Open AccessResearch on Optimization of Sea Rice Biscuit Based on BP Neural Network
Author(s) -
Rui Feng Han,
Juxian Wu,
Xiaoyan Shi,
Mingjun Li,
Shengquan Ye
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1802/2/022027
Subject(s) - chewiness , artificial neural network , food science , product (mathematics) , computer science , raw material , agricultural engineering , mathematics , statistics , artificial intelligence , engineering , chemistry , ecology , biology , geometry
Sea rice is rich in dietary fiber and selenium, and has the characteristics of low fat and low calories. The main raw material of this paper is sea rice noodles. The sensory evaluation, chewiness and hardness of functional biscuits are the main inspection criteria. Through orthogonal experiments and the use of BP neural network modeling and prediction, the relationship between the three influencing factors of the model and sensory evaluation indicators Carry out modeling and predictive analysis to guide the optimization research of product formula and improve efficiency. The difference between the three predicted values and the actual indicators is small, which are - 0.0073%, -0.0091%, and 0.3225%, respectively, which have a good prediction effect.