Fourier-transform infrared (FTIR) spectroscopy can detect
the presence
of functional groups and molecules directly from a mixed solution
of organic molecules. Although it is quite useful to monitor chemical
reactions, quantitative analysis of FTIR spectra becomes difficult
when various peaks of different widths overlap. To overcome this difficulty,
we propose a chemometrics approach to accurately predict the concentration
of components in chemical reactions, yet interpretable by humans.
The proposed method first decomposes a spectrum into peaks with various
widths by the wavelet transform. Subsequently, a sparse linear regression
model is built using the wavelet coefficients. Models by the method
are interpretable using the regression coefficients shown on Gaussian
distributions with various widths. The interpretation is expected
to reveal the relation of broad regions in spectra to the model prediction.
In this study, we conducted the prediction of monomer concentration
in copolymerization reactions of five monomers against methyl methacrylate
by various chemometric approaches including conventional methods.
A rigorous validation scheme revealed that the proposed method overall
showed better predictive ability than various linear and non-linear
regression methods. The visualization results were consistent with
the interpretation obtained by another chemometric approach and qualitative
evaluation. The proposed method is found to be useful for calculating
the concentrations of monomers in copolymerization reactions and for
the interpretation of spectra.