Curve Resolution Models API Reference
Functions
ChemometricsTools.BTEM
— Function.BTEM(X, bands = nothing; Factors = 3, particles = 50, maxiters = 1000)
Returns a single recovered spectra from a 2-Array X
, the selected bands
, number of Factors
, using a Particle Swarm Optimizer.
Note: This is not the function used in the original paper. This will be updated... it was written from memory. Also the original method uses Simulated Annealing not PSO. Band-Target Entropy Minimization (BTEM): An Advanced Method for Recovering Unknown Pure Component Spectra. Application to the FTIR Spectra of Unstable Organometallic Mixtures. Wee Chew,Effendi Widjaja, and, and Marc Garland. Organometallics 2002 21 (9), 1982-1990. DOI: 10.1021/om0108752
ChemometricsTools.BTEMobjective
— Method.BTEMobjective( a, X )
Returns the scalar BTEM objective function obtained from the linear combination vector a
and loadings X
.
Note: This is not the function used in the original paper. This will be updated... it was written from memory.
ChemometricsTools.FNNLS
— Method.FNNLS(A, b; LHS = false, maxiters = 520)
Uses an implementation of Bro et. al's Fast Non-Negative Least Squares on the matrix A
and vector b
. We can state whether to pose the problem has a left-hand side problem (LHS
= true) or a right hand side problem (default). Returns regression coefficients in the form of a vector.
Note: this function does not have guarantees. Use at your own risk for now. Fast Non-Negative Least Squares algorithm based on Bro, R., & de Jong, S. (1997) A fast non-negativity-constrained least squares algorithm. Journal of Chemometrics, 11, 393-401.
ChemometricsTools.MCRALS
— Function.MCRALS(X, C, S = nothing; norm = (false, false), Factors = 1, maxiters = 20, nonnegative = (false, false) )
Performs Multivariate Curve Resolution using Alternating Least Squares on X
taking initial estimates for S
or C
. S or C can be constrained by their norm
, or by nonnegativity using nonnegative
arguments. The number of resolved Factors
can also be set.
Tauler, R. Izquierdo-Ridorsa, A. Casassas, E. Simultaneous analysis of several spectroscopic titrations with self-modelling curve resolution.Chemometrics and Intelligent Laboratory Systems. 18, 3, (1993), 293-300.
ChemometricsTools.NMF
— Method.NMF(X; Factors = 1, tolerance = 1e-7, maxiters = 200)
Performs a variation of non-negative matrix factorization on Array X
and returns the a 2-Tuple of (Concentration Profile, Spectra)
Note: This is not a coordinate descent based NMF. This is a simple fast version which works well enough for chemical signals Algorithms for non-negative matrix factorization. Daniel D. Lee. H. Sebastian Seung. NIPS'00 Proceedings of the 13th International Conference on Neural Information Processing Systems. 535-54
ChemometricsTools.SIMPLISMA
— Method.SIMPLISMA(X; Factors = 1)
Performs SIMPLISMA on Array X
. Returns a tuple of the following form: (Concentraion Profile, Pure Spectral Estimates, Pure Variables)
Note: This is not the traditional SIMPLISMA algorithm presented by Willem Windig. REAL-TIME WAVELET COMPRESSION AND SELF-MODELING CURVE RESOLUTION FOR ION MOBILITY SPECTROMETRY. PhD. Dissertation. 2003. Guoxiang Chen.