Department of Biotechnology, Graduate School of Engineering, Osaka University, Suita, Osaka, Japan.
J Biosci Bioeng. 2012 Aug;114(2):170-5. doi: 10.1016/j.jbiosc.2012.03.023. Epub 2012 May 19.
Soy sauces, produced from different ingredients and brewing processes, have variations in components and quality. Therefore, it is extremely important to comprehend the relationship between components and the sensory attributes of soy sauces. The current study sought to perform metabolite profiling in order to devise a method of assessing the attributes of soy sauces. Quantitative descriptive analysis (QDA) data for 24 soy sauce samples were obtained from well selected sensory panelists. Metabolite profiles primarily concerning low-molecular-weight hydrophilic components were based on gas chromatography with time-of-flightmass spectrometry (GC/TOFMS). QDA data for soy sauces were accurately predicted by projection to latent structure (PLS), with metabolite profiles serving as explanatory variables and QDA data set serving as a response variable. Moreover, analysis of correlation between matrices of metabolite profiles and QDA data indicated contributing compounds that were highly correlated with QDA data. Especially, it was indicated that sugars are important components of the tastes of soy sauces. This new approach which combines metabolite profiling with QDA is applicable to analysis of sensory attributes of food as a result of the complex interaction between its components. This approach is effective to search important compounds that contribute to the attributes.
酱油因原料和酿造工艺的不同,成分和质量存在差异。因此,了解成分与感官属性之间的关系极为重要。本研究旨在通过代谢组学分析,构建酱油属性评价方法。通过精心挑选的感官评价小组,获得了 24 个酱油样本的定量描述性分析(QDA)数据。基于气相色谱飞行时间质谱(GC/TOFMS)的代谢组学分析主要涉及低分子量亲水性成分。将酱油的 QDA 数据作为因变量,代谢组学图谱作为自变量,采用偏最小二乘法(PLS)进行预测。通过代谢组学图谱和 QDA 数据矩阵的相关性分析,确定了与 QDA 数据高度相关的贡献化合物。特别是,结果表明糖是酱油味道的重要组成部分。这种将代谢组学分析与 QDA 相结合的新方法适用于分析食品的感官属性,因为其成分之间存在复杂的相互作用。这种方法可以有效地寻找对属性有贡献的重要化合物。