Accurate radial basis function based neural network approach for analysis of photonic crystal fibers

2008 ◽  
Vol 40 (11-12) ◽  
pp. 891-905 ◽  
Author(s):  
M. F. O. Hameed ◽  
S. S. A. Obayya ◽  
K. Al-Begain ◽  
A. M. Nasr ◽  
M. I. Abo el Maaty
Keyword(s):  
Neural Network ◽  
Photonic Crystal ◽  
Radial Basis Function ◽  
Basis Function ◽  
Photonic Crystal Fibers ◽  
Network Approach ◽  
Neural Network Approach ◽  
Radial Basis ◽  
Crystal Fibers

A Radial Basis Function Neural Network Approach To Determine the Survival of Listeria monocytogenes in Katiki, a Traditional Greek Soft Cheese

2008 ◽  
Vol 71 (4) ◽  
pp. 750-759 ◽  
Author(s):  
EFSTATHIOS Z. PANAGOU
Keyword(s):  
Neural Network ◽  
Listeria Monocytogenes ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Approach ◽  
F Test ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Radial Basis ◽  
Modified Weibull

A radial basis function neural network was developed to determine the kinetic behavior of Listeria monocytogenes in Katiki, a traditional white acid-curd soft spreadable cheese. The applicability of the neural network approach was compared with the reparameterized Gompertz, the modified Weibull, and the Geeraerd primary models. Model performance was assessed with the root mean square error of the residuals of the model (RMSE), the regression coefficient (R2), and the F test. Commercially prepared cheese samples were artificially inoculated with a five-strain cocktail of L. monocytogenes, with an initial concentration of 106 CFU g −1 and stored at 5, 10, 15, and 20°C for 40 days. At each storage temperature, a pathogen viability loss profile was evident and included a shoulder, a log-linear phase, and a tailing phase. The developed neural network described the survival of L. monocytogenes equally well or slightly better than did the three primary models. The performance indices for the training subset of the network were R2 = 0.993 and RMSE = 0.214. The relevant mean values for all storage temperatures were R2 = 0.981, 0.986, and 0.985 and RMSE = 0.344, 0.256, and 0.262 for the reparameterized Gompertz, modified Weibull, and Geeraerd models, respectively. The results of the F test indicated that none of the primary models were able to describe accurately the survival of the pathogen at 5°C, whereas with the neural network all f values were significant. The neural network and primary models all were validated under constant temperature storage conditions (12 and 17°C). First or second order polynomial models were used to relate the inactivation parameters to temperature, whereas the neural network was used a one-step modeling approach. Comparison of the prediction capability was based on bias and accuracy factors and on the goodness-of-fit index. The prediction performance of the neural network approach was equal to that of the primary models at both validation temperatures. The results of this work could increase the knowledge basis for the applicability of neural networks as an alternative tool in predictive microbiology.

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