Abstract
This paper presents a method for source detection within unknown chemical mixtures using several spectroscopy measurement modalities. Contrary to the well studied case of single source detection, this approach enables simultaneous detection of multiple chemical components by exploiting the mixing coefficients resulting from supervised linear unmixing and thresholded non-negative least-squares. The first contribution of this work is to propose an automated procedure to compute an optimized binary classifier rule for each component independently using a database of known mixtures. The second contribution is to propose a global decision rule based on the fusion of the multimodal decisions using weighting schemes such as those used in multiple classifier systems (MCS). A real database of Ion Mobiliy Mass Spectrometry (IMMS) data is used to evaluate the detection performance. The main result is to reach an increase of the detection accuracy using the multiple thresholds within the independent classifiers approach as compared to single modality detection.
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Lefeuvre, J., Moussaoui, S., Grosset, L., Siqueira, A.L.M., Delayens, F. (2022). Supervised Mixture Analysis and Source Detection from Multimodal Measurements. In: Rozinaj, G., Vargic, R. (eds) Systems, Signals and Image Processing. IWSSIP 2021. Communications in Computer and Information Science, vol 1527. Springer, Cham. https://doi.org/10.1007/978-3-030-96878-6_19
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