Orange juice classification with a biologically based neural network
| Autor: | Hans P. Dettmar, Kim T. Blackwell, Fred S. Fry, Joseph E. Totah, Daniel L. Alkon, Thomas P. Vogl, T. L. Chambers, Garth S. Barbour |
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| Rok vydání: | 1996 |
| Předmět: |
Flavonoids
Orange juice Citrus Chromatography General Chemical Engineering Food Contamination Applied Microbiology and Biotechnology High-performance liquid chromatography Trace Elements Beverages chemistry.chemical_compound chemistry Evaluation Studies as Topic Neural Networks Computer Food science Flavanone Inductively coupled plasma mass spectrometry Algorithms Chromatography High Pressure Liquid Biotechnology |
| Zdroj: | Computers & Chemistry. 20:261-266 |
| ISSN: | 0097-8485 |
| DOI: | 10.1016/0097-8485(95)00015-1 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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