Application of Neural Network in Wine Grape Quality Evaluation
- 10.2991/icacsei.2013.158How to use a DOI?
- Single layer forward neural network, Multiple linear regression model, Related weight, Grape wine, Ingredients.
The aim of this paper is to help select suitable grapes for making red wine by studying the correlation between ingredients of the grapes and the resulting wine. Two progressive models are carried out in determining the relation between physical and chemical indexes of the grapes and the resulting wine. Multiple linear regression model is used to get the function relationship between them and the related weight. According to the weight, the less relevant indexes of grape are eliminated. Single layer forward neural network is established between the rest physical and chemical indexes of the grapes and wine to make a more accurate solution of the weight. The correlated factors of each index in grape wine are obtained by analyzing the related weight. The anthocyanin, DPPH radical and soluble solid in grapes has a considerable effect on the quality of red wine.
- © 2013, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Wei Fan AU - Zhi Pan PY - 2013/08 DA - 2013/08 TI - Application of Neural Network in Wine Grape Quality Evaluation BT - Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013) PB - Atlantis Press SP - 662 EP - 664 SN - 1951-6851 UR - https://doi.org/10.2991/icacsei.2013.158 DO - 10.2991/icacsei.2013.158 ID - Fan2013/08 ER -