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
油料作物由于含油量高、基质复杂,导致其弱极性多环芳烃类化合物提取率低,成为准确检测高油样品中多环芳烃的瓶颈。本文对比了16种多环芳烃的GC-MS/MS检测条件SIM(Single Ion Monitoring)模式和SRM(Selective Reaction Monitoring)模式...油料作物由于含油量高、基质复杂,导致其弱极性多环芳烃类化合物提取率低,成为准确检测高油样品中多环芳烃的瓶颈。本文对比了16种多环芳烃的GC-MS/MS检测条件SIM(Single Ion Monitoring)模式和SRM(Selective Reaction Monitoring)模式质谱信号响应,SRM模式干扰峰更少,检出限更低;对比了QuEChERS和超声辅助提取方法对大豆、油菜籽、花生三种油料中16种多环芳烃的提取效果,超声辅助提取的基质效应很高,部分多环芳烃基质减弱80%以上,且油菜籽的提取稳定性差,部分相对标准偏差达到32%~45%。并比较了乙腈和丙酮作为QuCEhERS方法提取溶剂的提取效果。结果表明,QuCEhERS方法中乙腈作为提取溶剂,在极性最弱的多环芳烃回收率低,如苯并[b]荧蒽、苯并[k]荧蒽等,回收率甚至小于10%。而丙酮作为QuCEhERS方法提取溶剂,而在极性弱的多环芳烃中,回收率提高了3~5倍,适合提取高油样品中多环芳烃。三种油料基质匹配标准曲线的相关系数均在0.99以上。16种多环芳烃均能获得较好的回收率(58%~100%),相对标准偏差为0.4%~10.6%,方法稳定性好。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.
文摘油料作物由于含油量高、基质复杂,导致其弱极性多环芳烃类化合物提取率低,成为准确检测高油样品中多环芳烃的瓶颈。本文对比了16种多环芳烃的GC-MS/MS检测条件SIM(Single Ion Monitoring)模式和SRM(Selective Reaction Monitoring)模式质谱信号响应,SRM模式干扰峰更少,检出限更低;对比了QuEChERS和超声辅助提取方法对大豆、油菜籽、花生三种油料中16种多环芳烃的提取效果,超声辅助提取的基质效应很高,部分多环芳烃基质减弱80%以上,且油菜籽的提取稳定性差,部分相对标准偏差达到32%~45%。并比较了乙腈和丙酮作为QuCEhERS方法提取溶剂的提取效果。结果表明,QuCEhERS方法中乙腈作为提取溶剂,在极性最弱的多环芳烃回收率低,如苯并[b]荧蒽、苯并[k]荧蒽等,回收率甚至小于10%。而丙酮作为QuCEhERS方法提取溶剂,而在极性弱的多环芳烃中,回收率提高了3~5倍,适合提取高油样品中多环芳烃。三种油料基质匹配标准曲线的相关系数均在0.99以上。16种多环芳烃均能获得较好的回收率(58%~100%),相对标准偏差为0.4%~10.6%,方法稳定性好。
