Spectral data research

The adaptive and automated analysis of spectral data plays an important role in many areas of research such as physics, geophysics, chemistry, bioinformatics, biochemistry, engineering, and others. Artificial intelligenceā€based methods such as chemometrics, machine learning, and deep learning are promising tools that lead to a clearer and better understanding of data. Only with these tools, data can be used to its full extent, and the gained knowledge on processes, interactions, and characteristics of the sample is maximized. We provide software that allows chemists to analyze spectroscopy data using innovative machine learning (ML) techniques. The software is designed for use with spectroscopic equipment, and has an intuitive graphical user interface for building new ML models, supporting standard file types and data preprocessing, and incorporating well-known standard chemometric analysis techniques as well as new ML techniques for analysis of spectra.