摘要
采用CIELAB色空间体系对119 种市售红葡萄酒颜色参数进行分析,并利用超高效液相色谱-串联质谱法、pH示差法分析测定红葡萄酒样品中16 种单体花色素含量、总花色素含量,使用主成分分析、相关性分析和多元线性回归分析法对上述变量因子进行分析,研究红葡萄酒CIELAB体系中L*、a*值和b*值与单体花色素、总花色素含量、pH值之间的关系。结果表明,通过主成分分析得到对红葡萄酒颜色贡献程度较大的3 种主成分,累计贡献率达到84.11%。CIELAB色空间体系的颜色参数分别受不同单体花色素含量影响,对L*、a*值影响最大的单体花色素为矢车菊素-3-O-葡萄糖苷,对b*值影响最大的单体花色素为锦葵色素,总花色素含量对L*、a*、b*值均有极显著影响,L*值与a*值呈极显著负相关。
A total of 119 red wine samples were analyzed for color parameters by using CIELAB color space, the contents of 16 different anthocyanin monomers by ultra-high performance liquid chromatography tandem mass spectrometry, and total anthocyanins content by the pH differential method. Principal component analysis, correlation analysis and multiple linear regression analysis were conducted to study the relationship between CIELAB color parameters (L*, a* and b* values) and the contents of total anthocyanins and anthocyanin monomers and pH in red wine. Three principal component factors re flecting the color in red wine samples were selected, which cumulatively accounted for 84.11% of the total variability. The color parameters were affected by different anthocyanin monomers. Among the 16 anthocyanin monomers, cyanidin-3- glucoside content had the greatest impact on L* and a* values, while malvidin content had the greatest impact on b* value. The content of total anthocyanins had a very significant effect on all color parameters. A significant negative correlation occurred between L* and a* values.
作者
郭耀东
王飞
董少杰
王圣仪
张昂
GUO Yaodong;WANG Fei;DONG Shaojie;WANG Shengyi;ZHANG Ang(College of Health Management, Shangluo University, Shangluo 726000, China;Inspection and Quarantine Technique Centre, Qinhuangdao Entry-Exit Inspection and Quarantine Bureau, Qinhuangdao 066004, China;College of Biology Pharmacy and Food Engineering, Shangluo University, Shangluo 726000, China)
出处
《食品科学》
EI
CAS
CSCD
北大核心
2019年第18期210-215,共6页
Food Science
基金
“十三五”国家重点研发计划重点专项（2017YFC1601703）
河北省科技计划项目（16275519）
国家质检总局科研项目（2014QK134）
关键词
CIELAB色空间
花色素
相关性分析
主成分分析
多元线性回归分析
CIELAB color space
anthocyanin
correlation analysis
principal component analysis
multiple linear regression analysis
