Abstract
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.
Publication Date
2-28-2018
First Page
120
Last Page
123
DOI
10.13652/j.issn.1003-5788.2018.02.026
Recommended Citation
Qi, HOU; Jing, LIU; and Xiao, GUAN
(2018)
"Prediction model of microbial growth based on neural network,"
Food and Machinery: Vol. 34:
Iss.
2, Article 26.
DOI: 10.13652/j.issn.1003-5788.2018.02.026
Available at:
https://www.ifoodmm.cn/journal/vol34/iss2/26
References
[1] PEREZ-RODRIGUEZ F, VALERO A. Predictive microbiology in foods[M]. New York: Springer-Verlag, 2012: 1-10.
[2] WHITING R C, BUCHANAN R L. Predictive food microbiolo-gy[J]. Trends in Food Science and Technology, 1994, 4(1): 6-11.
[3] FUJIKAWA H. Application of the new logistic model to microbial growth prediction in food[J]. Biocontrol Science, 2011, 16(2): 47-54.
[4] BARANYI J. Comparison of stochastic and deterministic conce-pts of bacterial lag[J]. Journal of Theoretical Biology, 1998, 192(3): 403-408.
[5] BARANYI J. Stochastic modelling of bacterial lag phase[J]. International Journal of Food Microbiology, 2002, 73(2/3): 203-206.
[6] MARE Le, ELFWING A, BARANYI A, et al. Modelling the variability of lag time and the first generation times of single cells of E. coli[J]. International Journal of Food Microbiology, 2005, 100(1): 13-19.
[7] HUANG Li-han. Optimization of a new mathematical model for bacterial growth[J]. Food Control, 2013, 32(1): 283-288.
[8] BALSAC E, ALONSOLONSO A A, BANAGA J R. An iterative identi-fication procedure for dynamic modeling of biochemical networks[J]. BioMed Central, 2010, 4(1): 33-46.
[9] FILIPL, BORIS H, MORITZ D. Robust multilobjective optimal control of uncertain (bio)chemical processes[J]. Chemical Engineering Science, 2011, 66(20): 4 670-4 682.
[10] GARCIA-GIMENO R M, HERVAS-MARTINEZ C, SILONIZ M, et al. Improving artificial neural networks with a pruning methodology and genetic algorithms for their application in microbial growth prediction in food[J]. Food Microbiol, 2002, 72(1/2): 19-30.
[11] XIAO Hai-tao, LI Bai-lin, OU Jie. Predicting the bacterial growth in MAP chilled beef by artificial neural networks[J]. Journal of Pure & Applied Microbiology, 2013, 7: 123-129.
[12] KODOGIANNIS V S, PACHIDIS T, KONTOGIANNI E. An intelligent based decision support system for the detection of meat spoilage[J]. Engineering Applications of Artificial Intelligence, 2014, 34(3): 23-36.
[13] BASHEER I, HAJMEER M. Artificial neural networks: fundamentals, computing, design, and application[J]. Journal of Microbiological Methods, 2000, 43(1): 3-31.
[14] 王学武, 谭得健. 神经网络的应用与发展趋势[J]. 计算机工程与应用, 2003, 39(3): 98-100.
[15] LIU Hui, TIAN Hong-qi, LIANG Xi-feng. Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks[J]. Appl. Energy, 2015, 157: 183-194.
[16] PALAEIOS A P, MARIN J M, QUINTO E J, et al. Bayesian modeling of bacterial growth for multiple populations[J]. The Annals of Application Statistics, 2014, 8(3): 1 516-1 537.
[17] OHKOCHI M, KOSEKI S, KUNOU M, et al. Growth modeling of Listeria monocytogenes in pasteurized liquid egg[J]. J Food Prot, 2013, 76(9): 1 549-1 556.
[18] MEJLHOLM O, GUNVIG A, BORGGAARD C, et al. Predicting growth rates and growth boundary of Listeria monocytogenes-an international validation study with focus on processed and ready-to-eat meat and seafood[J]. International Journal of Food Microbiology, 2010, 141(3): 137-150.
[19] ZHOU Kang, ZHONG Kai-cheng, LONG Chao, et al. Development and validation of a predictive model for the growth of salmonella enterica in chicken meat[J]. Journal of Food Safety, 2014, 34(4): 326-332.