کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
19758 43125 2015 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Modeling of rheological behavior of honey using genetic algorithm–artificial neural network and adaptive neuro-fuzzy inference system
ترجمه فارسی عنوان
مدل سازی رفتار رئولوژیکی عسل با استفاده از الگوریتم ژنتیک شبکه عصبی مصنوعی و سیستم استنتاج فازی سازگار
کلمات کلیدی
درهم، الگوریتم ژنتیک، رئوئولوژی، تجزیه و تحلیل میزان حساسیت، شبیه سازی، عسل
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی بیو مهندسی (مهندسی زیستی)
چکیده انگلیسی

Knowledge of rheological properties of honey is of great interest to honey handlers, processors and keepers. In this study, genetic algorithm–artificial neural network (GA–ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict the viscosity of four types of honey, two poly floral (Mountain, Forest) and two monofloral (Sunflower, Ivy). The GA–ANN and ANFIS were fed with 3 inputs of water content (15.25–19.92%), temperature (10–30 °C) and shear rate (1–42 s−1). The developed GA–ANN, which included 11 hidden neurons, could predict honey viscosity with correlation coefficient of 0.997. The overall agreement between ANFIS predictions and experimental data was also very good (r=0.999). Sensitivity analysis results showed that temperature was the most sensitive factor for prediction of honey viscosity. Both GA–ANN and ANFIS models predictions agreed well with testing data sets and could be useful for understanding and controlling factors affecting viscosity of honey.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Food Bioscience - Volume 9, 1 March 2015, Pages 60–67
نویسندگان
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