Journal article

Development of Artificial Neural Network Models to Assess Beer Acceptability Based on Sensory Properties Using a Robotic Pourer: A Comparative Model Approach to Achieve an Artificial Intelligence System

Claudia Gonzalez Viejo, Damir D Torrico, Frank R Dunshea, Sigfredo Fuentes

Beverages | MDPI | Published : 2019

Abstract

Artificial neural networks (ANN) have become popular for optimization and prediction of parameters in foods, beverages, agriculture and medicine. For brewing, they have been explored to develop rapid methods to assess product quality and acceptability. Different beers (N = 17) were analyzed in triplicates using a robotic pourer, RoboBEER (University of Melbourne, Melbourne, Australia), to assess 15 color and foam-related parameters using computer-vision. Those samples were tested using sensory analysis for acceptability of carbonation mouthfeel, bitterness, flavor and overall liking with 30 consumers using a 9-point hedonic scale. ANN models were developed using 17 different training algorit..

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Grants

Awarded by Australian Government through the Australian Research Council


Funding Acknowledgements

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. This research was supported by the Australian Government through the Australian Research Council [Grant number IH120100053] "Unlocking the Food Value Chain: Australian industry transformation for ASEAN markets". C.G.V. is supported by the Melbourne Research Scholarship from the University of Melbourne.