Volume 19, Issue 125 (2022)                   FSCT 2022, 19(125): 243-253 | Back to browse issues page


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Salehi F, Cheraghi R, Rasouli M. Application of neuro-fuzzy approach for modeling of dehydration process from banana slices by osmosis-ultrasound method. FSCT 2022; 19 (125) :243-253
URL: http://fsct.modares.ac.ir/article-7-59325-en.html
1- Associate Professor, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran , fs1446@yahoo.com
2- MSc Student, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
3- Assistant Professor, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
Abstract:   (987 Views)
Adaptive neuro-fuzzy inference system (neuro-fuzzy or ANFIS) is a well-known hybrid neuro-fuzzy network for modeling complex systems. In this system ,the most frequently used fuzzy clustering method is the fuzzy subtractive clustering algorithm. In this algorithm, a cluster with a certain degree has each data point, explained by a membership function level. In this study, ANFIS model was used for prediction of weight reduction (%), solid gain (%),water loss (%) and rehydration (%) of banana slices dehydrated by osmosis-ultrasound method. The ANFIS model was developed with 3 inputs of sonication power (at three levels of 0, 75 and 150 watts), ultrasound treatment time (at three times of 10, 15 and 20 minutes) and sucrose solution concentration (at three levels of 30, 45 and 60 °Brix) to predict the characteristics of dehydrated banana slices. The calculated coefficient of determination values for prediction of weight reduction (%), solid gain (%),water loss (%) and rehydration (%) of dehydrated banana slices using the ANFIS-based subtractive clustering algorithm were 0.93, 0.95, 0.94, and 0.91, respectively. In general, it can be said that the high coefficients of determination between the experimental results and the outputs of the ANFIS model indicate acceptable accuracy and usability this method in controlling complex processes in the food industry, including dehydration and drying processes.
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Article Type: Original Research | Subject: Food Chemistry
Received: 2022/02/5 | Accepted: 2022/06/6 | Published: 2022/07/1

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