Characterization of selected Chinese soybean paste based on flavor profiles using HS-SPME-GC/MS, E-nose and E-tongue...

Headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) with electronic nose (E-nose) and electronic tongue (E-tongue) was applied for flavor characterization of traditional Chinese fermented soybean paste. Considering geographical distribution and market representation, twelve kinds of samples were selected to investigate the feasibility. A total of 57 volatile organic compounds (VOCs) were identified, of which 8 volatiles were found in all samples. Linear discrimination analysis (LDA) of fusion data exhibited a high discriminant accuracy of 97.22%. Compared with partial least squares regression (PLSR), support vector machine regression (SVR) analysis exhibited a more satisfying performance on predicting the content of esters, total acids, reducing sugar, salinity and amino acid nitrogen, of which correlation coefficients for prediction (R_p) were about 0.803, 0.949, 0.960, 0.896, 0.923 respectively. This study suggests that intelligent sensing technologies combined with chemometrics can be a promising tool for flavor characterization of fermented soybean paste or other food matrixes.