Quantitative analysis of cefalexin based on artificial neural networks combined with modified genetic algorithm using short near-infrared spectroscopy

被引：8
作者：
Huan, Yanfu [1 ]
Feng, Guodong [1 ]
Wang, Bin [1 ]
Ren, Yulin [1 ]
Fei, Qiang [1 ]
机构：
[1] Jilin Univ, Coll Chem, Changchun 130021, Peoples R China
来源：
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY | 2013年 / 109卷
关键词：
Artificial neural networks; Genetic algorithm; Short near-infrared spectroscopy; Wavelength selection; Cefalexin; LEAST-SQUARES REGRESSION; MUTUAL INFORMATION; VARIABLE SELECTION; TRIMETHOPRIM; INHIBITORS; QSAR;
D O I：
10.1016/j.saa.2013.02.047
中图分类号：
O433 [光谱学];
学科分类号：
0703 ; 070302 ;
摘要：
In this paper, a novel chemometric method was developed for rapid, accurate, and quantitative analysis of cefalexin in samples. The experiments were carried out by using the short near-infrared spectroscopy coupled with artificial neural networks. In order to enhancing the predictive ability of artificial neural networks model, a modified genetic algorithm was used to select fixed number of wavelength. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码：308 / 312
页数：5
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