No abstract

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.

The performance of low-cost, microgroove silicon (Si)-based internal reflection elements (μ-groove IREs) for infrared chemical imaging of microfluidic devices is described.

Paintings and painted objects are quite susceptible to degradation, as paint layers are usually composed of complex mixtures of materials that can participate in chemical degradation processes. The identification of the constituent materials in paint (including binders, pigments, and fillers) and the degradation products within paint layers is of particular importance to ensuring the conservation of paintings, by providing important information both about their material history as well as their state of conservation. Metal fatty acid salts (metal soaps) are degradation products that can form in situ from interactions between inorganic pigments and free fatty acids in oil-based binding media, and can cause significant condition issues in paintings. Fourier transform infrared (FTIR) spectroscopy is one of the leading analytical techniques for the study of metal soaps. In this article, the materials analysis of several cross-sections from paintings and painted objects from works in Canadian collections is presented. Recent results on the use of external reflection FTIR (R-FTIR) spectroscopy to identify and map the distribution of paint components and metal soap degradation products is presented. In particular, zinc, lead, calcium, and copper fatty acid salts were all readily identified in paint cross-sections by R-FTIR spectroscopy, along with several pigments and the oil binding medium. The results shown here are among the first detailed examinations of these metal soaps in paint cross-sections using R-FTIR spectroscopy. The use of highly polished samples in which specular reflection is dominant allowed for spectral transformations to be applied to generate transmission/absorption-like spectra which facilitated identification of these species. The distribution of these species across the cross-sections was mapped by integrating characteristic absorption features in the R-FTIR spectra. Attenuated total internal reflection (ATR) FTIR spectroscopy was also performed on several samples, which provided additional compositional details at the interface of paint layers and degradation products.