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 http://www.sciencedirect.com/science/article/pii/S0169743903002077), 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!