Predicting Total Acceptance of Ice Cream Using Artificial Neural Network

Artificial neural network (ANN) models were used to predict the total acceptance of ice cream. The experimental sensory attributes (appearance, flavor, body and texture, coldness, firmness, viscosity, smoothness and liquefying rate) were used as inputs and independent total acceptance was output of ANN. Thirty, ten and sixty percent of the sensory attributes data were used to train, validate and test the ANN model, respectively. It was found that ANN with one hidden layer comprising 10 neurons gives the best fitting with the experimental data, which made it possible to predict total acceptance with acceptable mean absolute errors (0.27) and correlation coefficients (0.96). Sensitivity analysis results showed that flavor and texture were the most sensitive sensory attribute for prediction of total acceptance of ice cream. These results indicate that ANN model could potentially be used to estimate total sensory acceptance of ice cream.

[1]  R. Baer,et al.  Effect of Emulsifiers and Food Gum on Nonfat Ice Cream , 1999 .

[2]  A. Akesowan.  Influence of Soy Protein Isolate on Physical and Sensory Properties of Ice Cream , 2009 .

[3]  Madhukar G. Bhotmange,et al.  Application of Artificial Neural Networks to Food and Fermentation Technology , 2011 .

[4]  Manjeet S. Chinnan,et al.  Optimization of a chocolate-flavored, peanut-soy beverage using response surface methodology (RSM) as applied to consumer acceptability data , 2008 .

[5]  Noah Lotan,et al.  Information Processing by Biochemical Systems: Neural Network-Type Configurations , 2009 .

[6]  O. B. Karaca,et al.  The effects of the combined use of stabilizers containing locust bean gum and of the storage time on Kahramanmaraş‐type ice creams , 2003 .

[7]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[8]  Ali Mohebbi,et al.  A neural network for predicting saturated liquid density using genetic algorithm for pure and mixed refrigerants , 2008 .

[9]  B. Mckenna,et al.  15 – Factors affecting texture of ice cream , 2003 .

[10]  J. Stokols,et al.  PROFILING OF SENSORY EVALUATION OF A NO-SUGAR-ADDED VANILLA ICE CREAM AMONG SPECIFIC AGE AND GENDER POPULATIONS , 2005 .

[11]  Zata M Vickers,et al.  Optimization of Cheddar Cheese Taste in Model Cheese Systems , 2006 .

[12]  Howard R. Moskowitz,et al.  Sensory and Consumer Research in Food Product Design and Development: Moskowitz/Sensory , 2007 .

[13]  Gilles Trystram,et al.  Modelling the operator know-how to control sensory quality in traditional processes , 2007 .

[14]  C. Soukoulis,et al.  Study of the functionality of selected hydrocolloids and their blends with κ-carrageenan on storage quality of vanilla ice cream , 2008 .

[15]  Abderrahim Abbas,et al.  Modeling of an RO water desalination unit using neural networks , 2005 .

[16]  J. Laîné,et al.  Neural networks for prediction of ultrafiltration transmembrane pressure – application to drinking water production , 1998 .

[17]  Tom Duckett,et al.  A computer vision system for appearance-based descriptive sensory evaluation of meals , 2007 .

[18]  Shyam S. Sablani,et al.  Artificial Neural Network Modeling , 2006 .

[19]  Shiv O. Prasher,et al.  ARTIFICIAL NEURAL NETWORK MODELING OF HYPERSPECTRAL RADIOMETRIC DATA FOR QUALITY CHANGES ASSOCIATED WITH AVOCADOS DURING STORAGE , 2011 .

[20]  Belinda Vallejo-Cordoba,et al.  Predicting Milk Shelf‐life Based on Artificial Neural Networks and Headspace Gas Chromatographic Data , 1995 .

[21]  Nidal Hilal,et al.  Neural network modeling for separation of bentonite in tubular ceramic membranes , 2008 .

[22]  A. Mohammad.  MODELING AND OPTIMIZATION OF MUCILAGE EXTRACTION FROM LALLEMANTIA ROYLEANA: A RESPONSE SURFACE – GENETIC ALGORITHM APPROACH , 2007 .

[23]  W. Richard Bowen,et al.  Predicting salt rejections at nanofiltration membranes using artificial neural networks , 2000 .

[24]  Ş. Kurt,et al.  Optimization of emulsion characteristics of beef, chicken and turkey meat mixtures in model system using mixture design. , 2006, Meat science.

[25]  Mostafa Mazaheri Tehrani,et al.  Application and Functions of Stabilizers in Ice Cream , 2011 .

[26]  Shankararaman Chellam,et al.  Artificial neural network model for transient crossflow microfiltration of polydispersed suspensions , 2005 .

[27]  Clay King,et al.  Sensory Evaluation in Snack Foods Development and Production , 2001 .

[28]  S. Turgeon,et al.  Formula Optimization of a Low-fat Food System Containing Whey Protein Isolate- Xanthan Gum Complexes as Fat Replacer , 2005 .

[29]  Anthony E. Beezer,et al.  ARTIFICIAL NEURAL NETWORKS IN MODELING OSMOTIC DEHYDRATION OF FOODS , 2008 .

[30]  M. Bomio.  Neural networks and the future of sensory evaluation , 1998 .

[31]  Gilles Trystram,et al.  Optimisation of the meat emulsification process using at-line human evaluations and the Simplex method , 2004 .

[32]  S. Arntfield,et al.  Textural analysis of fat reduced vanilla ice cream products , 2001 .

[33]  H. Heymann,et al.  Effect of milk fat content on flavor perception of vanilla ice cream. , 1997, Journal of dairy science.

[34]  Andreas Almqvist,et al.  On the influence of surface roughness on real area of contact in normal, dry, friction free, rough contact by using a neural network , 2007 .

[35]  A. Riazi,et al.  A compositional study on two current types of salep in Iran and their rheological properties as a function of concentration and temperature , 2007 .

[36]  Ali Mortazavi,et al.  Dynamic modelling of milk ultrafiltration by artificial neural network , 2003 .

[37]  H. .,et al.  Effect of Skim Milk in Soymilk Blend on the Quality of Ice Cream , 2003 .

[38]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[39]  Howard R. Moskowitz,et al.  Sensory and Consumer Research in Food Product Design and Development , 2006 .