Abstract
The correctness of sensory assessment of food quality based on machine learning approach is significantly growing in the food industry. It contributes to an improvement of the food composition and develops the new food products. However, this process requires human intervention. And thus, it is costly, time consuming and easily be biased. In this paper, we propose the Active learning method based on Sequential minimizing optimization in order to evaluate sensory of red wine quality. The general idea is letting the algorithm choose the most uncertain products and asking experts for their opinions. This scheme greatly reduces the number of labels needed for the training process, and, consequently leads to the reduction on the cost of the sensory evaluation process. Experimental results show that the prospect of this method can be widely applied in the optimization of food ingredient and consumer tastes from food consumption markets.
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Lu, NV., Huynh, VN., Yuizono, T., Nguyen, TK. (2018). Sensory Quality Assessment of Food Using Active Learning. In: Huynh, VN., Inuiguchi, M., Tran, D., Denoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2018. Lecture Notes in Computer Science(), vol 10758. Springer, Cham. https://doi.org/10.1007/978-3-319-75429-1_17
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