Quasar: Easy Machine Learning for Biospectroscopy.

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.

Probing Heterogeneity in Attenuated Total Reflection Surface-Enhanced Infrared Absorption Spectroscopy (ATR-SEIRAS) Response with Synchrotron Infrared Microspectroscopy.

The heterogeneity of metal island films electrodeposited on conductive metal oxide modified internal reflection elements is shown to provide a variable attenuated total reflection surface-enhanced infrared absorption spectroscopy (ATR-SEIRAS) response. A self-assembled monolayer of a ferrocene-terminated thiol monolayer (FcC11SH) was formed on the gold islands covering a single substrate, which was measured using both a conventional spectrometer and a custom-built horizontal microscope. Cyclic voltammetry and ATR-SEIRAS results reveal that the FcC11SH-modified substrate undergoes a reversible electron transfer and an associated re-orientation of both the ferrocene/ferrocenium headgroup and the hydrocarbon backbone. The magnitude of the absorption signal arising from the redox changes in the monolayer, as well as the IR signature arising from the ingress/egress of the perchlorate counterions, is shown to depend significantly on the size of the infrared beam spot when using a conventional Fourier transform infrared spectrometer. By performing equivalent measurements on a horizontal microscope, the primary cause of the differences in the signal level is found to be the heterogeneity in the density of gold islands on the conductive metal oxide.