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Orange juice classification with a biologically based neural network.

H P Dettmar·G S Barbour·K T Blackwell·T P Vogl·D L Alkon·F S Fry·J E Totah·T L Chambers
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Abstract

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).

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AlgorithmsBeveragesChromatography, High Pressure LiquidCitrusEvaluation Studies as TopicFlavonoidsFood ContaminationNeural Networks, ComputerTrace ElementsJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.

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H P Dettmar, G S Barbour, K T Blackwell, T P Vogl, D L Alkon, F S Fry, J E Totah, T L Chambers. 1996. Orange juice classification with a biologically based neural network.. https://doi.org/10.1016/0097-8485(95)00015-1

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