Bread Shrimp Microbe Growth Simulation and Prediction System Based on Neural Network
International Journal of Intelligent Information Systems
Volume 5, Issue 2, April 2016, Pages: 25-36
Received: Jan. 22, 2016; Accepted: Feb. 3, 2016; Published: Mar. 14, 2016
Views 4443      Downloads 135
Xiao Laisheng, Educational Information Center, Guangdong Ocean University, Zhanjiang, China
Zheng Yuandan, Information College, Guangdong Ocean University, Zhanjiang, China
Article Tools
Follow on us
According to the requirements of a scientific research project, a set of bread shrimp microbial growth simulation and prediction system is designed and implemented in detail. The system is established by taking vibrio parahemolyticus in bread shrimp as research objects, according to effects of temperature, salt and time on their growth, and employing neural network technology. In order to improve its compatibility, the system is developed by using C# on Visual Studio 2008 platform, and its design and implementation are based on Aforge.NET framework and sliding-window modeling method. The system consists of three parts: data management, data simulation and data prediction, which would provide an effective analytical tool for bread shrimp safe production. After tested carefully, the system can meet the requirements of the project design.
Neural Network, AForge.NET, Simulation and Prediction System, Microbial Growth, Bread Shrimp
To cite this article
Xiao Laisheng, Zheng Yuandan, Bread Shrimp Microbe Growth Simulation and Prediction System Based on Neural Network, International Journal of Intelligent Information Systems. Vol. 5, No. 2, 2016, pp. 25-36. doi: 10.11648/j.ijiis.20160502.11
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Xiao Laisheng, A Neural Network-Based Multi-Dimensional Simulation Modeling Approach for Food Microbial Growth, Advanced Science Letters, ISSN: 1936-6612, Volume 6, Pages 400-405(15 March 2012).
Wang Zhengxia, Xiao Laisheng, “Prediction Model of Ocean Food Microbe Growth Based on Neural Network and Its Simulation”, CCCA2011, Volume II, p160-165, ISBN: 978-1-61284-102-1, 2011.
Zhou Kang, Liu Shouchun, Li Pinglan, Ma Changwei, Peng Zhaohui, New Advances in Predictive Food Microbial Growth Model, Microbiology, APR 20, 2008, 35(4): 589-594.
Zhang Yuting, Meng Yaquan, Yan Guoting, Application of Matlab in microbial growth forecast model, Hebei Chemical Industry, Vol. 31, No.1, Jan. 2008, pp.20-22.
Zhang Yuting, Wu Kun, Zhang Chunhui, Wang Yufeng, Selection and application of microorganism growth model in cold fresh pork, Meat Industry, 2005, No.11, Totally 295, pp.23-25.
Yang Hongju, Nan Qingxian, Establishment of main corruption microbial growth model in cold pork, Storage and Process, 2004, No.3, pp.7-10.
Liu Xinyou, Nan Haijun, Hao Yaqing, Gao Yuanjun, Tang Xueyan, Zhang Fang, Research on microbial growth model for fresh cut apples in storage period, Journal of Henan Agricultural Sciences, 2007, No.3, pp.88-91.
I. Stamati, F. Logist, E. Van Derlinden, J.-P. Gauchi, J. Van Impe, Optimal experimental design for discriminating between microbial growth models as function of suboptimal temperature, Mathematical Biosciences 250 (2014) 69–80.
I. Stamati, F. Logist, S. Akkermans, E. Noriega Fernández, J. Van Impe, On the effect of sampling rate and experimental noise in the discrimination between microbial growth models in the suboptimal temperature range, Computers and Chemical Engineering 85 (2016) 84–93.
Si Zhu, Guibing Chen, Numerical solution of a microbial growth model applied to dynamic environments, Journal of Microbiological Methods 112 (2015) 76–82.
Anastasia Lytou, Efstathios Z. Panagou, George-John E. Nychas, Development of a predictive model for the growth kinetics of aerobic microbial population on pomegranate marinated chicken breast fillets under isothermal and dynamic temperature conditions, Food Microbiology 55 (2016) 25e31.
Albert Ibarz • Pedro E. D. Augusto, An autocatalytic kinetic model for describing microbial growth during fermentation, Bioprocess Biosyst Eng (2015) 38:199–205.
