Software for Spectral Data Processing by Chemometrics and Machine Learning Methods

The article describes a software package that supports the basic methods of chemometrics, and machine learning used for spectral data processing. The package can be used both as part of the software for analytical spectral instruments or independently. The package contains both common methods Qlinear and quadratic discriminant analysis, principal component regression, and partial least squaresS, as well as lesser known but proven effective in processing spectra, including the random forest method and extreme gradient boosting. Data on the testing of the program are provided, incl. an example of using the developed software package to solve problems of classifying
black carbon particles according to the initial combustion objects.