Modeling quality changes of sesame oil during extraction process using intelligent and regression system

Document Type : Research Article

Authors

1 MSc student of Mechanics of Biosystems Engineering Department, Faculty of Agricultural Engineering and Rural Development, Khuzestan Agricultural Sciences and Natural Resources university (KAU), Mollasani, Iran.

2 Assistant professor of Biosystems Engineering Department, Faculty of Agricultural Engineering and Rural Development, , Khuzestan Agricultural Sciences and Natural Resources university (KAU), Mollasani, Iran.

3 Assistant professor of Food Science & Technology, Faculty of Animal Science and Food Technology, Khuzestan Agricultural Sciences and Natural Resources university (KAU), Mollasani, Iran

Abstract

Sesame is one of the most important oil seeds with high nutritional value and high functional properties in the world. Therefore, it is important to model and investigate the relationship between the factors that can affect the quality of sesame oil. . In this research, artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict the quality of sesame oil extracted by the press method. The model obtained from ANN had a higher correlation coefficient and less RMSE in predicting the quality of sesame oil extracted. Results showed the highest of acid values is related to the speed of 20 (rpm) and the lowest value is related to the speed of 80 (rpm), and increase temperature has increased it. Also, the highest of ions values was related to 80 (rpm) and extraction temperature 90 °C (1.13) and the lowest value was 80 (rpm) and 20 °C, (0.312). Increased temperature caused an increase acid value.

Graphical Abstract

Modeling quality changes of sesame oil during extraction process using intelligent and regression system

Highlights

  • The effect of rotational speed, extraction temperature and interaction between them on the acid value of extracted oil was significant (p≤0.01)
  • The effect of rotational speed, extraction temperature and interaction between them on the iodine value of extracted oil was significant (p≤0.01)
  • The model obtained from ANN had a higher correlation coefficient and less RMSE in predicting the quality of sesame oil extracted.

Keywords

Main Subjects

References

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Innovative Food Technologies
Volume 5, Issue 4
November 2018
Pages 627-636

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History

  • Receive Date: 14 January 2018
  • Revise Date: 09 May 2018
  • Accept Date: 18 June 2018

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