Fringe and Noise Reductions of pMAIRS Spectra Using Principal Component Analysis

Chemometrics and Related Fields in Python

The Python programing language is becoming a promising tool for data analysis in various fields. However, little attention has been paid to using Python in the field of analytical chemistry, though recent advances in instrumental analysis require robust and reliable data analysis. In order to overcome the difficulty in accurate analysis, multivariate analysis, or chemometrics, has been widely applied to various kinds of data obtained by instrumental analysis. In the present work, the potential usefulness of Python for chemometrics and related fields in chemistry is reviewed. Many practical tools for chemometrics, e.g., principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), etc., are included in the scikit-learn machine learning (ML) library for Python. Other useful libraries such as pyMCR for multivariate curve resolution (MCR), 2Dpy for two-dimensional correlation spectroscopy (2D-COS), etc. can be obtained from GitHub. For these reasons, a computational environment for chemometrics is easily constructed in Python.

Second Generation of Multiple-Angle Incidence Resolution Spectrometry

Infrared surface spectroscopic techniques commonly have long-term issues that (1) the multiple reflections of light in the substrate yield optical interference fringes in the absorption spectrum and (2) the double modulation of light at the interferometer in a Fourier transform infrared spectrometer makes the water-vapor subtraction impossible. These measurement troubles often disturb the quantitative analysis of chemical bands of the analyte thin film. Multiple-angle incidence resolution spectrometry (MAIRS) is not an exception in this matter, either. In the present study, the long-term common issues have first been resolved by fixing the angle of incidence at a large angle, whereas the polarization angle is changed. With this simple conceptual change of MAIRS, as a result, we are ready for concentrating on spectral analysis only without concerning about the measurement troubles.