Prediction of consumer liking from trained sensory panel information: Evaluation of neural networks

This study was set out to establish artificial neural networks (ANN) as an alternative to regression methods (multiple linear, principal component and partial least squares regression) to predict consumer liking from trained sensory panel data. The sensory profile and acceptability of 10 market samples of beef bouillon products were measured. The products were distinct as evaluated by the trained sensory panel. A total of 100 regular beef bouillon product users from Manila measured overall liking, flavour, aftertaste and mouthfeel of the products. Curve fitting method was applied to identify sensory drivers of consumer liking. The sensory drivers of consumer liking were used as explanatory variables in artificial neural networks and regression methods. To overcome the limitations of regression methods we have used artificial neural network techniques to model consumer liking score as a function of trained sensory panel scores and achieved quite encouraging results. Our simulation experiments show that though the regression methods such as multiple linear regression (MLR), principal component regression (PCR) and partial least square (PLS) give an accurate prediction of consumer liking scores, this approach is not robust enough to handle the variations normally encountered in trained sensory panel data. ANNs were trained using the sensory panel raw data and transformed data. The networks trained with sensory panel raw data achieved 98% correct learning, the testing was in a range of 28-35%. Suitable transformation method was applied to reduce the variations in trained sensory panel raw data. The networks trained with transformed sensory panel data achieved about 80-90% correct learning and 80-95% correct testing. It is shown that due to its excellent noise tolerance property and ability to predict more than one type of consumer liking using a single model, the ANN approach promises to be an effective modelling tool. (C) 2006 Elsevier Ltd. All rights reserved.