Application of Sensory Evaluation, HS-SPME GC-MS, E-Nose, and E-Tongue for Quality Detection in Citrus Fruits.
Identifieur interne : 000237 ( PubMed/Checkpoint ); précédent : 000236; suivant : 000238Application of Sensory Evaluation, HS-SPME GC-MS, E-Nose, and E-Tongue for Quality Detection in Citrus Fruits.
Auteurs : Shanshan Qiu [République populaire de Chine] ; Jun Wang [République populaire de Chine]Source :
- Journal of food science [ 1750-3841 ] ; 2015.
English descriptors
- KwdEn :
- Benzenesulfonates, Citrus (chemistry), Citrus sinensis (chemistry), Discriminant Analysis, Electrical Equipment and Supplies, Electronic Nose, Food Additives, Food Analysis (methods), Fruit (chemistry), Fruit and Vegetable Juices (analysis), Fruit and Vegetable Juices (standards), Gas Chromatography-Mass Spectrometry (methods), Humans, Least-Squares Analysis, Odors (analysis), Smell, Solid Phase Microextraction (methods), Taste, Tongue, Volatile Organic Compounds (analysis).
- MESH :
- chemical , analysis : Volatile Organic Compounds.
- chemical : Benzenesulfonates, Food Additives.
- analysis : Fruit and Vegetable Juices, Odors.
- chemistry : Citrus, Citrus sinensis, Fruit.
- methods : Food Analysis, Gas Chromatography-Mass Spectrometry, Solid Phase Microextraction.
- standards : Fruit and Vegetable Juices.
- Discriminant Analysis, Electrical Equipment and Supplies, Electronic Nose, Humans, Least-Squares Analysis, Smell, Taste, Tongue.
Abstract
In this study, electronic tongue (E-tongue), headspace solid-phase microextraction gas chromatography-mass spectrometer (GC-MS), electronic nose (E-nose), and quantitative describe analysis (QDA) were applied to describe the 2 types of citrus fruits (Satsuma mandarins [Citrus unshiu Marc.] and sweet oranges [Citrus sinensis {L.} Osbeck]) and their mixing juices systematically and comprehensively. As some aroma components or some flavor molecules interacted with the whole juice matrix, the changes of most components in the fruit juice were not in proportion to the mixing ratio of the 2 citrus fruits. The potential correlations among the signals of E-tongue and E-nose, volatile components, and sensory attributes were analyzed by using analysis of variance partial least squares regression. The result showed that the variables from the sensor signals (E-tongue system and E-nose system) had significant and positive (or negative) correlations to the most variables of volatile components (GC-MS) and sensory attributes (QDA). The simultaneous utilization of E-tongue and E-nose obtained a perfect classification result with 100% accuracy rate based on linear discriminant analysis and also attained a satisfying prediction with high coefficient association for the sensory attributes (R(2) > 0.994 for training sets and R(2) > 0.983 for testing sets) and for the volatile components (R(2) > 0.992 for training sets and R(2) > 0.990 for testing sets) based on random forest. Being easy-to-use, cost-effective, robust, and capable of providing a fast analysis procedure, E-nose and E-tongue could be used as an alternative detection system to traditional analysis methods, such as GC-MS and sensory evaluation by human panel in the fruit industry.
