Abstract
The Taguchi method with fuzzy logic was applied for optimizing the hydrothermal pretreatment of canola with multiple performance responses (oil extraction yield, free acidity and peroxide index) using published data. The canola seeds had been subjected to hydrothermal pretreatments using steam in an autoclave whose base was perforated, under different conditions of temperature (100, 120 and 130 °C), time (5, 15 and 30 min) and granulometry (entire, broken and ground seeds), and the responses were measured as performance characteristics of the process. The output value that represents the responses was called multi-response performance index (MRPI), and the significance of the experimental factors was analyzed by ANOVA. A confirmation test of the optimum parameters was carried out to verify the optimum parameters, obtaining a predicted MRPI of 0.588, while the experimental value was of 0.849, and the MRPI calculated using the predicted values from the literature was of 0.577.
Highlights
Canola oil, widely consumed, has a high content of unsaturated fatty acids and vitamin E (780.2 ppm, Sánchez et al, 2018a)
The results of the analysis of variance showed a larger contribution of parameters B and D on the process, with the latter being the most important, whereas the contribution of parameter C, due its low value, was treated as error using the pooling technique (Özdemir et al, 2004; Fernández et al, 2014) These results are in agreement with those obtained by analyzing the responses of yield, acidity and peroxide index individually (Fernández et al, 2014)
In the case of factor D, the oil yield obtained at level 2 was significantly higher than that obtained at level 3, but the quality indices showed the opposite behaviour, whereas multi-response performance index (MRPI) maintained a constant value for these levels, showing a compensation of the effect of the responses
Summary
Widely consumed, has a high content of unsaturated fatty acids and vitamin E (780.2 ppm, Sánchez et al, 2018a). The Taguchi method has been used for the optimization of a single performance characteristic, and in several works, it has applied to study oil extraction processes. Sánchez et al (2018b) applied an artificial neural network to model the yields of canola oil extraction with different pretreatments. In this context, fuzzy logic is a mathematical theory of inexact reasoning that allows modeling the human reasoning process in linguistic terms (Ross, 2016). Fuzzy logic is a mathematical theory of inexact reasoning that allows modeling the human reasoning process in linguistic terms (Ross, 2016) It is a form of many-valued logic that deals with fluid or approximate reason rather than precise or exact statements. Fuzzy logic has been used to handle the concept of partial truth by Kulekci et al (2016), where the truth value may range between completely true and completely false
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
