The present study analyzed the volatile compounds of three mango varieties (Tommy Atkins, Rosa and Espada) using the static headspace technique with SPME coupled to CG-MS. Multivariate methodologies, such as factorial...The present study analyzed the volatile compounds of three mango varieties (Tommy Atkins, Rosa and Espada) using the static headspace technique with SPME coupled to CG-MS. Multivariate methodologies, such as factorial design and response surface methodology, were used to optimize the conditions of adsorption and desorption of these substances. The data were evaluated by using principal components analysis (PCA) and hierarchical grouping analysis, in order to visualize grouping tendencies of volatile compounds. Thirty-seven volatile compounds belonging to different chemical classes, such as esters, terpenes, alcohols and others, were tentatively identified in the three varieties of mango. Amongst them, twenty-three presented chromatographic peaks with relative areas larger than 2%. The multivariate analysis made it possible to visualize the grouping tendencies of the mango samples, according to the presence of their respective volatile substances, and enabled the identification of the groups of substances responsible for the discrimination among the three varieties.展开更多
文摘The present study analyzed the volatile compounds of three mango varieties (Tommy Atkins, Rosa and Espada) using the static headspace technique with SPME coupled to CG-MS. Multivariate methodologies, such as factorial design and response surface methodology, were used to optimize the conditions of adsorption and desorption of these substances. The data were evaluated by using principal components analysis (PCA) and hierarchical grouping analysis, in order to visualize grouping tendencies of volatile compounds. Thirty-seven volatile compounds belonging to different chemical classes, such as esters, terpenes, alcohols and others, were tentatively identified in the three varieties of mango. Amongst them, twenty-three presented chromatographic peaks with relative areas larger than 2%. The multivariate analysis made it possible to visualize the grouping tendencies of the mango samples, according to the presence of their respective volatile substances, and enabled the identification of the groups of substances responsible for the discrimination among the three varieties.
