When hyperspectral technology is used to detect the fermentation quality of Daqu, the obtained hyperspectral data of high-content substances, such as water, may conceal the hyperspectral data of trace substances, which are essential for the quality evaluation of Daqu. In order to eliminate the interference of water and other high-content substances and decompose the spectral curve of trace substances, the unsupervised deep autoencoder model was established to decompose the water hyperspectral curve of Daqu. Through the experimental design, the spectral data of the mixture of Daqu powder and water were collected. Firstly, in the coding part, the spectral curve of mixed Daqu was compressed into a low dimensional representation, that is, the end member; in the decoding part, the low dimensional representation of the spectrum was decompressed and reconstructed into an original spectral curve, and the decoding weight could be updated by comparing different objective functions and transferring the reconstruction error in reverse; finally, the spectral curves of water and Daqu powder were decoded through the end member, then the Euclidean distance and Pearson correlation coefficient method were used to evaluate the effect of the solution from two aspects of characteristic distance and correlation coefficient at the same time. The experiment showed that using deep autoencoder model and choosing the L-C objective function could get the best result, the Euclidean distance and Pearson similarity of the demixed curve and the pure powder curve were 0.342 7 and 0.996 7, respectively. The results showed that the deep autoencoder network can be used to demix the hyperspectral data of Daqu, which can provide theoretical and technical support for the detection of trace substances of Daqu hyperspectral.
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