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HS-SPME/GC-MS Analysis of VOC and Multivariate Techniques Applied to the Discrimination of Brazilian Varieties of Mango

机译:挥发性有机化合物的HS-SPME / GC-MS分析和多元技术在巴西芒果品种鉴别中的应用

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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.
机译:本研究使用静态顶空技术结合SPME和CG-MS分析了三个芒果品种(Tommy Atkins,Rosa和Espada)的挥发性化合物。多元方法,例如因子设计和响应面方法,被用来优化这些物质的吸附和解吸条件。通过使用主成分分析(PCA)和分层分组分析对数据进行评估,以便可视化挥发性化合物的分组趋势。在三种芒果中,初步鉴定出37种属于不同化学类别的挥发性化合物,例如酯,萜烯,醇等。其中有23个色谱峰,相对面积大于2%。通过多变量分析,可以根据芒果样品各自的挥发性物质的存在来可视化它们的分组趋势,并可以识别出负责区分这三个品种的物质组。

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