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Paper Information

Journal:   ELECTRONIC JOURNAL OF FOOD PROCESSING AND PRESERVATION   2017 , Volume 8 , Number 2 ; Page(s) 25 To 42.
 
Paper: 

PREDICTION AND ASSURANCE OF VIRGIN OLIVE OIL QUALITY BY USING THE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS)

 
 
Author(s):  RAFIEI NAZARI R., KAKOEI H., ARABAMERI M.
 
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Abstract: 

Background and objective: The prediction of olive oil quality parameters is of great importance in modern approaches to quality control. One of the main problems in predicting olive oil quality during storage is the complexity of the physicochemical properties of raw material and the difference in data of planting, varieties, method of extraction, the type and amounts of the components of olive oil. Oxidative stability is one of the most important parameters in controlling virgin olive oil. Modeling the oxidative stability of olive oil with Adaptive neuro fuzzy inference system (ANFIS) can help to improve the quality control process for this product.
Materials and methods: Parameters of the model design were acidity, peroxide value (PV) specific extinction coefficient K232, phenolic compounds as input variables and the extinction coefficient k270 as the output. In order to develop ANFIS model, number of membership functions, and different learning cycles by trial and error were used. Then, the network was trained for every different pattern models and the best model was selected based on statistical criteria.
Results: This study has shown that some analyses such as acidity, peroxide value (PV) specific extinction coefficient K232, phenolic compounds are valuable in illustrating oxidative stability of olive oil, however no individual test can identify all problems associated with storage conditions or aging. Peroxide values and acidity were obtained between 7 to 15 mmol-equiv/kg and 0.16 to 0.84 mg KOH/g, respectively. The concentration of total phenols varied 77 from 381 to mg/kg. The lowest extinction coefficients k270 and k232 were found to be 0.1 and 1.73, while the highest values were 0.25 (k270) and 3.14 (k232). ANFIS technique was successfully used to modeling the rates of changes of physicochemical parameters associated to oxidative stability of olive oil. The best model with the least mean square error 0.012 and the best regression coefficient of 0.99 was obtained using trapezoidal membership functions, numbers memberships of 3 3 3 3 3 and learning cycle of 5.
Conclusion: Analysis of the model revealed that the ANFIS is a powerful tool to predict the oxidative stability of olive oil. Therefore, by using the information from ANFIS model, olive oil producers will be able to predict olive oil quality.

 
Keyword(s): SPECIFIC EXTINCTION COEFFICIENT, ADAPTIVE NEURO FUZZY INFERENCE SYSTEM, VIRGIN OLIVE OIL, NONLINEAR MODEL
 
 
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Click to Cite.
APA: Copy

RAFIEI NAZARI, R., & KAKOEI, H., & ARABAMERI, M. (2017). PREDICTION AND ASSURANCE OF VIRGIN OLIVE OIL QUALITY BY USING THE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS). ELECTRONIC JOURNAL OF FOOD PROCESSING AND PRESERVATION, 8(2 ), 25-42. https://www.sid.ir/en/journal/ViewPaper.aspx?id=569852



Vancouver: Copy

RAFIEI NAZARI R., KAKOEI H., ARABAMERI M.. PREDICTION AND ASSURANCE OF VIRGIN OLIVE OIL QUALITY BY USING THE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS). ELECTRONIC JOURNAL OF FOOD PROCESSING AND PRESERVATION. 2017 [cited 2021May09];8(2 ):25-42. Available from: https://www.sid.ir/en/journal/ViewPaper.aspx?id=569852



IEEE: Copy

RAFIEI NAZARI, R., KAKOEI, H., ARABAMERI, M., 2017. PREDICTION AND ASSURANCE OF VIRGIN OLIVE OIL QUALITY BY USING THE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS). ELECTRONIC JOURNAL OF FOOD PROCESSING AND PRESERVATION, [online] 8(2 ), pp.25-42. Available: https://www.sid.ir/en/journal/ViewPaper.aspx?id=569852.



 
 
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