Qualitative and quantitative assessment of flavor quality of Chinese soybean paste using multiple sensor technologies combined with chemometrics and a data fusion strategy

Multiple sensor technologies including electronic nose (E-nose), electronic tongue (E-tongue), colorimeter and texture analyzer combined with chemometrics and dada fusion strategies were applied to characterize the flavor quality of traditional Chinese fermented soybean paste. Principal components analysis (PCA) was performed to divide the selected soybean pastes into three clusters which was not completely consistent with geographical regions of selected samples. Support vector machine regression (SVR) outperformed partial least squares regression (PLSR) in quantitatively predicting sensory attributes. Additionally, prediction of overall flavor of soybean paste based on data fusion of multiple sensor information, with a correlation coefficient of prediction (Rp) of 0.9636 based on SVR, was better than prediction of E-nose and E-tongue data fusion (Rp = 0.9267). This study suggested multiple sensor technologies coupled with chemometrics can be a promising tool for flavor assessment and characterization of fermented soybean paste or other food matrixes.