兼香型年份酒中香味物质的分析

通过单因素试验优化顶空固相微萃取（HS-SPME）条件，并结合气相色谱-质谱（GC-MS）技术研究兼香型年份酒中香味物质成分含量及差异。结果表明，最佳HS-SPME萃取条件为酒样酒精度14%vol，萃取温度40 ℃，萃取时间30 min，解吸时间5 min。GC-MS法共鉴定出67种挥发性风味物质，包括50种酯类，3种醇类，5种酸类，1种芳香族化合物，1种含硫化合物，2种呋喃类，3种酮类和2种醛类。酯类、醇类、酸类和呋喃类总含量随贮藏年份增长呈现上升趋势，醛类和酮类总含量变化规律不明显。经香气活度值（OAV）分析，确定出22种（OAV＞1）成分对白酒香味有贡献，其中有9种（OAV＞10）为重要香气成分，酯类为白酒香气的主要贡献物质。     

腊八豆挥发性风味物质的研究

采用同时蒸馏萃取(SDE)和顶空固相微萃取(HS-SPME)两种方法提取4种不同品牌腊八豆中的挥发性风味物质,并用气相色谱-质谱(GC-MS)联用对挥发性活性成分进行鉴定。结果显示:共鉴定出115种挥发性物质,包括醇类12种,醛类15种,酮类6种,醚类7种,酚类5种,酸类4种,酯类17种,烷烃类40种,含硫类4种和杂环类5种,其中SDE法和HS-SPME法分别鉴定出98种和73种。4种腊八豆样品中,JC样品、LMZ样品、TYGS样品、TDJ样品分别鉴定出73种、66种、77种和77种挥发性物质。其中主要的挥发性风味物质包括1-辛烯-3-醇、芳樟醇、2-茨醇、α-松油醇、苯乙醛、(E,E)-2,4-庚二烯醛、橙花醛、甲基庚烯酮、烯丙基甲基二硫醚、乙基麦芽酚、丁香酚、棕榈酸乙酯、蒎烯、莰烯、桧烯和α-姜黄烯等。同时发现两种提取方法检测出的结果差异很大,4种腊八豆样品的挥发性风味物质也有一定的差异。     

骏枣精油的超临界CO2萃取工艺及其组成分析

以新疆骏枣为原料,采用超临界二氧化碳萃取技术对骏枣精油进行萃取分离,通过单因素和正交试验优化骏枣精油的萃取工艺,最后采用顶空-固相微萃取(headspace solid phase microextraction,HS-SPME)和气相色谱-质谱联用仪(gas chromatography-mass spectrometry,GC-MS)对骏枣精油成分进行分析。结果表明:超临界二氧化碳萃取骏枣精油最佳工艺为:乙醇作携带剂,萃取压力25 MPa,萃取温度32℃,夹带剂流速0.3 mL/min,萃取时间3 h,骏枣精油的萃取率可达1.224%。经GC-MS分析,共检出68种物质,结构推断59种,占总峰面积的91.737%,其中主要包括酸类、烷烃类、酮类、酯类以及少量的醛类、醇类等。各类物质中焦糖香味的麦芽醇和5-羟甲基糠醛,杏仁气味苯甲酸和糠醛以及奶香味的3-羟基-2-丁酮都可能是骏枣精油的主要香气成分。     

顶空-固相微萃取法测定地表水中四乙基铅

建立了满足地表水中四乙基铅限值要求的"顶空-固相微萃取-气质联用法".分析结果表明:采用空调及水浴控温使萃取温度恒定在23.0～24.5℃范围内,结合内标法进行定量分析,标准曲线线性关系良好(r=0.9995),检出限(3.143SD)为O.085μg/L;河流及水库水水样的加标回收率为93.2%～128%,加标样品平...     

金华火腿香精的调配

借助顶空固相微萃取法,研究并确定了煮熟后金华火腿的香气核心成分,配合调香师的调香技艺,模拟调配出适合市场需要的金华火腿咸味香精,可为市场上的金华火腿调味料提供低成本技术支持。     

Electronic nose and GC–MS analysis of volatile compounds in Tuber magnatum Pico: Evaluation of different storage conditions

The characteristic aromatic composition of white truffles (Tuber magnatum Pico) determines its culinary and commercial value. However modifications of truffle organoleptic proprieties occur during preservation. A study of headspace of white truffles by using Electronic nose (E-nose), gas chromatography–mass spectrometry (GC–MS) and sensory analyses was performed. Truffles were stored at different conditions for 7 days: +4 and +8 °C wrapped in blotting paper or covered by rice or none of the above. Headspace E-nose measurements and sensory analyses were performed each day. Statistical multivariate analysis of the data showed the capability of E-nose to predict sensorial analysis scores and to monitor aroma profile changes during storage. Truffle’s volatile molecules were also extracted by headspace solid phase microextraction technique and separated and identified by GC–MS. Partial Components Analysis of data was performed. E-nose and GC–MS results were in agreement and showed that truffle storage in paper at +8 °C seemed to be the best storage condition.     

Characterization of Volatile Compounds in Microfiltered Pasteurized Milk Using Solid-Phase Microextraction and GC×GC-TOFMS

Volatiles of milk were characterized by solid-phase microextraction coupled with comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry. Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry two-dimensional gas chromatography with time-of-flight mass spectrometry not only separated the 52 compounds that co-elute in conventional gas chromatography-mass spectrometry, but also identified 107 compounds that were first reported in milk. These volatiles included aliphatic hydrocarbons (69), aromatic hydrocarbons (42), ketones (28), esters (16), aldehydes (14), alcohols (14), acids (14), nitrogenous compounds (9), ethers (8), and sulfo compounds (3). Five dominant volatiles were hexanoic acid (193.57 ng/mL milk), methoxy-phenyl-oxime (114.83 ng/mL milk), octanoic acid (109.38 ng/mL milk), 4,5-dimethyl-1-hexene (101.48 ng/mL milk), and 2-Pentanone (99.74 ng/mL milk). This proposes an improvement methodology for determining the volatiles of dairy products.     

Differentiation of “Claret”, Rosé, Red and Blend wines based on the content of volatile compounds by headspace solid-phase microextraction and gas chromatography

We studied 46 samples of wine from Denomination of Origin of La Rioja: 8 rosé, 8 “claret”, 10 red, 10 artificial blend and 10 ungrouped wines (3 rosé, 2 “claret”, 2 red and 3 blend). The samples were analysed by gas chromatography using headspace-solid-phase microextraction (HS-SPME) with a Carbowax-divinylbenzene (CW/DVB) fibre. The variables considered were 3-methyl butyl acetate, 3-methyl-1-butanol, ethyl hexanoate, 1-hexanol, ethyl octanoate, diethyl succinate, hexanoic acid, 2-phenylethanol, octanoic acid, decanoic acid and geraniol, all significant for distinguishing the types of wines except 3 metil-1-butanol and 1-hexanol. The different wine samples were classified by multivariate statistical methods. Good differentiation between the different groups of samples was achieved by means of principal component analysis and hierarchical cluster analysis. Linear discriminant analysis was used to differentiate and classify these wines. 3-methyl-butyl acetate, ethyl octanoate, diethyl succinate, hexanoic acid, 2-phenylethanol and decanoic acid were the most discriminant variables and gave 100% recognition ability and 100% prediction ability.