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Characterization of Volatile Compounds in New Cider Apple Genotypes Using Multivariate Analysis
Gas chromatography combined with solid-phase microextraction has been used for the identification of the aromatic profiles of new cider apple genotypes, and a chemometric characterization of these new cider apple genotypes has been carried out using exploratory and modelling techniques. Three breedi...
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| Published in: | Food analytical methods 2016-12, Vol.9 (12), p.3492-3500 |
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| Main Authors: | , , , , , , |
| Format: | Article |
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| cites | cdi_FETCH-LOGICAL-c359t-6ed8042dd54b6a195473c2c9cedfa0d90739b2102a670f7c9a642504c6ba0a8a3 |
| container_end_page | 3500 |
| container_issue | 12 |
| container_start_page | 3492 |
| container_title | Food analytical methods |
| container_volume | 9 |
| creator | Pello-Palma, Jairo Mangas-Alonso, Juan José de la Fuente, Enrique Dapena González-Álvarez, Jaime Díez, Jorge Álvarez, María Dolores Gutiérrez Abrodo, Pilar Arias |
| description | Gas chromatography combined with solid-phase microextraction has been used for the identification of the aromatic profiles of new cider apple genotypes, and a chemometric characterization of these new cider apple genotypes has been carried out using exploratory and modelling techniques. Three breeding targets have been explored: (1) regular bearing and scab resistance, (2) resistance to bio-aggressors and (3) high polyphenol content and late ripening. Exploratory techniques established two genotype groups: those that come from breeding towards targets 1 and 2 with low polyphenol contents and those that come from breeding towards target 3 with high polyphenol contents. Alcohols were related to the genotypes with breeding towards target 3, and compounds such as esters were related to the genotypes with breeding towards targets 1 and 2. Models computed using the soft independent modelling of class analogy (SIMCA) technique presented good sensitivity (93 %), specificity (91 %) and classification hits (96 %). However, the predictions computed by SIMCA (70 %) and the artificial neural network (ANN) (76 %) were low. |
| doi_str_mv | 10.1007/s12161-016-0521-7 |
| format | article |
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Models computed using the soft independent modelling of class analogy (SIMCA) technique presented good sensitivity (93 %), specificity (91 %) and classification hits (96 %). However, the predictions computed by SIMCA (70 %) and the artificial neural network (ANN) (76 %) were low.</description><subject>Alcohols</subject><subject>Analytical Chemistry</subject><subject>Artificial neural networks</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Chemistry/Food Science</subject><subject>Cider</subject><subject>Computation</subject><subject>Esters</subject><subject>Food safety</subject><subject>Food Science</subject><subject>Gas chromatography</subject><subject>Microbiology</subject><subject>Modelling</subject><subject>Multivariate analysis</subject><subject>Ripening</subject><subject>Solid phases</subject><subject>Volatile compounds</subject><issn>1936-9751</issn><issn>1936-976X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp1kE9LxDAUxIMouK5-AG8Bz9X30jZpjkvxH6x6ccVbyKbpmqXb1KSrrJ_eLhXx4ukNvJlh-BFyjnCJAOIqIkOOCSBPIGeYiAMyQZnyRAr-evirczwmJzGuAThkyCZElW86aNPb4L5073xLfU1ffDPoxtLSbzq_batIXUsf7SctXWUDnXXd8Ly1re93nY10EV27og_bpncfOjjdWzprdbOLLp6So1o30Z793ClZ3Fw_l3fJ_On2vpzNE5Pmsk-4rQrIWFXl2ZJrlHkmUsOMNLaqNVQSRCqXDIFpLqAWRmqesRwyw5cadKHTKbkYe7vg37c29mrtt2EYERUWBRR5CgUMLhxdJvgYg61VF9xGh51CUHuOauSoBo5qz1GJIcPGTBy87cqGP83_hr4BKzF1_Q</recordid><startdate>20161201</startdate><enddate>20161201</enddate><creator>Pello-Palma, Jairo</creator><creator>Mangas-Alonso, Juan José</creator><creator>de la Fuente, Enrique Dapena</creator><creator>González-Álvarez, Jaime</creator><creator>Díez, Jorge</creator><creator>Álvarez, María Dolores Gutiérrez</creator><creator>Abrodo, Pilar Arias</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20161201</creationdate><title>Characterization of Volatile Compounds in New Cider Apple Genotypes Using Multivariate Analysis</title><author>Pello-Palma, Jairo ; Mangas-Alonso, Juan José ; de la Fuente, Enrique Dapena ; González-Álvarez, Jaime ; Díez, Jorge ; Álvarez, María Dolores Gutiérrez ; Abrodo, Pilar Arias</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-6ed8042dd54b6a195473c2c9cedfa0d90739b2102a670f7c9a642504c6ba0a8a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Alcohols</topic><topic>Analytical Chemistry</topic><topic>Artificial neural networks</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>Chemistry/Food Science</topic><topic>Cider</topic><topic>Computation</topic><topic>Esters</topic><topic>Food safety</topic><topic>Food Science</topic><topic>Gas chromatography</topic><topic>Microbiology</topic><topic>Modelling</topic><topic>Multivariate analysis</topic><topic>Ripening</topic><topic>Solid phases</topic><topic>Volatile compounds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pello-Palma, Jairo</creatorcontrib><creatorcontrib>Mangas-Alonso, Juan José</creatorcontrib><creatorcontrib>de la Fuente, Enrique Dapena</creatorcontrib><creatorcontrib>González-Álvarez, Jaime</creatorcontrib><creatorcontrib>Díez, Jorge</creatorcontrib><creatorcontrib>Álvarez, María Dolores Gutiérrez</creatorcontrib><creatorcontrib>Abrodo, Pilar Arias</creatorcontrib><collection>CrossRef</collection><jtitle>Food analytical methods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pello-Palma, Jairo</au><au>Mangas-Alonso, Juan José</au><au>de la Fuente, Enrique Dapena</au><au>González-Álvarez, Jaime</au><au>Díez, Jorge</au><au>Álvarez, María Dolores Gutiérrez</au><au>Abrodo, Pilar Arias</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Characterization of Volatile Compounds in New Cider Apple Genotypes Using Multivariate Analysis</atitle><jtitle>Food analytical methods</jtitle><stitle>Food Anal. 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| subjects | Alcohols Analytical Chemistry Artificial neural networks Chemistry Chemistry and Materials Science Chemistry/Food Science Cider Computation Esters Food safety Food Science Gas chromatography Microbiology Modelling Multivariate analysis Ripening Solid phases Volatile compounds |
| title | Characterization of Volatile Compounds in New Cider Apple Genotypes Using Multivariate Analysis |
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