Title: Age identification of Chinese rice wine using electronic nose

Authors: Wei Ding; Peiyi Zhu; Ya Gu

Addresses: School of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu 215500, Jiangsu, China; Institute for Intelligent Systems, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, Gauteng, South Africa ' School of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu 215500, China ' School of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu 215500, Jiangsu, China

Abstract: This paper is concerned with the identification of the age of Chinese rice wine. To address this problem, a new electronic nose system with the multivariate analysis method based on the artificial olfactory technique is developed. First, four features are extracted to represent the dynamic behaviour of the signal that is generated from the array of the Taguchi Gas Sensor (TGS) deployed in the volatile substance of the rice wine. Then, the Principal Component Analysis (PCA), the Linear Discriminant Analysis (LDA) and the error Back Propagation Neural Network (BPANN) are combined to build a model for the identification of the age of Chinese rice wine. The results show that the LDA model fails to distinguish the Chinese wine with a one-year age difference in the proposed electronic nose system, whose accuracy of training and prediction are 98.44% and 96.88%, respectively. By contrast, the optimised BPANN model is capable of identifying the age of the Chinese wine and achieves the accuracy of 100% in the training and the prediction sets. It is verified that the self-designed electronic nose with the optimised BPANN is valuable on the application of the age prediction of Chinese rice wine.

Keywords: age identification; Chinese rice wine; electronic nose system; multivariate analysis.

DOI: 10.1504/IJCAT.2020.109345

International Journal of Computer Applications in Technology, 2020 Vol.63 No.3, pp.185 - 190

Received: 27 Jan 2020
Accepted: 29 Mar 2020
Published online: 03 Sep 2020 *

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