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http://dx.doi.org/10.9717/kmms.2019.22.7.790

Construction of Abalone Sensory Texture Evaluation System Based on BP Neural Network  

Li, Xiaochen (Dept of Mechanical Engineering, Dalian Polytechnic University)
Zhao, Yuyang (Dept of Mechanical Engineering, Dalian Polytechnic University)
Li, Renjie (Dept of Mechanical Engineering, Dalian Polytechnic University)
Zhang, Ning (Dept of Information Communications Engineering, Tongmyong University)
Tao, Xueheng (Dept of Mechanical Engineering, Dalian Polytechnic University)
Lee, Eung-Joo (Dept of Information Communications Engineering, Tongmyong University)
Publication Information
Journal of Korea Multimedia Society / v.22, no.7, 2019 , pp. 790-803 More about this Journal
Abstract
The effects of different heat treatments on the sensory characteristics of abalones are studied in this study. In this paper, the sensory evaluation of abalone samples under different heat treatment conditions is carried out, and the evaluation results are analyzed. The three-dimensional (3D) scanning and reverse engineering are used in tooth modeling of the sensory evaluation of abalone samples under different heat treatment conditions. Besides, the chewing movement models are simplified into three modes, including the cutting mode, compressing mode and grinding mode, which are simulated using finite element simulation. The elastic modulus of the abalone samples is obtained through the compression testing using a texture analyzer to distinguish their material properties under different heat treatments and to obtain simulated mechanical parameters. Finally, taking the mechanical parameters of the finite element simulation of abalone chewing as input and sensory evaluation parameters as the output, BP neural network is established in which the sensory texture evaluation model of abalone samples is obtained. Through verification, the neural network prediction model can meet the requirements of food texture evaluation, with an average error of 9.12%.
Keywords
Abalone; Heat Treatment; Sensory Evaluation; Finite Element Simulation; BP Neural Network;
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