  We apply a recently developed technique utilizing machine learning for
statistical analysis of computational nitrogen K-edge spectra of aqueous
triglycine. This method, the emulator-based component analysis, identifies
spectrally relevant structural degrees of freedom from a data set filtering
irrelevant ones out. Thus tremendous reduction in the dimensionality of the
ill-posed nonlinear inverse problem of spectrum interpretation is achieved.
Structural and spectral variation across the sampled phase space is notable.
Using these data, we train a neural network to predict the intensities of
spectral regions of interest from the structure. These regions are defined by
the temperature-difference profile of the simulated spectra, and the analysis
yields a structural interpretation for their behavior. Even though the utilized
local many-body tensor representation implicitly encodes the secondary
structure of the peptide, our approach proves that this information is
irrecoverable from the spectra. A hard X-ray Raman scattering experiment
confirms the overall sensibility of the simulated spectra, but the predicted
temperature-dependent effects therein remain beyond the achieved statistical
confidence level.
