Unmixing mass spectra using machine learning for species identification

MALDI-TOF mass spectrometry is a fast and powerful tool for identifying biological species based on molecular fingerprints. However, when samples contain mixtures, such as multiple species or strains, the resulting spectra overlap, making it hard to tell them apart. To solve this, we can apply machine learning techniques that “unmix” these complex signals into their original components. This concept, known as spectral deconvolution, is similar to separating instruments in a song or resolving mixed cell types in genomics. With access to a high-quality reference spectral database from the EU-funded MALDIBANK project, we can explore statistical and ML models that enhance identification accuracy, potentially transforming how we analyze complex biological samples. This project will develop and test spectral deconvolution models, such as ICA, NMF, and neural networks, to separate overlapping MALDI-TOF signals. This will evaluate how different input formats (raw spectra vs. spectral peaks) and modeling approaches (including Bayesian techniques) affect performance. The goal is to build a prototype pipeline that improves species identification from mixed samples.