Orange juice classification with a biologically based neural network

被引:17
作者
Dettmar, HP
Barbour, GS
Blackwell, KT
Vogl, TP
Alkon, DL
Fry, FS
Totah, JE
Chambers, TL
机构
[1] ENVIRONM RES INST MICHIGAN,ARLINGTON,VA 22209
[2] NINCDS,BETHESDA,MD 20892
[3] US FDA,CTR FOOD SAFETY & APPL NUTR,WASHINGTON,DC 20204
来源
COMPUTERS & CHEMISTRY | 1996年 / 20卷 / 02期
关键词
D O I
10.1016/0097-8485(95)00015-1
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Dystal, an artificial neural network, was used to classify orange juice products. Nine varieties of oranges collected from six geographical regions were processed into single-strength, reconstituted or frozen concentrated orange juice. The data set represented 240 authentic and 173 adulterated samples of juices; 16 variables [8 flavone and flavanone glycoside concentrations measured by high-performance liquid chromatography (HPLC) and 8 trace element concentrations measured by inductively coupled plasma spectroscopy] were selected to characterize each juice and were used as input to Dystal. Dystal correctly classified 89.8% of the juices as authentic or adulterated. Classification performance increased monotonically as the percentage of pulpwash in the sample increased. Dystal correctly identified 92.5% of the juices by variety (Valencia vs non-Valencia).
引用
收藏
页码:261 / 266
页数:6
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