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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Bibliographic Details
Published in:Food analytical methods 2016-12, Vol.9 (12), p.3492-3500
Main Authors: Pello-Palma, Jairo, Mangas-Alonso, Juan José, de la Fuente, Enrique Dapena, González-Álvarez, Jaime, Díez, Jorge, Álvarez, María Dolores Gutiérrez, Abrodo, Pilar Arias
Format: Article
Language:English
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
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Summary: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.
ISSN:1936-9751
1936-976X
DOI:10.1007/s12161-016-0521-7