文献类型： 外文期刊

作者： Guo Hai-xia ¹ ; Wang Tao ¹ ; Liu Yang ¹ ; Wu Hai-yun ¹ ; Zuo Yue-ming ¹ ; Song Hai-yan ¹ ; He Jin-yu ² ;

作者机构： 1.Shanxi Agr Univ, Coll Engn, Taigu 030801, Peoples R China

2.Shanxi Acad Agr Sci, Pomol Inst, Taigu 030815, Peoples R China

关键词： Red wine;Visible-near infrared spectroscopy;BP neural network;Ture or false;Recognition

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2011 年 31 卷 12 期

页码：

收录情况： SCI

摘要： This study selected 90 samples from different brands of red wine. In order to eliminate the impact of spectral curve's baseline, the first derivatives of all of spectral curves were calculated and the principal component analysis was carried out on the first derivative spectra. The result showed that the contribution rate of the first two principal components was over 80 percent. By the first two principal components, all the red wine samples were obviously divided into two classes. Furthermore a 3-layer artificial neural network predictive model was built with the first four principal components as input variables and 100 percent correct prediction rate was gained. The research showed that the visible-near infrared spectroscopy combined with principal component analysis provides an accurate and reliable new method to rapidly and nondestructively recognize the true or false red wines.