{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["9(1)"],"submitter":["Braidy N"],"funding":["Natural Sciences and Engineering Research Council of Canada","Canada Research Chairs"],"pubmed_abstract":["Analytical electron microscopy plays a key role in the development of novel nanomaterials. Electron energy-loss spectroscopy (EELS) and energy-dispersive X-ray spectroscopy (EDX) datasets are typically processed to isolate the background-subtracted elemental signal. Multivariate tools have emerged as powerful methods to blindly map the components, which addresses some of the shortcomings of the traditional methods. Here, we demonstrate the superior performance of a new multivariate optimization method using a challenging EELS and EDX dataset. The dataset was recorded from a spectrum image P-type metal-oxide-semiconductor stack with 7 components exhibiting heavy spectral overlap and a low signal-to-noise ratio. Compared to peak integration, independent component analysis, Baysian Linear Unm"],"journal":["Scientific reports"],"pagination":["18797"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6906416"],"repository":["biostudies-literature"],"pubmed_title":["Unmixing noisy co-registered spectrum images of multicomponent nanostructures."],"pmcid":["PMC6906416"],"pubmed_authors":["Braidy N","Gosselin R"],"additional_accession":[]},"is_claimable":false,"name":"Unmixing noisy co-registered spectrum images of multicomponent nanostructures.","description":"Analytical electron microscopy plays a key role in the development of novel nanomaterials. Electron energy-loss spectroscopy (EELS) and energy-dispersive X-ray spectroscopy (EDX) datasets are typically processed to isolate the background-subtracted elemental signal. Multivariate tools have emerged as powerful methods to blindly map the components, which addresses some of the shortcomings of the traditional methods. Here, we demonstrate the superior performance of a new multivariate optimization method using a challenging EELS and EDX dataset. The dataset was recorded from a spectrum image P-type metal-oxide-semiconductor stack with 7 components exhibiting heavy spectral overlap and a low signal-to-noise ratio. Compared to peak integration, independent component analysis, Baysian Linear Unm","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 Dec","modification":"2025-04-18T15:34:35.506Z","creation":"2020-05-21T22:28:41Z"},"accession":"S-EPMC6906416","cross_references":{"pubmed":["31827162"],"doi":["10.1038/s41598-019-55219-2"]}}