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Determination of Aromatic Components of Rosa davurica Pall. by Headspace Solid Phase Microextraction Combined with GC-MS 被引量:5
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作者 Yuan NIU Qiong XU +4 位作者 Jian ZHUANG Yude WANG Lilan DAI Dengfei LI Yalan ZHAO 《Medicinal Plant》 CAS 2018年第5期20-22,26,共4页
[Objectives] To determine the aromatic components of Rosa davurica Pall. [Methods] 42 kinds of aromatic components were identified from the flowers of R. davurica by headspace solid phase microextraction( HS-SPME) com... [Objectives] To determine the aromatic components of Rosa davurica Pall. [Methods] 42 kinds of aromatic components were identified from the flowers of R. davurica by headspace solid phase microextraction( HS-SPME) combined with gas chromatography-mass spectrometry( GC-MS). The main compounds were alcohols( 54. 88%) and aldehydes( 19. 55%). [Results] The top five components with the highest relative content were phenylethyl alcohol( 12. 69%),geraniol( 9. 85%),citronellol( 8. 80%),nerol( 7. 84%) and 2-n-pentylfuran( 7. 45%). [Conclusions] Headspace solid phase microextraction( HS-SPME) combined with gas chromatography-mass spectrometry( GC-MS) can provide basis for further development and utilization of R. davurica. 展开更多
关键词 Rosa davurica Pall. HEADSPACE solid phase microextraction (hs-spme) Gas chromatography-mass spectrometry (GC-MS) AROMATIC COMPONENTS
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酱油挥发性成分固相微萃取条件的优化 被引量:9
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作者 刘非 杜丽平 肖冬光 《食品与发酵工业》 CAS CSCD 北大核心 2017年第7期70-75,共6页
顶空固相微萃取(headspace solid-phase microextraction,HS-SPME)是提取挥发性成分比较常用的方法。为了确定提取酱油挥发性成分的SPME萃取头,分别以挥发性成分的累积峰面积标准化值和峰面积相对标准偏差的平均值与标准偏差为指标,考... 顶空固相微萃取(headspace solid-phase microextraction,HS-SPME)是提取挥发性成分比较常用的方法。为了确定提取酱油挥发性成分的SPME萃取头,分别以挥发性成分的累积峰面积标准化值和峰面积相对标准偏差的平均值与标准偏差为指标,考察了不同型号萃取头的萃取灵敏度和重复性,确定采用65μm PDMS/DVB提取酱油挥发性成分;并以各类挥发性成分峰面积和总峰面积为指标,优化了其萃取条件,确定萃取条件为:样品用量8 m L,萃取温度60℃,萃取时间60 min。利用优化方法对酱油挥发性成分进行提取,经气相色谱-质谱联用(gas chromatography-mass spectrometry,GC-MS)分析,共鉴定出86种挥发性成分,其中最主要的为酯类物质,其次为醇类、醛类和酸类。挥发性成分峰面积的平均相对标准偏差为8.14%,方法重复性较好。 展开更多
关键词 顶空固相微萃取(headspace solid—phase microextraction hs-spme) 酱油 挥发性成分 灵敏度 重复性 优化
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羊奶粉贮藏过程中挥发性成分的分析 被引量:3
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作者 雷飞艳 艾对 +2 位作者 于玲玲 云丹 张富新 《食品与发酵工业》 CAS CSCD 北大核心 2017年第2期196-200,共5页
采用顶空固相微萃取结合气质联用(headspace-solid phase microextraction coupled with gas chromatography-mass spectrometry,SPME-GC/MS)技术,对常温贮存下羊奶粉中挥发性成分进行提取与分析。在常温贮存1年的羊奶粉中,共检出41种... 采用顶空固相微萃取结合气质联用(headspace-solid phase microextraction coupled with gas chromatography-mass spectrometry,SPME-GC/MS)技术,对常温贮存下羊奶粉中挥发性成分进行提取与分析。在常温贮存1年的羊奶粉中,共检出41种挥发性成分。其中酸类成分最多,共13种,其次是酯类、烃类、醇类和醛酮类,分别为6、4、4和3种。羊奶粉的主体挥发性成分组成是丁酸、己酸、庚酸、辛酸、4-甲基辛酸、癸酸、月桂酸、邻苯二甲酸二甲酯、辛酸乙酯、十五烷、苯甲醛、甘菊环、己内酰胺,己酸、辛酸、4-甲基辛酸和癸酸在贮存6个月之后含量有明显增加趋势。在整个贮存期间,对羊奶粉膻味有较大影响的酸类物质有己酸、辛酸、4-甲基辛酸和癸酸,尤其是4-甲基辛酸,由于其较低阈值,成为最主要的羊膳物质,并且随着贮存时间的增加,酸类、酯类含量逐渐增加。 展开更多
关键词 羊奶粉 顶空固相微萃取(headspace-solid phase microextraction coupled with gas chromatography-mass spectrometry SPME-GC/MS) 气质联用 挥发性成分
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Selection of Yeast Strains Containing β-Glucosidase for Improving Wine Aroma 被引量:3
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作者 Chokchai Wanapu Patcharapom Sripunya Nantakom Boonkerd 《Journal of Agricultural Science and Technology(B)》 2012年第6期691-702,共12页
