gamma_flow: Denoise, classify and disentangle spectral data!
Session Abstract
gamma_flow is an open-source Python package for real-time spectral data analysis. Designed for speed and efficiency, it avoids large models, opting instead for a novel supervised dimensionality reduction approach. This enables seamless denoising, classification, and disentangling of single-label or multi-label spectra.
Session Description
In many research fields, spectral measurements help to assess material properties. In this context, an area of interest for many researchers is the classification (automated labelling) of the measured spectra. Additionally, there may be a need to decompound multi-label spectra (linear combinations of different substances) and identify their constituents.
As proprietary spectral analysis software are often limited in their functionality and adaptability, a Python package was developed and will be presented in this talk.
gamma_flow (Guided Analysis of Multi-label spectra by Matrix Factorization for Lightweight Operational Workflows) includes the
– classification of test spectra to predict their constituents
– denoising of test spectra for better recognizability
– outlier detection to evaluate the model’s applicability to test spectra
It is based on a dimensionality reduction model that constitutes a novel, supervised approach to non-negative matrix factorization (NMF). Hence, it exploits and adapts conventional data science methods rather than using extensive, energy-intensive models like neural networks. This results in a fast, robust and reliable automated analysis, leading to classification accuracies above 90%.
