Affiliations: [a] School of Digital Technology and Engineering, Ningbo University of Finance and Economics, Ningbo, China
| [b] School of Business, Zhejiang Wanli University, Ningbo, China
| [c] School of Management Science and Engineering, Nanjing University of Finance and Economics, Nanjing, Jiangsu, China
| [d] Ningbo World Information Technology Development Co., Ltd, Zhejiang University City College, Ningbo, China
| [e] Ningbo Qihe Gabriel Agricultural Technology Co., Ltd., Ningbo, China
Correspondence:
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Corresponding author. Leina Zheng, School of Business, Zhejiang Wanli University, No. 8, South Qian Hu Road, Ningbo, China. E-mail: [email protected].
Abstract: At present, the identification of wine is artificial, and it has certain subjectivity. An alternative way that predicting brand based on fusion spectrum combined with Compressive Sampling as a dimensionality reduction technique is proposed in order to achieve the fast, scientific quality evaluation for wine. Successful results have been obtained in the wine identification use Artificial Neural Networks (ANNs) which has the faster speed and precision than typical algorithm. In the end, a multi-spectral information fusion analysis method for wine identification is proposed, which is an efficient solution to the problem of counterfeiting wine.
