Rapid detection of the reducing sugar and amino acid nitrogen contents of Daqu based on hyperspectral imaging

In this research, the hyperspectral imaging technique was employed to realize the rapid and accurate detection of the reducing sugar and amino acid nitrogen contents of Daqu during fermentation. Different preprocessed data were employed to establish cascade forest (CF) models to determine the best preprocessing method. Principal component analysis (PCA) and the successive projection algorithm (SPA) were then combined to extract the characteristic wavelengths. Four types of models (CF, BPNN, SVR, and PLSR) were established based on the full and characteristic wavelengths, respectively. Among all the models, the CF model established by the characteristic wavelengths was determined to be superior for the detection of the reducing sugar and amino acid nitrogen contents: for reducing sugar, RP2 = 0.9862, RMESP = 0.0812 g/100 g, and RPD = 6.0402; for amino acid nitrogen, RP2 = 0.9876, RMSEP = 0.0500 g/100 g, and RPD = 6.3698. The best model achieved the visualization of the reducing sugar and amino acid nitrogen contents in the region of interest. The satisfactory detection results demonstrate that the hyperspectral imaging technique can be used to realize the rapid and accurate detection of the reducing sugar and amino acid nitrogen contents of Daqu during fermentation.