DOI: 10.1111/1750-3841.13012
PubMed: 26416698
Affiliations:
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Box 310058, Hangzhou, PR, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Dept. of Biosystems Engineering, Zhejiang Univ, 866 Yuhangtang Road, P.O. 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Box 310058, Hangzhou, PR, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Dept. of Biosystems Engineering, Zhejiang Univ, 866 Yuhangtang Road, P.O. Box 310058, Hangzhou, PR</wicri:regionArea><wicri:noRegion>PR</wicri:noRegion></affiliation></author><author><name sortKey="Wang, Jun" sort="Wang, Jun" uniqKey="Wang J" first="Jun" last="Wang">Jun Wang</name><affiliation wicri:level="1"><nlm:affiliation>Dept. of Biosystems Engineering, Zhejiang Univ, 866 Yuhangtang Road, P.O. Box 310058, Hangzhou, PR, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Dept. of Biosystems Engineering, Zhejiang Univ, 866 Yuhangtang Road, P.O. Box 310058, Hangzhou, PR</wicri:regionArea><wicri:noRegion>PR</wicri:noRegion></affiliation></author></analytic><series><title level="j">Journal of food science</title><idno type="eISSN">1750-3841</idno><imprint><date when="2015" type="published">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Benzenesulfonates</term><term>Citrus (chemistry)</term><term>Citrus sinensis (chemistry)</term><term>Discriminant Analysis</term><term>Electrical Equipment and Supplies</term><term>Electronic Nose</term><term>Food Additives</term><term>Food Analysis (methods)</term><term>Fruit (chemistry)</term><term>Fruit and Vegetable Juices (analysis)</term><term>Fruit and Vegetable Juices (standards)</term><term>Gas Chromatography-Mass Spectrometry (methods)</term><term>Humans</term><term>Least-Squares Analysis</term><term>Odors (analysis)</term><term>Smell</term><term>Solid Phase Microextraction (methods)</term><term>Taste</term><term>Tongue</term><term>Volatile Organic Compounds (analysis)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="analysis" xml:lang="en"><term>Volatile Organic Compounds</term></keywords><keywords scheme="MESH" type="chemical" xml:lang="en"><term>Benzenesulfonates</term><term>Food Additives</term></keywords><keywords scheme="MESH" qualifier="analysis" xml:lang="en"><term>Fruit and Vegetable Juices</term><term>Odors</term></keywords><keywords scheme="MESH" qualifier="chemistry" xml:lang="en"><term>Citrus</term><term>Citrus sinensis</term><term>Fruit</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Food Analysis</term><term>Gas Chromatography-Mass Spectrometry</term><term>Solid Phase Microextraction</term></keywords><keywords scheme="MESH" qualifier="standards" xml:lang="en"><term>Fruit and Vegetable Juices</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Discriminant Analysis</term><term>Electrical Equipment and Supplies</term><term>Electronic Nose</term><term>Humans</term><term>Least-Squares Analysis</term><term>Smell</term><term>Taste</term><term>Tongue</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this study, electronic tongue (E-tongue), headspace solid-phase microextraction gas chromatography-mass spectrometer (GC-MS), electronic nose (E-nose), and quantitative describe analysis (QDA) were applied to describe the 2 types of citrus fruits (Satsuma mandarins [Citrus unshiu Marc.] and sweet oranges [Citrus sinensis {L.} Osbeck]) and their mixing juices systematically and comprehensively. As some aroma components or some flavor molecules interacted with the whole juice matrix, the changes of most components in the fruit juice were not in proportion to the mixing ratio of the 2 citrus fruits. The potential correlations among the signals of E-tongue and E-nose, volatile components, and sensory attributes were analyzed by using analysis of variance partial least squares regression. The result showed that the variables from the sensor signals (E-tongue system and E-nose system) had significant and positive (or negative) correlations to the most variables of volatile components (GC-MS) and sensory attributes (QDA). The simultaneous utilization of E-tongue and E-nose obtained a perfect classification result with 100% accuracy rate based on linear discriminant analysis and also attained a satisfying prediction with high coefficient association for the sensory attributes (R(2) > 0.994 for training sets and R(2) > 0.983 for testing sets) and for the volatile components (R(2) > 0.992 for training sets and R(2) > 0.990 for testing sets) based on random forest. Being easy-to-use, cost-effective, robust, and capable of providing a fast analysis procedure, E-nose and E-tongue could be used as an alternative detection system to traditional analysis methods, such as GC-MS and sensory evaluation by human panel in the fruit industry.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">26416698</PMID><DateCreated><Year>2015</Year><Month>10</Month><Day>8</Day></DateCreated><DateCompleted><Year>2016</Year><Month>05</Month><Day>20</Day></DateCompleted><DateRevised><Year>2015</Year><Month>10</Month><Day>8</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1750-3841</ISSN><JournalIssue CitedMedium="Internet"><Volume>80</Volume><Issue>10</Issue><PubDate><Year>2015</Year><Month>Oct</Month></PubDate></JournalIssue><Title>Journal of food science</Title><ISOAbbreviation>J. Food Sci.</ISOAbbreviation></Journal><ArticleTitle>Application of Sensory Evaluation, HS-SPME GC-MS, E-Nose, and E-Tongue for Quality Detection in Citrus Fruits.