Basics of spectra processing and analysis

Overview & Synopsis

This course at the MECAREACT Thematic School offers a concise overview of key methods for analyzing spectroscopic data, from basic preprocessing to advanced multivariate techniques. Beginning with the fundamentals—what a spectrum represents and the difference between univariate and multivariate analysis—it introduces practical strategies for enhancing data quality, including smoothing, baseline correction, and background subtraction. Univariate approaches such as second derivatives, Fourier Self-Deconvolution, and curve fitting are presented for resolving and modeling spectral features. These are complemented by multivariate exploratory tools like 2D Correlation Spectroscopy, PCA, SVD, and SIMPLISMA, which help uncover hidden patterns in complex datasets. A particular emphasis is placed on Multivariate Curve Resolution using Alternating Least Squares (MCR-ALS), including soft constraints and kinetic modeling. Related techniques like Global and Target Analyses and 2D IRIS are also introduced. The course concludes with an introduction to SpectroChemPy, a Python-based package for spectroscopic data analysis, enabling participants to apply the discussed techniques in practical, reproducible workflows.