Characterisation of tea leaves according to their total mineral content by means of probabilistic neural networks

Food Chemistry

Volumn 123, Issue 3, 2010, Pages 859-864

  McKenzie, James S ^a   Jurado, José Marcos ^a   de Pablos, Fernando ^a  

^a UNIVERSITY OF SEVILLE   (Spain)

Author keywords

Inductively coupled plasma atomic emission spectrometry; Linear discriminant analysis; Pattern recognition; Probabilistic neural networks; Tea

Indexed keywords

ALUMINUM; BARIUM; CALCIUM; COPPER; IRON; MAGNESIUM; MANGANESE; MINERAL; NICKEL; PHOSPHORUS; POTASSIUM; SODIUM; STRONTIUM; SULFUR; ZINC;

ARTICLE; ARTIFICIAL NEURAL NETWORK; ATOMIC EMISSION SPECTROMETRY; CHEMICAL ANALYSIS; CONCENTRATION (PARAMETERS); CONTROLLED STUDY; DISCRIMINANT ANALYSIS; MICROWAVE RADIATION; NONPARAMETRIC TEST; PATTERN RECOGNITION; PLANT LEAF; TEA; VARIETAS;

EID: 77954624523     PISSN: 03088146     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.foodchem.2010.05.007     Document Type: Article

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