MODELING AND PREDICTING THE OXIDATIVE STABILITY OF OLIVE OIL DURING THE STORAGE TIME AT AMBIENT CONDITIONS USING ARTIFICIAL NEURAL NETWORK

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

RAFIEI NAZARI R.; ARABAMERI M.; NOURI L.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Iranian Journal of Nutrition Sciences and Food Technology Year:2015 | Volume:10 | Issue:1 Page(s): 71-80

Download Full-Text

Persian Verion

View:

900

Download:

Cites:

Information Journal Paper

Title

MODELING AND PREDICTING THE OXIDATIVE STABILITY OF OLIVE OIL DURING THE STORAGE TIME AT AMBIENT CONDITIONS USING ARTIFICIAL NEURAL NETWORK

Author(s)

RAFIEI NAZARI R. | ARABAMERI M. | NOURI L. | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORKQ2

VIRGIN OLIVE OILQ2

OXIDATIVE STABILITYQ2

Abstract

Background and Objectives: OXIDATIVE STABILITY is one of the most significant parameters in maintaining the quality of olive oil during the storage time. The confidence of the stability and quality of olive oil is a great concern for producers and sellers. Therefore, this study aimed at modeling of the oxidation stability of olive oil by using ARTIFICIAL NEURAL NETWORK (ANN) in order to improve the quality control process of this product.Materials and Methods: In the present study, a Feed-forward Neural Network (FF-ANN) was used to estimate the OXIDATIVE STABILITY of olive oils during storage. In the neural network structure, the parameters of acidity, peroxide value (PV) specific extinction coefficient K232, phenolic compounds, structure of saturated and unsaturated fatty acids were used as input variables, and the extinction coefficient k270 was used as the output variable.Results: The Feed-Forward-Back-Propagation network using the Tangent Sigmoid transfer function, Levenberg-Marquardt learning algorithm, and ten neurons in the hidden layer with lowest mean square error, and the best regression coefficient was determined as the best neural model. The regression coefficients of the best FF-ANN model in (30-120-210-300-420) days were 0.936, 0.955, 0.957, 0.974 and 0.9769, respectively and the mean square errors were 0.0057, 0.0015, 0.0012, 0.0046, and 0.0062, respectively.Conclusion: Our analysis demonstrated that FF-ANN was a powerful tool capable to predict OXIDATIVE STABILITY of olive oils during the storage time.

Cites

No record.

References

Cite

APA: Copy

RAFIEI NAZARI, R., ARABAMERI, M., & NOURI, L.. (2015). MODELING AND PREDICTING THE OXIDATIVE STABILITY OF OLIVE OIL DURING THE STORAGE TIME AT AMBIENT CONDITIONS USING ARTIFICIAL NEURAL NETWORK. JOURNAL OF NUTRITION SCIENCES & FOOD TECHNOLOGY, 10(1), 71-80. SID. https://sid.ir/paper/395195/en

Vancouver: Copy

RAFIEI NAZARI R., ARABAMERI M., NOURI L.. MODELING AND PREDICTING THE OXIDATIVE STABILITY OF OLIVE OIL DURING THE STORAGE TIME AT AMBIENT CONDITIONS USING ARTIFICIAL NEURAL NETWORK. JOURNAL OF NUTRITION SCIENCES & FOOD TECHNOLOGY[Internet]. 2015;10(1):71-80. Available from: https://sid.ir/paper/395195/en

IEEE: Copy

R. RAFIEI NAZARI, M. ARABAMERI, and L. NOURI, “MODELING AND PREDICTING THE OXIDATIVE STABILITY OF OLIVE OIL DURING THE STORAGE TIME AT AMBIENT CONDITIONS USING ARTIFICIAL NEURAL NETWORK,” JOURNAL OF NUTRITION SCIENCES & FOOD TECHNOLOGY, vol. 10, no. 1, pp. 71–80, 2015, [Online]. Available: https://sid.ir/paper/395195/en

Related Journal Papers