Aroma is an important component for characteristic of wine. β-glucosidase is an enzyme for enhancing wine aroma during fermentation and aging. The aim of this study was to evaluate β-glucosidase activities from 17 y... Aroma is an important component for characteristic of wine. β-glucosidase is an enzyme for enhancing wine aroma during fermentation and aging. The aim of this study was to evaluate β-glucosidase activities from 17 yeast strains. It was found that strain 71 B-1122 exhibited highest activity of β-glucosidase. The purified enzyme of this strain was characterized. The purified enzyme was added in Muscat must before wine making process. The must and wine were determined for volatile compounds by headspace-SPME/GC-MS and found that the hexyl acetate, linalool, phenethyl, nerol and geraniol compounds were significantly increased after treated with the enzyme. Sensory evaluation of the combined alcoholic fermentation and flavour enrichment of Muscat juice by adding of purified β-glucosidase enzyme showed well oenological properties especially aroma. Therefore, in producing wine, yeast strain selection and addition of β-glucosidase enzyme should be considered because they were important factors affecting wine aroma. 展开更多
关键词 Β-GLUCOSIDASE headspace-solid phase microextraction/gas chromatography-mass spectrophotometer Saccharomyces cerevisiae wine aroma.
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Volatile profile analysis and quality prediction of Longjing tea(Camellia sinensis) by HS-SPME/GC-MS 被引量:42
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作者 Jie LIN Yi DAI +2 位作者 Ya-nan GUO Hai-rong XU Xiao-chang WANG 《Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)》 SCIE CAS CSCD 2012年第12期972-980,共9页
This study aimed to analyze the volatile chemical profile of Longjing tea, and further develop a prediction model for aroma quality of Longjing tea based on potent odorants. A total of 21 Longjing samples were analyze... This study aimed to analyze the volatile chemical profile of Longjing tea, and further develop a prediction model for aroma quality of Longjing tea based on potent odorants. A total of 21 Longjing samples were analyzed by headspace solid phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS).Pearson's linear correlation analysis and partial least square (PLS) regression were applied to investigate the relationship between sensory aroma scores and the volatile compounds. Results showed that 60 volatile compound scould be commonly detected in this famous green tea. Terpenes and esters were two major groups characterized,representing 33.89% and 15.53% of the total peak area respectively. Ten compounds were determined to contribute significantly to the perceived aroma quality of Longjing tea, especially linalool (0.701), nonanal (0.738), (Z)-3-hexenyl hexanoate (-0.785), and β-ionone (-0.763). On the basis of these 10 compounds, a model (correlation coefficient of89.4% and cross-validated correlation coefficient of 80.4%) was constructed to predict the aroma quality of Longjingtea. Summarily, this study has provided a novel option for quality prediction of green tea based on HS-SPME/GC-MStechnique. 展开更多
关键词 Partial least square (PLS) regression Green tea Headspace solid phase microextraction (hs-spme Volatile profile Quality prediction
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