The use of response surface methodology (RSM) for improving Freezing point, Cloud point and Pour point of common carp (Cyprinus carpio L.) fish viscera oil extracted by infrared ray

Atheer A. Al–Mtury; Sabah M.  Al–Shatty; Asaad R. Al–Hilphy

doi:10.58629/ijaq.v20i1.457

Authors

Atheer A. Al–Mtury Collage of Agriculture, University of Basrah, Basrah- Iraq
Sabah M. Al–Shatty Collage of Agriculture, University of Basrah, Basrah- Iraq
Asaad R. Al–Hilphy Collage of Agriculture, University of Basrah, Basrah- Iraq

DOI:

https://doi.org/10.58629/ijaq.v20i1.457

Keywords:

Fish waste oil, infrared, freezing point, cloud point, pour point

Abstract

As a result of the special advantages of infrared such as ambient does not affect by IR heating, being environmentally friendly, reducing the size of equipment used in food heating and ease of control. Moreover, it is one of the most promising techniques, so the current study aimed to investigate the application of infrared ray in extracting the oil of common carp fish wastes (Cyprinus carpio L.), and the study of the effect of the independent factors, which are: power (130, 190, 250 watts), temperature (50, 60, 70 ºC), the distance between the radiation source and the surface of the waste (1, 15.5, 30 cm) on the freezing point, cloud point and pour of carp waste oil using response surfaces methodology. The results showed that the lowest practical and predicted values for the above traits were -4.6, -3.1, 11.9, 12.2, 6.8, and 7.01, respectively, and that the increase in power and temperature had a negative effect, as it led to an increase in the cloud point. The results illustrated that the interaction between distance, power and temperature led to a decrease in the freezing point. The results also clarified that the increase in power with a decrease in temperature and stability of the distance led to an increase in the pour point. The results indicated that the optimal conditions were the power 315.94W, the temperature 66.14° C, and the distance is 20.02 cm. The results of the statistical showed that the nonlinear multiple regression equation was used to predict the values of the cloud point, while the multiple linear regression equation was used to predict the pour point values. The nonlinear multiple regression equation had a non-significant effect to predict the freezing point values, with an error rate ranging from 0.32% to 30%.

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Author Biographies

Atheer A. Al–Mtury, Collage of Agriculture, University of Basrah, Basrah- Iraq

Department of Food Science,

Sabah M. Al–Shatty, Collage of Agriculture, University of Basrah, Basrah- Iraq

Department of Food Science,

Asaad R. Al–Hilphy, Collage of Agriculture, University of Basrah, Basrah- Iraq

Department of Food Science,

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