“…Therefore, the application of signal deconvolution to measure solid-state NMR data is an important challenge to extract hidden information in the NMR spectra of macromolecular samples with multiple phases and components. Several methods for spectral separation [ 32 ], apodization, zero filling, linear prediction, fitting and numerical simulation [ 33 ], such as covariance analysis [ 34 ], SIMPSON [ 35 ], SPINEVOLUTION [ 36 ], dmfit [ 37 ], EASY-GOING deconvolution [ 38 ], INFOS [ 39 ], Fityk [ 40 ], ssNake [ 41 ], the noise reduction method based on principal component analysis [ 42 ], and the signal deconvolution method that combines short-time Fourier transform (STFT, a time–frequency analytical method), and probabilistic sparse matrix factorization (PSMF which is one of the non-negative matrix factorizations) [ 43 ] were developed as computational approaches to measured data.…”