Classification of spirits by headspace gaschromatography and artificial neural networks

被引：0
|
作者：
Kursawe, P [1 ]
Zinn, P [1 ]
机构：
[1] Ruhr Univ Bochum, Lehrstuhl Analyt Chem, D-44780 Bochum, Germany
来源：
DEUTSCHE LEBENSMITTEL-RUNDSCHAU | 1999年 / 95卷 / 11期
关键词：
D O I：
暂无
中图分类号：
TS2 [食品工业];
学科分类号：
0832 ;
摘要：
Three types of artificial neural networks (backprop, RBF-DDA and DLVQ) were tested:for their applicability as classifiers using spirits as a test case. 68 samples of six classes were analysed by headspace GC. Well resolved chromatograms allowed 75% of correct classifications by backprop and RBF-DDA (validated using the leave-one-out method). DLVQ. gave the correct result for just 65% of the samples. For worse resolved chromatograms those values we re 57, 56 and 60%. RBF-DDA-networks proved to be especially well suited to the development of automatic classifier systems. These systems perform as well as backprop nets which are more difficult to optimise and which learn much slower.
引用
收藏
页码：453 / 457
页数：5
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