Evaluating the Combined Effect of Temperature, Shear Rate and Water Content on Wild-Flower Honey Viscosity Using Adaptive Neural Fuzzy Inference System and Artificial Neural Networks-Reference-Cited by-同舟云学术

Evaluating the Combined Effect of Temperature, Shear Rate and Water Content on Wild-Flower Honey Viscosity Using Adaptive Neural Fuzzy Inference System and Artificial Neural Networks

Published:2013-04-17 Issue:4 Volume:36 Page:510-520
ISSN:0145-8876
Container-title:Journal of Food Process Engineering
language:en
Short-container-title:J Food Process Eng

Author:

Al-Mahasneh M.A.¹,Rababah T.M.²,Ma'Abreh A.S.³

Affiliation:

1. Department of Chemical Engineering; Jordan University of Science and Technology; Irbid Jordan

2. Department of Nutrition and Food Technology; Jordan University of Science and Technology; Irbid Jordan

3. Department of Agricultural Engineering; King Saud University; Riyadh Saudi Arabia

Publisher

Wiley

Subject

General Chemical Engineering,Food Science

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1111/jfpe.12014/fullpdf

Reference28 articles.

1. Heat effect on rheology of light- and dark-colored honey;Abu-Jdayil;J. Food Eng.,2002

2. Physico-chemical, rheological, calorimetric and dielectric behaviour of selected Indian honey;Ahmed;J. Food Eng.,2007

3. Crystal control in processed liquid honey;Assil;J. Food Sci.,1991

4. Neuro-fuzzy methods for nonlinear system identification;Babuška;Ann. Rev. Control,2003

5. Włas'ciwos'ci reologiczne miodu pszczelego w postaci skrystalizowanej;Bakier;In_Zynieria Rolnicza,2000

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis;Journal of the Mechanical Behavior of Materials;2024-01-01

2. A comprehensive review of food rheology: analysis of experimental, computational, and machine learning techniques;Korea-Australia Rheology Journal;2023-10-28

3. Analytical Rheology of Honey: A State-of-the-Art Review;Foods;2021-07-23

4. Classification and Prediction of Bee Honey Indirect Adulteration Using Physiochemical Properties Coupled with K-Means Clustering and Simulated Annealing-Artificial Neural Networks (SA-ANNs);Journal of Food Quality;2021-04-07

5. Artificial Neural Network and Regression Models to Evaluate Rheological Properties of Selected Brazilian Honeys;Journal of Apicultural Science;2020-11-07