摘要
本研究通过顶空固相微萃取-气相色谱-质谱联用(HS-SPME-GC/MS)对福建红曲醋(永春老醋和永春香醋)中的挥发性风味物质进行分析,以峰面积为指标,确定固相微萃取最优条件为:50/30μm DVB/CAR/PDMS萃取头,6mL稀释10倍的醋样中添加1.5g氯化钠,萃取温度50℃,萃取时间40min。在该条件下采用HS-SPME-GC/MS共检测出福建红曲醋挥发性风味物质达63种,主要包括酯类(20种)、醇类(12种)、酸类(13种)、醛类(7种)、酮类(5种)、酚类(2种)、及其他类物质(4种)。热图分析发现,乙酸乙酯、苯乙醇、苯甲醛、乙酸异丁酯、雪松醇、乙偶姻和乙酸是永春老醋和永春香醋二者共有的主要香气成分,主成分分析发现,永春老醋密切相关的挥发性风味物质为乙酸异丁酯、2-乙酰氧基-3-丁酮和庚酸,而辛酸、2-十二醇、丁酸、苯乙烯、己酸乙酯和1-壬醇为永春香醋的密切相关的挥发性风味物质。
A method based on headspace solid-phase microextraction(HS-SPME)coupled to chromatography-mass spectrometry(GC-MS)was developed to quantify the volatile flavor compounds in Fujian Monascus vinegar(Yongchun aged vinegar and Yongchun aromatic vinegar).Optimal extraction conditions showed that incubation of 6 mL vinegar(diluted 10-fold)with 1.5 g of sodium chloride,an extraction time of 40 min,an extraction temperature of 50℃using a DVB/CAR/PDMS fiber will give the best extraction.Under these conditions,totally 63 volatile flavor compounds were identified in Fujian Monascus vinegar,including esters(20),alcohols(12),acids(13),aldehydes(7),ketones(5),phenols(2)and others(4).Ethyl acetate,phenylethyl alcohol,benzaldehyde,isobutyl acetate,cedrol,acetoin and acetic acid were the main volatile compounds in Fujian Monascus Vinegar.Isobutyl acetate,2-acetoxy-3-butanone and heptanoic acid were strongly associated with Yongchun aged vinegar,whereas octanoic acid,2-dodecanol,butanoic acid,styrene,ethyl hexanoate and 1-nonanol showed statistically significant positive correlations with Yongchun aromatic vinegar.
作者
蒋雅君
张翀
吕旭聪
郑宝东
田玉庭
JIANG Ya-jun;ZHANG Chong;LYU Xu-cong;ZHENG Bao-dong;TIAN Yu-ting(College of Food Science,Fujian Agriculture and Forestry University,Fuzhou 350002,China)
出处
《现代食品科技》
EI
CAS
北大核心
2019年第3期154-160,共7页
Modern Food Science and Technology
基金
国家自然科学基金资助项目(31601466)
中国博士后基金特别资助项目(2015T80671
2016T90591)
福建省高校产学合作科技重大项目(2018N5003)
关键词
福建红曲醋
顶空固相微萃取
气相色谱-质谱联用
挥发性风味成分
主成分分析
Fujian Monascus vinegar
headspace solid-phase micro-extraction(HS-SPME)
gas chromatography-mass spectrometry(GC-MS)
volatile flavor components
principal component analysis