Construction of Predictive Models to Describe Apparent and Complex Viscosity Values of O/W Model System Meat Emulsions Using Adaptive Neuro - Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN)


YILMAZ M. T. , Karaman S., Kayacıer A. , DOĞAN M. , Yetim H.

FOOD BIOPHYSICS, cilt.7, ss.329-340, 2012 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 7 Konu: 4
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1007/s11483-012-9271-2
  • Dergi Adı: FOOD BIOPHYSICS
  • Sayfa Sayıları: ss.329-340

Özet

This paper introduces an adaptive neuro - fuzzy inference system (ANFIS) and artificial neural networks (ANN) models to predict the apparent and complex viscosity values of model system meat emulsions. Constructed models were compared with multiple linear regression (MLR) modeling based on their estimation performance. The root mean square error (RMSE), mean absolute error (MAE) and determination coefficient (R (2)) statistics were performed to evaluate the accuracy of the models tested. Comparison of the models showed that the ANFIS model performed better than the ANN and MLR models to estimate the apparent and complex viscosity values of the model system meat emulsions. Coefficients of determination (R (2)) calculated for estimation performance of ANFIS modeling to predict apparent and complex viscosity of the emulsions were 0.996 and 0.992, respectively. Similar R (2) values (0.991 and 0.985) were obtained when estimating the performance of the ANN model. In the present study, use of the constructed ANFIS models can be suggested to effectively predict the apparent and complex viscosity values of model system meat emulsions.
This paper introduces an adaptive neuro–fuzzy inference system (ANFIS) and artificial neural networks (ANN) models to predict the apparent and complex viscosity values of model system meat emulsions. Constructed models were compared with multiple linear regression (MLR) mod-eling based on their estimation performance. The root mean square error (RMSE), mean absolute error (MAE) and deter-mination coefficient (R2) statistics were performed to evaluate the accuracy of the models tested. Comparison of the models showed that the ANFIS model performed better than the ANN and MLR models to estimate the apparent and complex vis-cosity values of the model system meat emulsions. Coeffi-cients of determination (R2) calculated for estimation performance of ANFIS modeling to predict apparent and complex viscosity of the emulsions were 0.996 and 0.992,respectively. Similar R2 values (0.991 and 0.985) were obtainedwhen estimating the performance of theANN model. In the present study, use of the constructedANFISmodels can be suggested to effectively predict the apparent and complex viscosity values of model system meat emulsions.