Long Liu • Zhiguo Guo • Jianjiang Lu •Xiaolin Xu, Kinetic model for microbial growth and desulphurisation with Enterobacter sp., Biotechnol Lett (2015) 37:375–381.
María Jesús Munoz-Lopez, Maureen P. Edwards, Ulrike Schumann and Rober s. Anderssen, Multiplicative modelling of four-phase microbial growth, Pacific Journal ofMathematics for Industry (2015) 7: 7.
Yury V. Bukhman • NathanW. DiPiazza • Jeff Piotrowski • Jason Shao• Adam G. W. Halstead • Minh Duc Bui • Enhai Xie • Trey K. Sato, Modeling Microbial Growth Curves with GCAT, Bioenerg. Res. (2015) 8: 1022–1030.
Yong-guang Yin, Yun Ding, A close to real-time prediction method of total coliform bacteria in foods based on image identification technology and artificial neural network, Food Research International 42 (2009) 191–199.
M. Hajmeer, I. Basheer, A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data, Journal of Microbiological Methods 51 (2002) 217–226.
M. Cheroutre-Vialette, A. Lebert, Application of recurrent neural network to predict bacterial growth in dynamic conditions, International Journal of Food Microbiology 73 (2002) 107–118.
A. H. Geeraerd, C. H. Herremans, C. Cenens, J. F. Van Impe, Application of artificial neural networks as a non-linear modular modeling technique to describe bacterial growth in chilled food products, International Journal of Food Microbiology 44 (1998) 49–68.
Adolf Willem Schepers, Jules Thibault, Christophe Lacroix, Comparison of simple neural networks and nonlinear regression models for descriptive modeling of Lactobacillus helveticus growth in pH-controlled batch cultures, Enzyme and Microbial Technology 26 (2000) 431–445.
Francisco Fernández-Navarroa, Antonio Valero, César Hervás-Martínez, Pedro A. Gutiérrez, Rosa M. García-Gimeno, Gonzalo Zurera-Cosano, Development of a multi-classification neural network model to determine the microbial growth/no growth interface, International Journal of Food Microbiology 141 (2010) 203–212.
Francisco Fernández-Navarroa, César Hervás-Martíneza, M. Cruz-Ramírez, Pedro Antonio Gutiérrez, Antonio Valero, Evolutionary q-Gaussian Radial Basis Function Neural Network to determine the microbial growth/no growth interface of Staphylococcus aureus, Applied Soft Computing 11 (2011) 3012–3020.
Daniel S. Esser • Johan H. J. Leveau • Katrin M. Meyer, Modeling microbial growth and dynamics, Appl Microbiol Biotechnol (2015) 99:8831–8846.
Wang Zhengxia, Xiao Laisheng, Lin Honghong, Qiu Shuzhong, Huang Chiyun, Lei Xiaoling, Intelligent General Predictive Platform for Sea Food Microorganism Growth, Computer Knowledge and Technology, Vol 7, No. 19, July 2011.
Xiao-sheng LIU, Xiao HU, Ting-li WANG, Rapid assessment of flood loss based on neural network ensemble, Trans. Nonferrous Met. Soc. China 24(2014) 2636−2641.
Chengying Gong, Hui He, Research of AForge.NET in Motion Video Detection, Applied Mechanics and Materials Vols. 496-500 (2014), pp 2150-2153.
Suraj Verma*, Prashant Pillai and Yim-Fun Hu, Development of an eye-tracking control system using AForge.NET framework, Int. J. Intelligent Systems Technologies and Applications, Vol. 11, Nos. 3/4, 2012.
ŽIDEK Kamil, RIGASOVá Eva, Diagnostics of Products by Vision System, Applied Mechanics and Materials Vol. 308 (2013) pp 33-38.
Ondrej Krejcar, Utilization of C# Neural Networks Library in Industry Applications, ICeND 2011, CCIS 171, pp. 61-72, 2011.
Laisheng Xiao, “A sliding-window modeling approach for neural network”, International Journal of Control and Automation, ISSN 2005-4297, Vol.7, No.8, Aug. 2014.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186