Data volumes collected in many scientific fields have long exceeded the capacity of human comprehension. This is especially true in biomedical research where multiple replicates and techniques are required to conduct reliable studies. Ever-increasing data rates from new instruments compound our dependence on statistics to make sense of the numbers. The currently available data analysis tools lack user-friendliness, various capabilities or ease of access. Problem-specific software or scripts freely available in supplementary materials or research lab websites are often highly specialized, no longer functional, or simply too hard to use. Commercial software limits access and reproducibility, and is often unable to follow quickly changing, cutting-edge research demands. Finally, as machine learning techniques penetrate data analysis pipelines of the natural sciences, we see the growing demand for user-friendly and flexible tools to fuse machine learning with spectroscopy datasets. In our opinion, open-source software with strong community engagement is the way forward. To counter these problems, we develop Quasar, an open-source and user-friendly software, as a solution to these challenges. Here, we present case studies to highlight some Quasar features analyzing infrared spectroscopy data using various machine learning techniques.

Machine learning–based approach unravels distinct pathological signatures induced by patient-derived α-synuclein seeds in monkeys.

Main conclusion Chemical imaging of pollen by vibrational microspectroscopy enables characterization of pollen ultrastructure, in particular phenylpropanoid components in grain wall for comparative study of extant and extinct plant species. A detailed characterization of conifer (Pinales) pollen by vibrational microspectroscopy is presented. The main problems that arise during vibrational measurements were scatter and saturation issues in Fourier transform infrared (FTIR), and fluorescence and penetration depth issues in Raman. Single pollen grains larger than approx. 15 µm can be measured by FTIR microspectroscopy using conventional light sources, while smaller grains may be measured by employing synchrotron light sources. Pollen grains that were larger than 50 µm were too thick for FTIR imaging since the grain constituents absorbed almost all infrared light. Chemical images of pollen were obtained on sectioned samples, unveiling the distribution and concentration of proteins, carbohydrates, sporopollenins and lipids within pollen substructures. The comparative analysis of pollen species revealed that, compared with other Pinales pollens, Cedrus atlantica has a higher relative amount of lipid nutrients, as well as different chemical composition of grain wall sporopollenin. The pre-processing and data analysis, namely extended multiplicative signal correction and principal component analysis, offer simple estimate of imaging spectral data and indirect estimation of physical properties of pollen. The vibrational microspectroscopy study demonstrates that detailed chemical characterization of pollen can be obtained by measurement of an individual grain and pollen ultrastructure. Measurement of phenylpropanoid components in pollen grain wall could be used, not only for the reconstruction of past environments, but for assessment of diversity of plant species as well. Therefore, analysis of extant and extinct pollen species by vibrational spectroscopies is suggested as a valuable tool in biology, ecology and palaeosciences.