Orange juice classification with a biologically based neural network.
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).
Dettmar
H P
HP
Environmental Research Institute of Michigan, Arlington, VA 22209, USA.
Barbour
G S
GS
Blackwell
K T
KT
Vogl
T P
TP
Alkon
D L
DL
Fry
F S
FS
Totah
J E
JE
Chambers
T L
TL
D000465:Algorithms; D001628:Beverages / Q000009:adverse effects* / Q000032:analysis / Q000145:classification*; D002851:Chromatography, High Pressure Liquid; D002957:Citrus / Q000737:chemistry; D005069:Evaluation Studies as Topic; D005419:Flavonoids / Q000032:analysis; D005506:Food Contamination / Q000032:analysis*; D016571:Neural Networks, Computer; D014131:Trace Elements / Q000032:analysis
D005419:Flavonoids; D014131:Trace Elements