Pattern recognition and genetic algorithms for discrimination of orange juices and reduction of significant components from headspace solid-phase microextraction.
Identifieur interne : 000A03 ( PubMed/Corpus ); précédent : 000A02; suivant : 000A04Pattern recognition and genetic algorithms for discrimination of orange juices and reduction of significant components from headspace solid-phase microextraction.
Auteurs : Maurizio Rinaldi ; 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
Links to Exploration step
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><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">19609881</PMID><DateCreated><Year>2009</Year><Month>8</Month><Day>31</Day></DateCreated><DateCompleted><Year>2010</Year><Month>03</Month><Day>02</Day></DateCompleted><DateRevised><Year>2009</Year><Month>8</Month><Day>31</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1099-1565</ISSN><JournalIssue CitedMedium="Internet"><Volume>20</Volume><Issue>5</Issue><PubDate><MedlineDate>2009 Sep-Oct</MedlineDate></PubDate></JournalIssue><Title>Phytochemical analysis : PCA</Title><ISOAbbreviation>Phytochem Anal</ISOAbbreviation></Journal><ArticleTitle>Pattern recognition and genetic algorithms for discrimination of orange juices and reduction of significant components from headspace solid-phase microextraction.</ArticleTitle><Pagination><MedlinePgn>402-7</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1002/pca.1140</ELocationID><Abstract><AbstractText Label="INTRODUCTION" NlmCategory="BACKGROUND">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.</AbstractText><AbstractText Label="OBJECTIVE" NlmCategory="OBJECTIVE">To provide information on the amounts of volatile constituents in fresh-squeezed juices from four orange cultivars and to establish suitable discrimination rules to differentiate orange juices using new chemometric approaches.</AbstractText><AbstractText Label="METHODOLOGY" NlmCategory="METHODS">Fresh juices of four orange cultivars were analysed by headspace solid-phase microextraction (HS-SPME) coupled with GC-MS. Principal component analysis, linear discriminant analysis and heuristic methods, such as neural networks, allowed clustering of the data from HS-SPME analysis while genetic algorithms addressed the problem of data reduction. To check the quality of the results the chemometric techniques were also evaluated on a sample.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Thirty volatile compounds were identified by HS-SPME and GC-MS analyses and their relative amounts calculated. Differences in composition of orange juice volatile components were observed. The chosen orange cultivars could be discriminated using neural networks, genetic relocation algorithms and linear discriminant analysis. Genetic algorithms applied to the data were also able to detect the most significant compounds.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">SPME is a useful technique to investigate orange juice volatile composition and a flexible chemometric approach is able to correctly separate the juices.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Rinaldi</LastName><ForeName>Maurizio</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Università degli Studi del Piemonte Orientale, Dipartimento di Scienze Chimiche, Alimentari, Farmaceutiche e Farmacologiche, Novara, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gindro</LastName><ForeName>Roberto</ForeName><Initials>R</Initials></Author><Author ValidYN="Y"><LastName>Barbeni</LastName><ForeName>Massimo</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Allegrone</LastName><ForeName>Gianna</ForeName><Initials>G</Initials></Author></AuthorList><Language>ENG</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Phytochem Anal</MedlineTA><NlmUniqueID>9200492</NlmUniqueID><ISSNLinking>0958-0344</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D010936">Plant Extracts</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="Y">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001628" MajorTopicYN="N">Beverages</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="Y">analysis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D002957" MajorTopicYN="N">Citrus</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="Y">chemistry</QualifierName><QualifierName UI="Q000145" MajorTopicYN="N">classification</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016000" MajorTopicYN="N">Cluster Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003198" MajorTopicYN="N">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008401" MajorTopicYN="N">Gas Chromatography-Mass Spectrometry</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016571" MajorTopicYN="N">Neural Networks (Computer)</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010936" MajorTopicYN="N">Plant Extracts</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="N">analysis</QualifierName><QualifierName UI="Q000737" MajorTopicYN="N">chemistry</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D025341" MajorTopicYN="N">Principal Component Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015203" MajorTopicYN="N">Reproducibility of Results</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D052617" MajorTopicYN="N">Solid Phase Microextraction</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D013045" MajorTopicYN="N">Species Specificity</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014835" MajorTopicYN="N">Volatilization</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2009</Year><Month>7</Month><Day>18</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2009</Year><Month>7</Month><Day>18</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2010</Year><Month>3</Month><Day>3</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">19609881</ArticleId><ArticleId IdType="doi">10.1002/pca.1140</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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