  Chemical and mineral compositions of asteroids reflect the formation and
history of our Solar System. This knowledge is also important for planetary
defence and in-space resource utilisation. We aim to develop a fast and robust
neural-network-based method for deriving the mineral modal and chemical
compositions of silicate materials from their visible and near-infrared
spectra. The method should be able to process raw spectra without significant
pre-processing. We designed a convolutional neural network with two hidden
layers for the analysis of the spectra, and trained it using labelled
reflectance spectra. For the training, we used a dataset that consisted of
reflectance spectra of real silicate samples stored in the RELAB and C-Tape
databases, namely olivine, orthopyroxene, clinopyroxene, their mixtures, and
olivine-pyroxene-rich meteorites. We used the model on two datasets. First, we
evaluated the model reliability on a test dataset where we compared the model
classification with known compositional reference values. The individual
classification results are mostly within 10 percentage-point intervals around
the correct values. Second, we classified the reflectance spectra of S-complex
(Q-type and V-type, also including A-type) asteroids with known Bus-DeMeo
taxonomy classes. The predicted mineral chemical composition of S-type and
Q-type asteroids agree with the chemical composition of ordinary chondrites.
The modal abundances of V-type and A-type asteroids show a dominant
contribution of orthopyroxene and olivine, respectively. Additionally, our
predictions of the mineral modal composition of S-type and Q-type asteroids
show an apparent depletion of olivine related to the attenuation of its
diagnostic absorptions with space weathering. This trend is consistent with
previous results of the slower pyroxene response to space weathering relative
to olivine.