</ArticleTitle><Pagination><MedlinePgn>S2296-304</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1111/1750-3841.13012</ELocationID><Abstract><AbstractText Label="UNLABELLED">In this study, electronic tongue (E-tongue), headspace solid-phase microextraction gas chromatography-mass spectrometer (GC-MS), electronic nose (E-nose), and quantitative describe analysis (QDA) were applied to describe the 2 types of citrus fruits (Satsuma mandarins [Citrus unshiu Marc.] and sweet oranges [Citrus sinensis {L.} Osbeck]) and their mixing juices systematically and comprehensively. As some aroma components or some flavor molecules interacted with the whole juice matrix, the changes of most components in the fruit juice were not in proportion to the mixing ratio of the 2 citrus fruits. The potential correlations among the signals of E-tongue and E-nose, volatile components, and sensory attributes were analyzed by using analysis of variance partial least squares regression. The result showed that the variables from the sensor signals (E-tongue system and E-nose system) had significant and positive (or negative) correlations to the most variables of volatile components (GC-MS) and sensory attributes (QDA). The simultaneous utilization of E-tongue and E-nose obtained a perfect classification result with 100% accuracy rate based on linear discriminant analysis and also attained a satisfying prediction with high coefficient association for the sensory attributes (R(2) > 0.994 for training sets and R(2) > 0.983 for testing sets) and for the volatile components (R(2) > 0.992 for training sets and R(2) > 0.990 for testing sets) based on random forest. Being easy-to-use, cost-effective, robust, and capable of providing a fast analysis procedure, E-nose and E-tongue could be used as an alternative detection system to traditional analysis methods, such as GC-MS and sensory evaluation by human panel in the fruit industry.</AbstractText><AbstractText Label="PRACTICAL APPLICATION" NlmCategory="CONCLUSIONS">Being easy-to-use, cost-effective, robust, and capable of providing a fast analysis procedure, E-nose and E-tongue could be used as an alternative detection system to traditional analysis methods for characterizing food flavors. Based on those results, one can draw a conclusion that the fusion system composed of E-tongue and E-nose could guarantee a satisfying result in the prediction of sensory attributes and volatile components for fruit quality profile.</AbstractText><CopyrightInformation>© 2015 Institute of Food Technologists®</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Qiu</LastName><ForeName>Shanshan</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Dept. of Biosystems Engineering, Zhejiang Univ, 866 Yuhangtang Road, P.O. Box 310058, Hangzhou, PR, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Jun</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Dept. of Biosystems Engineering, Zhejiang Univ, 866 Yuhangtang Road, P.O. Box 310058, Hangzhou, PR, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>ENG</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2015</Year><Month>Sep</Month><Day>28</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>J Food Sci</MedlineTA><NlmUniqueID>0014052</NlmUniqueID><ISSNLinking>0022-1147</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D001557">Benzenesulfonates</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D005503">Food Additives</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D055549">Volatile Organic Compounds</NameOfSubstance></Chemical><Chemical><RegistryNumber>1208-67-9</RegistryNumber><NameOfSubstance UI="C056082">4-sulfophenylmethallyl ether</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D001557" MajorTopicYN="N">Benzenesulfonates</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002957" MajorTopicYN="N">Citrus</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="Y">chemistry</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D032084" MajorTopicYN="N">Citrus sinensis</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="N">chemistry</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016002" MajorTopicYN="N">Discriminant Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D055615" MajorTopicYN="Y">Electrical Equipment and Supplies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D062609" MajorTopicYN="N">Electronic 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MajorTopicYN="N">analysis</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">citrus juice</Keyword><Keyword MajorTopicYN="N">electronic nose</Keyword><Keyword MajorTopicYN="N">electronic tongue</Keyword><Keyword MajorTopicYN="N">quantitative describe analysis</Keyword><Keyword MajorTopicYN="N">random forest</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2015</Year><Month>5</Month><Day>22</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2015</Year><Month>7</Month><Day>27</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2015</Year><Month>9</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2015</Year><Month>9</Month><Day>30</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2016</Year><Month>5</Month><Day>21</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">26416698</ArticleId><ArticleId IdType="doi">10.1111/1750-3841.13012</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>République populaire de Chine</li></country></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Qiu, Shanshan" sort="Qiu, Shanshan" uniqKey="Qiu S" first="Shanshan" last="Qiu">Shanshan Qiu</name></noRegion><name sortKey="Wang, Jun" sort="Wang, Jun" uniqKey="Wang J" first="Jun" last="Wang">Jun Wang</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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