Dear all,
I am using Julia and the JuliaStats ecosystem together with SciKit-learn (thanks PyCall) to perform component analysis and blind-source separation in various Raman and also XAFS spectra of glasses with varying chemical compositions.
Reading papers, I learned that people call this field “Chemometrics”, and several commercial packages are available to do so (e.g., http://www.eigenvector.com/software/pls_toolbox.htm)… In particular, several authors refer to a technic called “multivariate curve resolution” (MCR), which aims at extracting endmembers components and associated concentrations from spectra of mixed chemical components.
I usually perform component extraction using the NMF package in JuliaStats, with using the ALS method in particular… From the descriptions in the papers (in particular, see Principles and methodologies in self-modeling curve resolution - ScienceDirect), I understood that MCR uses an ALS algorithm with a non-negative constrain… So its seems to me that MCR-ALS and NMF-ALS are similar…
I therefore wanted to know if people in the JuliaStats community have some know knowledge about MCR. Is it just a NMF performed using an ALS algorithm, or is it something different? Do somebody have any additional knowledge about Chemometrics? Is it just a name given to the application of usual stat/ML technics (PCA, PLS, NMF, ICA…) to spectroscopic/chemical data or is there additional things making Chemometrics specific?
Thanks in advance!
Best,
Charles.