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Year 2014, Volume: 6 Issue: 2, 13 - 16, 01.06.2014

Mehdi Kviani Narges Mirsaeed Ghazi Mohammad Ali Shariati Shirin Atarod

https://doi.org/10.24107/ijeas.251227

Abstract

References

  • AndrásFülöp, Jenő Hancsók, ,"Comparison of calibration models based on near infrared spectroscopy data for the determination of plant oil properties",2007.
  • Klaypradit .W, Kerdpiboon.S , Singh.R,"Application of Artificial Neural Networks to Predict the Oxidation of Menhaden Fish Oil Obtained from Fourier Transform Infrared Spectroscopy Method",Journal of Food Bioprocess Technol,2010.
  • Rocco Furferi, Monica Carfagni, Marco Daou .,"Artificial neural network software for realtime estimation of olive oil qualitative parameters during continuous extraction"Computers and Elect in Agri, 55, 2,115-131,2007. M.A.Shariati et al 16
  • Shahamiri ,S,R. An introduction to Neural Network. Electronic journal of Iran Document and Research Center,6 ,1,2005
  • Zheng Hai Jun Wang,2006,"Detection of adulteration in camellia seed oil and sesame oil using an electronic nose" Eur. J. Lipid Sci. Technol. 108, 116–124,2006.

EVALUATION OF ARTIFICIAL NEURAL NETWORK IN DETERMINING THE QUALITY AND CLASSIFICATION OF EDIBLE OIL

Year 2014, Volume: 6 Issue: 2, 13 - 16, 01.06.2014

Mehdi Kviani Narges Mirsaeed Ghazi Mohammad Ali Shariati Shirin Atarod

https://doi.org/10.24107/ijeas.251227

Abstract

This paper reviews the application of artificial neural network (ANN) in determination of the quality and classification of edible oils. This point should be considered that other modern methods for examining these parameters are time consuming, so that presenting new methods which are strongly relevant to determination parameters and yet are quick in respond can help to control the oil quality. Moreover, only one test cannot interpret any terms of experiment. One of latest technologies and developed science achievement is modeling which presents sophisticated tools to analyze, interpret and understand the world around us. Nowadays, with the development of processing technology, benefits of artificial intelligence technology such as artificial neural networks, are widely used to model processes. The results showed that the artificial neural network optimization is a successful method for evaluating the parameters. Ultimately, time saving, cost, experimental errors will lead to closer scrutiny and appropriate matching between experimental data and data obtained from the neural network

Keywords

Artificial Neural Networks Modeling Edible oil

References

  • AndrásFülöp, Jenő Hancsók, ,"Comparison of calibration models based on near infrared spectroscopy data for the determination of plant oil properties",2007.
  • Klaypradit .W, Kerdpiboon.S , Singh.R,"Application of Artificial Neural Networks to Predict the Oxidation of Menhaden Fish Oil Obtained from Fourier Transform Infrared Spectroscopy Method",Journal of Food Bioprocess Technol,2010.
  • Rocco Furferi, Monica Carfagni, Marco Daou .,"Artificial neural network software for realtime estimation of olive oil qualitative parameters during continuous extraction"Computers and Elect in Agri, 55, 2,115-131,2007. M.A.Shariati et al 16
  • Shahamiri ,S,R. An introduction to Neural Network. Electronic journal of Iran Document and Research Center,6 ,1,2005
  • Zheng Hai Jun Wang,2006,"Detection of adulteration in camellia seed oil and sesame oil using an electronic nose" Eur. J. Lipid Sci. Technol. 108, 116–124,2006.
There are 5 citations in total.

Details

Other ID JA66CH44KN
Journal Section Articles
Authors

Mehdi Kviani This is me

Narges Mirsaeed Ghazi This is me

Mohammad Ali Shariati This is me

Shirin Atarod This is me

Publication Date June 1, 2014
Published in Issue Year 2014 Volume: 6 Issue: 2

Cite

APA Kviani, M., Ghazi, N. M., Shariati, M. A., Atarod, S. (2014). EVALUATION OF ARTIFICIAL NEURAL NETWORK IN DETERMINING THE QUALITY AND CLASSIFICATION OF EDIBLE OIL. International Journal of Engineering and Applied Sciences, 6(2), 13-16. https://doi.org/10.24107/ijeas.251227
AMA Kviani M, Ghazi NM, Shariati MA, Atarod S. EVALUATION OF ARTIFICIAL NEURAL NETWORK IN DETERMINING THE QUALITY AND CLASSIFICATION OF EDIBLE OIL. IJEAS. June 2014;6(2):13-16. doi:10.24107/ijeas.251227
Chicago Kviani, Mehdi, Narges Mirsaeed Ghazi, Mohammad Ali Shariati, and Shirin Atarod. “EVALUATION OF ARTIFICIAL NEURAL NETWORK IN DETERMINING THE QUALITY AND CLASSIFICATION OF EDIBLE OIL”. International Journal of Engineering and Applied Sciences 6, no. 2 (June 2014): 13-16. https://doi.org/10.24107/ijeas.251227.
EndNote Kviani M, Ghazi NM, Shariati MA, Atarod S (June 1, 2014) EVALUATION OF ARTIFICIAL NEURAL NETWORK IN DETERMINING THE QUALITY AND CLASSIFICATION OF EDIBLE OIL. International Journal of Engineering and Applied Sciences 6 2 13–16.
IEEE M. Kviani, N. M. Ghazi, M. A. Shariati, and S. Atarod, “EVALUATION OF ARTIFICIAL NEURAL NETWORK IN DETERMINING THE QUALITY AND CLASSIFICATION OF EDIBLE OIL”, IJEAS, vol. 6, no. 2, pp. 13–16, 2014, doi: 10.24107/ijeas.251227.
ISNAD Kviani, Mehdi et al. “EVALUATION OF ARTIFICIAL NEURAL NETWORK IN DETERMINING THE QUALITY AND CLASSIFICATION OF EDIBLE OIL”. International Journal of Engineering and Applied Sciences 6/2 (June 2014), 13-16. https://doi.org/10.24107/ijeas.251227.
JAMA Kviani M, Ghazi NM, Shariati MA, Atarod S. EVALUATION OF ARTIFICIAL NEURAL NETWORK IN DETERMINING THE QUALITY AND CLASSIFICATION OF EDIBLE OIL. IJEAS. 2014;6:13–16.
MLA Kviani, Mehdi et al. “EVALUATION OF ARTIFICIAL NEURAL NETWORK IN DETERMINING THE QUALITY AND CLASSIFICATION OF EDIBLE OIL”. International Journal of Engineering and Applied Sciences, vol. 6, no. 2, 2014, pp. 13-16, doi:10.24107/ijeas.251227.
Vancouver Kviani M, Ghazi NM, Shariati MA, Atarod S. EVALUATION OF ARTIFICIAL NEURAL NETWORK IN DETERMINING THE QUALITY AND CLASSIFICATION OF EDIBLE OIL. IJEAS. 2014;6(2):13-6.

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