Pattern recognition and genetic algorithms for discrimination of orange juices and reduction of significant components from headspace solid-phase microextraction.
Identifieur interne : 000A27 ( Ncbi/Curation ); précédent : 000A26; suivant : 000A28Pattern recognition and genetic algorithms for discrimination of orange juices and reduction of significant components from headspace solid-phase microextraction.
Auteurs : Maurizio Rinaldi [Italie] ; Roberto Gindro ; Massimo Barbeni ; Gianna AllegroneSource :
- Phytochemical analysis : PCA [ 1099-1565 ]
English descriptors
- KwdEn :
- Algorithms, Beverages (analysis), Citrus (chemistry), Citrus (classification), Cluster Analysis, Computer Simulation, Gas Chromatography-Mass Spectrometry, Neural Networks (Computer), Plant Extracts (analysis), Plant Extracts (chemistry), Principal Component Analysis, Reproducibility of Results, Solid Phase Microextraction (methods), Species Specificity, Volatilization.
- MESH :
- chemical , analysis : Plant Extracts.
- analysis : Beverages.
- chemistry : Citrus, Plant Extracts.
- classification : Citrus.
- methods : Solid Phase Microextraction.
- Algorithms, Cluster Analysis, Computer Simulation, Gas Chromatography-Mass Spectrometry, Neural Networks (Computer), Principal Component Analysis, Reproducibility of Results, Species Specificity, Volatilization.
Abstract
Orange (Citrus sinensis L.) juice comprises a complex mixture of volatile components that are difficult to identify and quantify. Classification and discrimination of the varieties on the basis of the volatile composition could help to guarantee the quality of a juice and to detect possible adulteration of the product.
DOI: 10.1002/pca.1140
PubMed: 19609881
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pubmed:19609881Le document en format XML
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Classification and discrimination of the varieties on the basis of the volatile composition could help to guarantee the quality of a juice and to detect possible adulteration of the product.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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