结合微生物初始浓度对一级模型的时间与微生物生长情况进行预测,最后利用李斯特菌生长数据对模型进行仿真测试。试验结果证明,该模型可以对微生物生长的各个时期进行有效预测,相对于经验模型,该模型更加适用于微生物生长动力学预测,有效地解决了经验型模型的参数问题。

Most of the existing predictive models are empirical models which contain too many parameters without biological explanations.In this study,a non-empirical growth prediction model based on neural network was proposed.A BP neural network secondary growth model was established by using Listeria monocytogenes as an example,using the temperature,pH value and Aw value of the experimental environment.The growth rate and double time of microbes were fitted in different environments.Subsequently,combining with the initial concentration of microorganisms,the primary model of microorganism growth with time was predicted.Finally,the growth data of Listeria monocytogenes were tested,and the experimental results showed that the model could predict the growth period of microbes.Compared with the empirical model,this non-empirical prediction one was more suitable for predicting the microbial growth dynamics,and also the parameters of the empirical model could be solved effectively.