食品与发酵工业 >

2019 , Vol. 45 >Issue 2: 190 - 195

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.018206

分析与检测

基于电子鼻系统的白酒掺假检测方法

  • 马泽亮 ,
  • 国婷婷 ,
  • 殷廷家 ,
  • 王志强 ,
  • 杨方旭 ,
  • 李彩虹 ,
  • 李钊 ,
  • 袁文浩
展开
  • (山东理工大学 计算机科学与技术学院,山东 淄博,255049)
硕士研究生(王志强副教授为通讯作者,E-mail:wzq@sdut.edu.cn)。

收稿日期: 2018-07-05

  网络出版日期: 2019-02-21

基金资助

国家自然科学基金项目(61473179,61701286);山东省自然科学基金项目(ZR2018LF002,ZR2015FL003);赛尔网络下一代互联网技术创新项目(NGII20170314)

收起

Detection of liquor adulteration based on the electronic nose system

  • MA Zeliang ,
  • GUO Tingting ,
  • YIN Tingjia ,
  • WANG Zhiqiang ,
  • YANG Fangxu ,
  • LI Caihong ,
  • LI Zhao ,
  • YUAN Wenhao
Expand
  • (School of Computer Science and Technology, Shandong University of Technology,Zibo 255049, China)

Received date: 2018-07-05

  Online published: 2019-02-21

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摘要

采用自行研制的便携式电子鼻检测系统,建立了一种能够快速辨别白酒掺假的新方法。系统检测时:首先利用传感器阵列获得白酒“指纹数据”,随后通过离散小波变换(discrete wavelet transform,DWT)提取反馈信息里的特征信息,然后采用主成分分析(principal component analysis,PCA)实现对不同纯度掺假白酒样品的定性判别、采用人工蜂群优化最小二乘支持向量机(artificial bee colony least squared support vector machines,ABC-LSSVM)实现对不同纯度掺假白酒样品的定量预测。结果表明, PCA对掺假白酒区分效果较好,区分正确率高达100%;ABC-LSSVM预测模型对白酒纯度具有较高的定量预测性能,其验证集中相关系数R2为0.933 2,平均绝对误差MRE为6.564 3%,均方根误差RMSE为 0.023 4。该研究可为掺假白酒的定性辨别及定量预测提供技术支持。

关键词： 电子鼻; 白酒掺假; 离散小波变换; 人工蜂群优化最小二乘支持向量机

本文引用格式

马泽亮 , 国婷婷 , 殷廷家 , 王志强 , 杨方旭 , 李彩虹 , 李钊 , 袁文浩 . 基于电子鼻系统的白酒掺假检测方法[J]. 食品与发酵工业, 2019 , 45(2) : 190 -195 . DOI: 10.13995/j.cnki.11-1802/ts.018206

Abstract

A new method for quickly identifying liquor adulteration was established by using a self-developed portable electronic nose detection system. When the system was detecting, the “fingerprint data” of liquor was obtained by sensor array. Then, information reflected the features was extracted by discrete wavelet transform (DWT) from the feedback. The principal component analysis (PCA) was then used to determine the quality of adulterated liquor samples with different purity. The quantitative prediction of adulterated liquor samples with different purity was realized by artificial bee colony least squared-support vector machines (ABC-LSSVM). The results showed that PCA could well-distinguish adulterated liquor. Its accuracy could be as high as 100%. ABC-LSSVM prediction model had a good quantitative prediction performance for liquor purity. Its correlation coefficient (R2) was 0.933 2, the mean relative error (MRE) was 6.564 3%, and the root mean square error (RMSE) was 0.023 4. This study provides technical supports for qualitative discrimination and quantitative prediction of adulterated liquors.

Key words： electronic nose; liquor adulteration; discrete wavelet transform; artificial bee colony-least squares support vector machine

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