A Deep Learning Method for Yogurt Preferences Prediction Using Sensory Attributes
Kexin Bi, Tong Qiu, Yizhen Huang
Abstract
During the development of innovative products, consumer preferences are the essential factors for yogurt producers to improve their market share. A high-performance prediction method will be beneficial to understand the intrinsic relevance between preferences and sensory attributes. In this study, a novel deep learning method is proposed that uses an autoencoder to extract product features from the sensory attributes scored by experts, and the sensory features acquired are regressed on consumer preferences with support vector machine analysis. Model performance analysis, hedonic contour mapping, and feature clustering were implemented to validate the overall learning process. The results showed that the deep learning model can vouch an acceptable level of accuracy, and the hedonic mapping reflected could supply a great help for producers’ product design or modification. Finally, hierarchical clustering analysis revealed that for all three brands of yogurts, low temperature (4 °C) storage for no more than 4 weeks can promise the highest consumer preferences.