Multivariate relationships among sensory, physicochemical parameters, and targeted volatile compounds in commercial red sufus (Chinese fermented soybean curd): Comparison of QDA® and Flash Profile methods
Publication in refereed journal


摘要Selected physico- (texture and color) chemical (salt, protein, and moisture content) properties and 20 targeted volatile compounds (TVCs) from 12 commercial red sufus were investigated to identify correlations with sensory attributes obtained by either QDA or Flash Profile (FP). The intensities of 15 attributes from QDA and the ranking values of 19 attributes from FP of red sufus were evaluated, and a higher variance was generally found in the results from FP than those from QDA among the 12 samples. Principal component analysis (PCA) of TVCs showed that both the concentration and the ratio of volatile compounds influenced the flavor quality and the discrimination among the samples. Multiple factor analysis (MFA) was used to associate the sensory data from either FP or QDA with the physicochemical properties and TVCs of red sufus. The results showed that both FP and QDA data associated well with the physicochemical and TVC data, and the intensity of the sensory attributes could be predicted from the properties of red sufus. Pearson correlation coefficients between the sensory attributes (aroma/flavor) and TVCs pinpointed that a sulfur-like aroma was a key attribute in red sufus, and it might be a result of the comprehensive combination of different TVCs. The information reported here could be important for the quality control of traditional and new variants of red sufu products by providing an approach to substitute the sensory measurements with the instrumental measurements, and to strengthen the interpretation of sensory data by showing how they are affected by the physicochemical properties.
著者Wenmeng HE, Hau Yin CHUNG
期刊名稱Food Research International
關鍵詞Red sufu, Quantitative descriptive analysis, Flash profile, SPME-GC–MS, Multiple factor analysis, Chemometrics, Principal component analysis, Pearson correlation

上次更新時間 2020-11-09 於 23:04